Pub Date : 2024-09-05DOI: 10.1088/2632-2153/ad743f
Jinyang Sun, Xi Chen, Xiumei Wang, Dandan Zhu, Xingping Zhou
The concept of photonic modes is the cornerstone in optics and photonics, which can describe the propagation of the light. The Maxwell’s equations play the role in calculating the mode field based on the structure information, while this process needs a great deal of computations, especially in the handle with a three-dimensional model. To overcome this obstacle, we introduce the multi-modal diffusion model to predict the photonic modes in one certain structure. The Contrastive Language–Image Pre-training (CLIP) model is used to build the connections between photonic structures and the corresponding modes. Then we exemplify Stable Diffusion (SD) model to realize the function of optical fields generation from structure information. Our work introduces multi-modal deep learning to construct complex mapping between structural information and optical field as high-dimensional vectors, and generates optical field images based on this mapping.
{"title":"Photonic modes prediction via multi-modal diffusion model","authors":"Jinyang Sun, Xi Chen, Xiumei Wang, Dandan Zhu, Xingping Zhou","doi":"10.1088/2632-2153/ad743f","DOIUrl":"https://doi.org/10.1088/2632-2153/ad743f","url":null,"abstract":"The concept of photonic modes is the cornerstone in optics and photonics, which can describe the propagation of the light. The Maxwell’s equations play the role in calculating the mode field based on the structure information, while this process needs a great deal of computations, especially in the handle with a three-dimensional model. To overcome this obstacle, we introduce the multi-modal diffusion model to predict the photonic modes in one certain structure. The Contrastive Language–Image Pre-training (CLIP) model is used to build the connections between photonic structures and the corresponding modes. Then we exemplify Stable Diffusion (SD) model to realize the function of optical fields generation from structure information. Our work introduces multi-modal deep learning to construct complex mapping between structural information and optical field as high-dimensional vectors, and generates optical field images based on this mapping.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1088/2632-2153/ad7457
Aiden R Rosebush, Alexander C B Greenwood, Brian T Kirby, Li Qian
We propose a support vector machine (SVM) based approach for generating an entanglement witness that requires exponentially less training data than previously proposed methods. SVMs generate hyperplanes represented by a weighted sum of expectation values of local observables whose coefficients are optimized to sum to a positive number for all separable states and a negative number for as many entangled states as possible near a specific target state. Previous SVM-based approaches for entanglement witness generation used large amounts of randomly generated separable states to perform training, a task with considerable computational overhead. Here, we propose a method for orienting the witness hyperplane using only the significantly smaller set of states consisting of the eigenstates of the generalized Pauli matrices and a set of entangled states near the target entangled states. With the orientation of the witness hyperplane set by the SVM, we tune the plane’s placement using a differential program that ensures perfect classification accuracy on a limited test set as well as maximal noise tolerance. For N qubits, the SVM portion of this approach requires only O(6N) training states, whereas an existing method needs O(24N). We use this method to construct witnesses of 4 and 5 qubit GHZ states with coefficients agreeing with stabilizer formalism witnesses to within 3.7 percent and 1 percent, respectively. We also use the same training states to generate novel 4 and 5 qubit W state witnesses. Finally, we computationally verify these witnesses on small test sets and propose methods for further verification.
{"title":"An exponential reduction in training data sizes for machine learning derived entanglement witnesses","authors":"Aiden R Rosebush, Alexander C B Greenwood, Brian T Kirby, Li Qian","doi":"10.1088/2632-2153/ad7457","DOIUrl":"https://doi.org/10.1088/2632-2153/ad7457","url":null,"abstract":"We propose a support vector machine (SVM) based approach for generating an entanglement witness that requires exponentially less training data than previously proposed methods. SVMs generate hyperplanes represented by a weighted sum of expectation values of local observables whose coefficients are optimized to sum to a positive number for all separable states and a negative number for as many entangled states as possible near a specific target state. Previous SVM-based approaches for entanglement witness generation used large amounts of randomly generated separable states to perform training, a task with considerable computational overhead. Here, we propose a method for orienting the witness hyperplane using only the significantly smaller set of states consisting of the eigenstates of the generalized Pauli matrices and a set of entangled states near the target entangled states. With the orientation of the witness hyperplane set by the SVM, we tune the plane’s placement using a differential program that ensures perfect classification accuracy on a limited test set as well as maximal noise tolerance. For <italic toggle=\"yes\">N</italic> qubits, the SVM portion of this approach requires only <inline-formula>\u0000<tex-math><?CDATA $O(6^N)$?></tex-math><mml:math overflow=\"scroll\"><mml:mrow><mml:mi>O</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msup><mml:mn>6</mml:mn><mml:mi>N</mml:mi></mml:msup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"mlstad7457ieqn1.gif\"></inline-graphic></inline-formula> training states, whereas an existing method needs <inline-formula>\u0000<tex-math><?CDATA $O(2^{4^N})$?></tex-math><mml:math overflow=\"scroll\"><mml:mrow><mml:mi>O</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msup><mml:mn>2</mml:mn><mml:mrow><mml:msup><mml:mn>4</mml:mn><mml:mi>N</mml:mi></mml:msup></mml:mrow></mml:msup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"mlstad7457ieqn2.gif\"></inline-graphic></inline-formula>. We use this method to construct witnesses of 4 and 5 qubit GHZ states with coefficients agreeing with stabilizer formalism witnesses to within 3.7 percent and 1 percent, respectively. We also use the same training states to generate novel 4 and 5 qubit W state witnesses. Finally, we computationally verify these witnesses on small test sets and propose methods for further verification.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1088/2632-2153/ad7192
Will Newman, Jamshid Ghaboussi, Michael Insana
The challenge posed by the inverse problem associated with ultrasonic elasticity imaging is well matched to the capabilities of data-driven solutions. This report describes how data properties and the time sequence by which the data are introduced during training influence deformation-model accuracy and training times. Our goal is to image the elastic modulus of soft linear-elastic media as accurately as possible within a limited volume. To monitor progress during training, we introduce metrics describing convergence rate and stress entropy to guide data acquisition and other timing features. For example, a regularization term in the loss function may be introduced and later removed to speed and stabilize developing deformation models as well as establishing stopping rules for neural-network convergence. Images of a 14.4 cm3 volume within 3D software phantom visually indicate the quality of modulus images resulting over a range of training variables. The results show that a data-driven method constrained by the physics of a deformed solid will lead to quantitively accurate 3D elastic modulus images with minimum artifacts.
{"title":"Regulating the development of accurate data-driven physics-informed deformation models","authors":"Will Newman, Jamshid Ghaboussi, Michael Insana","doi":"10.1088/2632-2153/ad7192","DOIUrl":"https://doi.org/10.1088/2632-2153/ad7192","url":null,"abstract":"The challenge posed by the inverse problem associated with ultrasonic elasticity imaging is well matched to the capabilities of data-driven solutions. This report describes how data properties and the time sequence by which the data are introduced during training influence deformation-model accuracy and training times. Our goal is to image the elastic modulus of soft linear-elastic media as accurately as possible within a limited volume. To monitor progress during training, we introduce metrics describing convergence rate and stress entropy to guide data acquisition and other timing features. For example, a regularization term in the loss function may be introduced and later removed to speed and stabilize developing deformation models as well as establishing stopping rules for neural-network convergence. Images of a 14.4 cm<sup>3</sup> volume within 3D software phantom visually indicate the quality of modulus images resulting over a range of training variables. The results show that a data-driven method constrained by the physics of a deformed solid will lead to quantitively accurate 3D elastic modulus images with minimum artifacts.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As an emerging computing architecture, the computing-in-memory (CIM) exhibits significant potential for energy efficiency and computing power in artificial intelligence applications. However, the intrinsic non-idealities of CIM devices, manifesting as random interference on the weights of neural network, may significantly impact the inference accuracy. In this paper, we propose a novel training algorithm designed to mitigate the impact of weight noise. The algorithm strategically minimizes cross-entropy loss while concurrently refining the feature representations in intermediate layers to emulate those of an ideal, noise-free network. This dual-objective approach not only preserves the accuracy of the neural network but also enhances its robustness against noise-induced degradation. Empirical validation across several benchmark datasets confirms that our algorithm sets a new benchmark for accuracy in CIM-enabled neural network applications. Compared to the most commonly used forward noise training methods, our approach yields approximately a 2% accuracy boost on the ResNet32 model with the CIFAR-10 dataset and a weight noise scale of 0.2, and achieves a minimum performance gain of 1% on ResNet18 with the ImageNet dataset under the same noise quantization conditions.
{"title":"Improving model robustness to weight noise via consistency regularization","authors":"Yaoqi Hou, Qingtian Zhang, Namin Wang, Huaqiang Wu","doi":"10.1088/2632-2153/ad734a","DOIUrl":"https://doi.org/10.1088/2632-2153/ad734a","url":null,"abstract":"As an emerging computing architecture, the computing-in-memory (CIM) exhibits significant potential for energy efficiency and computing power in artificial intelligence applications. However, the intrinsic non-idealities of CIM devices, manifesting as random interference on the weights of neural network, may significantly impact the inference accuracy. In this paper, we propose a novel training algorithm designed to mitigate the impact of weight noise. The algorithm strategically minimizes cross-entropy loss while concurrently refining the feature representations in intermediate layers to emulate those of an ideal, noise-free network. This dual-objective approach not only preserves the accuracy of the neural network but also enhances its robustness against noise-induced degradation. Empirical validation across several benchmark datasets confirms that our algorithm sets a new benchmark for accuracy in CIM-enabled neural network applications. Compared to the most commonly used forward noise training methods, our approach yields approximately a 2% accuracy boost on the ResNet32 model with the CIFAR-10 dataset and a weight noise scale of 0.2, and achieves a minimum performance gain of 1% on ResNet18 with the ImageNet dataset under the same noise quantization conditions.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-03DOI: 10.1088/2632-2153/ad611e
G Aad, E Aakvaag, B Abbott, K Abeling, N J Abicht, S H Abidi, A Aboulhorma, H Abramowicz, H Abreu, Y Abulaiti, B S Acharya, C Adam Bourdarios, L Adamczyk, S V Addepalli, M J Addison, J Adelman, A Adiguzel, T Adye, A A Affolder, Y Afik, M N Agaras, J Agarwala, A Aggarwal, C Agheorghiesei, A Ahmad, F Ahmadov, W S Ahmed, S Ahuja, X Ai, G Aielli, A Aikot, M Ait Tamlihat, B Aitbenchikh, I Aizenberg, M Akbiyik, T P A Åkesson, A V Akimov, D Akiyama, N N Akolkar, S Aktas, K Al Khoury, G L Alberghi, J Albert, P Albicocco, G L Albouy, S Alderweireldt, Z L Alegria, M Aleksa, I N Aleksandrov, C Alexa, T Alexopoulos, F Alfonsi, M Algren, M Alhroob, B Ali, H M J Ali, S Ali, S W Alibocus, M Aliev, G Alimonti, W Alkakhi, C Allaire, B M M Allbrooke, J F Allen, C A Allendes Flores, P P Allport, A Aloisio, F Alonso, C Alpigiani, M Alvarez Estevez, A Alvarez Fernandez, M Alves Cardoso, M G Alviggi, M Aly, Y Amaral Coutinho, A Ambler, C Amelung, M Amerl, C G Ames, D Amidei, S P Amor Dos Santos, K R Amos, V Ananiev, C Anastopoulos, T Andeen, J K Anders, S Y Andrean, A Andreazza, S Angelidakis, A Angerami, A V Anisenkov, A Annovi, C Antel, M T Anthony, E Antipov, M Antonelli, F Anulli, M Aoki, T Aoki, J A Aparisi Pozo, M A Aparo, L Aperio Bella, C Appelt, A Apyan, S J Arbiol Val, C Arcangeletti, A T H Arce, E Arena, J-F Arguin, S Argyropoulos, J-H Arling, O Arnaez, H Arnold, G Artoni, H Asada, K Asai, S Asai, N A Asbah, K Assamagan, R Astalos, S Atashi, R J Atkin, M Atkinson, H Atmani, P A Atmasiddha, K Augsten, S Auricchio, A D Auriol, V A Austrup, G Avolio, K Axiotis, G Azuelos, D Babal, H Bachacou, K Bachas, A Bachiu, F Backman, A Badea, T M Baer, P Bagnaia, M Bahmani, D Bahner, K Bai, A J Bailey, V R Bailey, J T Baines, L Baines, O K Baker, E Bakos, D Bakshi Gupta, V Balakrishnan, R Balasubramanian, E M Baldin, P Balek, E Ballabene, F Balli, L M Baltes, W K Balunas, J Balz, E Banas, M Bandieramonte, A Bandyopadhyay, S Bansal, L Barak, M Barakat, E L Barberio, D Barberis, M Barbero, M Z Barel, K N Barends, T Barillari, M-S Barisits, T Barklow, P Baron, D A Baron Moreno, A Baroncelli, G Barone, A J Barr, J D Barr, F Barreiro, J Barreiro Guimarães da Costa, U Barron, M G Barros Teixeira, S Barsov, F Bartels, R Bartoldus, A E Barton, P Bartos, A Basan, M Baselga, A Bassalat, M J Basso, C R Basson, R L Bates, S Batlamous, B Batool, M Battaglia, D Battulga, M Bauce, M Bauer, P Bauer, L T Bazzano Hurrell, J B Beacham, T Beau, J Y Beaucamp, P H Beauchemin, P Bechtle, H P Beck, K Becker, A J Beddall, V A Bednyakov, C P Bee, L J Beemster, T A Beermann, M Begalli, M Begel, A Behera, J K Behr, J F Beirer, F Beisiegel, M Belfkir, G Bella, L Bellagamba, A Bellerive, P Bellos, K Beloborodov, D Benchekroun, F Bendebba, Y Benhammou, K C Benkendorfer, L Beresford, M Beretta, E Bergeaas Kuutmann, N Berger, B Bergmann, J Beringer, G Bernardi, C Bernius, F U Bernlochner, F Bernon, A Berrocal Guardia, T Berry, P Berta, A Berthold, S Bethke, A Betti, A J Bevan, N K Bhalla, M Bhamjee, S Bhatta, D S Bhattacharya, P Bhattarai, K D Bhide, V S Bhopatkar, R M Bianchi, G Bianco, O Biebel, R Bielski, M Biglietti, C S Billingsley, M Bindi, A Bingul, C Bini, A Biondini, C J Birch-sykes, G A Bird, M Birman, M Biros, S Biryukov, T Bisanz, E Bisceglie, J P Biswal, D Biswas, K Bjørke, I Bloch, A Blue, U Blumenschein, J Blumenthal, V S Bobrovnikov, M Boehler, B Boehm, D Bogavac, A G Bogdanchikov, C Bohm, V Boisvert, P Bokan, T Bold, M Bomben, M Bona, M Boonekamp, C D Booth, A G Borbély, I S Bordulev, H M Borecka-Bielska, G Borissov, D Bortoletto, D Boscherini, M Bosman, J D Bossio Sola, K Bouaouda, N Bouchhar, J Boudreau, E V Bouhova-Thacker, D Boumediene, R Bouquet, A Boveia, J Boyd, D Boye, I R Boyko, J Bracinik, N Brahimi, G Brandt, O Brandt, F Braren, B Brau, J E Brau, R Brener, L Brenner, R Brenner, S Bressler, D Britton, D Britzger, I Brock, G Brooijmans, E Brost, L M Brown, L E Bruce, T L Bruckler, P A Bruckman de Renstrom, B Brüers, A Bruni, G Bruni, M Bruschi, N Bruscino, T Buanes, Q Buat, D Buchin, A G Buckley, O Bulekov, B A Bullard, S Burdin, C D Burgard, A M Burger, B Burghgrave, O Burlayenko, J T P Burr, C D Burton, J C Burzynski, E L Busch, V Büscher, P J Bussey, J M Butler, C M Buttar, J M Butterworth, W Buttinger, C J Buxo Vazquez, A R Buzykaev, S Cabrera Urbán, L Cadamuro, D Caforio, H Cai, Y Cai, Y Cai, V M M Cairo, O Cakir, N Calace, P Calafiura, G Calderini, P Calfayan, G Callea, L P Caloba, D Calvet, S Calvet, M Calvetti, R Camacho Toro, S Camarda, D Camarero Munoz, P Camarri, M T Camerlingo, D Cameron, C Camincher, M Campanelli, A Camplani, V Canale, A C Canbay, J Cantero, Y Cao, F Capocasa, M Capua, A Carbone, R Cardarelli, J C J Cardenas, F Cardillo, G Carducci, T Carli, G Carlino, J I Carlotto, B T Carlson, E M Carlson, L Carminati, A Carnelli, M Carnesale, S Caron, E Carquin, S Carrá, G Carratta, A M Carroll, T M Carter, M P Casado, M Caspar, F L Castillo, L Castillo Garcia, V Castillo Gimenez, N F Castro, A Catinaccio, J R Catmore, T Cavaliere, V Cavaliere, N Cavalli, Y C Cekmecelioglu, E Celebi, F Celli, M S Centonze, V Cepaitis, K Cerny, A S Cerqueira, A Cerri, L Cerrito, F Cerutti, B Cervato, A Cervelli, G Cesarini, S A Cetin, D Chakraborty, J Chan, W Y Chan, J D Chapman, E Chapon, B Chargeishvili, D G Charlton, M Chatterjee, C Chauhan, Y Che, S Chekanov, S V Chekulaev, G A Chelkov, A Chen, B Chen, B Chen, H Chen, H Chen, J Chen, J Chen, M Chen, S Chen, S J Chen, X Chen, X Chen, Y Chen, C L Cheng, H C Cheng, S Cheong, A Cheplakov, E Cheremushkina, E Cherepanova, R Cherkaoui El Moursli, E Cheu, K Cheung, L Chevalier, V Chiarella, G Chiarelli, N Chiedde, G Chiodini, A S Chisholm, A Chitan, M Chitishvili, M V Chizhov, K Choi, Y Chou, E Y S Chow, K L Chu, M C Chu, X Chu, J Chudoba, J J Chwastowski, D Cieri, K M Ciesla, V Cindro, A Ciocio, F Cirotto, Z H Citron, M Citterio, D A Ciubotaru, A Clark, P J Clark, C Clarry, J M Clavijo Columbie, S E Clawson, C Clement, J Clercx, Y Coadou, M Cobal, A Coccaro, R F Coelho Barrue, R Coelho Lopes De Sa, S Coelli, B Cole, J Collot, P Conde Muiño, M P Connell, S H Connell, E I Conroy, F Conventi, H G Cooke, A M Cooper-Sarkar, A Cordeiro Oudot Choi, L D Corpe, M Corradi, F Corriveau, A Cortes-Gonzalez, M J Costa, F Costanza, D Costanzo, B M Cote, G Cowan, K Cranmer, D Cremonini, S Crépé-Renaudin, F Crescioli, M Cristinziani, M Cristoforetti, V Croft, J E Crosby, G Crosetti, A Cueto, T Cuhadar Donszelmann, H Cui, Z Cui, W R Cunningham, F Curcio, P Czodrowski, M M Czurylo, M J Da Cunha Sargedas De Sousa, J V Da Fonseca Pinto, C Da Via, W Dabrowski, T Dado, S Dahbi, T Dai, D Dal Santo, C Dallapiccola, M Dam, G D’amen, V D’Amico, J Damp, J R Dandoy, M Danninger, V Dao, G Darbo, S Darmora, S J Das, S D’Auria, A D’avanzo, C David, T Davidek, B Davis-Purcell, I Dawson, H A Day-hall, K De, R De Asmundis, N De Biase, S De Castro, N De Groot, P de Jong, H De la Torre, A De Maria, A De Salvo, U De Sanctis, F De Santis, A De Santo, J B De Vivie De Regie, D V Dedovich, J Degens, A M Deiana, F Del Corso, J Del Peso, F Del Rio, L Delagrange, F Deliot, C M Delitzsch, M Della Pietra, D Della Volpe, A Dell’Acqua, L Dell’Asta, M Delmastro, P A Delsart, S Demers, M Demichev, S P Denisov, L D’Eramo, D Derendarz, F Derue, P Dervan, K Desch, C Deutsch, F A Di Bello, A Di Ciaccio, L Di Ciaccio, A Di Domenico, C Di Donato, A Di Girolamo, G Di Gregorio, A Di Luca, B Di Micco, R Di Nardo, M Diamantopoulou, F A Dias, T Dias Do Vale, M A Diaz, F G Diaz Capriles, M Didenko, E B Diehl, S Díez Cornell, C Diez Pardos, C Dimitriadi, A Dimitrievska, J Dingfelder, I-M Dinu, S J Dittmeier, F Dittus, F Djama, T Djobava, C Doglioni, A Dohnalova, J Dolejsi, Z Dolezal, K M Dona, M Donadelli, B Dong, J Donini, A D’Onofrio, M D’Onofrio, J Dopke, A Doria, N Dos Santos Fernandes, P Dougan, M T Dova, A T Doyle, M A Draguet, E Dreyer, I Drivas-koulouris, M Drnevich, M Drozdova, D Du, T A du Pree, F Dubinin, M Dubovsky, E Duchovni, G Duckeck, O A Ducu, D Duda, A Dudarev, E R Duden, M D’uffizi, L Duflot, M Dührssen, A E Dumitriu, M Dunford, S Dungs, K Dunne, A Duperrin, H Duran Yildiz, M Düren, A Durglishvili, B L Dwyer, G I Dyckes, M Dyndal, B S Dziedzic, Z O Earnshaw, G H Eberwein, B Eckerova, S Eggebrecht, E Egidio Purcino De Souza, L F Ehrke, G Eigen, K Einsweiler, T Ekelof, P A Ekman, S El Farkh, Y El Ghazali, H El Jarrari, A El Moussaouy, V Ellajosyula, M Ellert, F Ellinghaus, N Ellis, J Elmsheuser, M Elsing, D Emeliyanov, Y Enari, I Ene, S Epari, P A Erland, M Errenst, M Escalier, C Escobar, E Etzion, G Evans, H Evans, L S Evans, A Ezhilov, S Ezzarqtouni, F Fabbri, L Fabbri, G Facini, V Fadeyev, R M Fakhrutdinov, D Fakoudis, S Falciano, L F Falda Ulhoa Coelho, P J Falke, J Faltova, C Fan, Y Fan, Y Fang, M Fanti, M Faraj, Z Farazpay, A Farbin, A Farilla, T Farooque, S M Farrington, F Fassi, D Fassouliotis, M Faucci Giannelli, W J Fawcett, L Fayard, P Federic, P Federicova, O L Fedin, M Feickert, L Feligioni, D E Fellers, C Feng, M Feng, Z Feng, M J Fenton, L Ferencz, R A M Ferguson, S I Fernandez Luengo, P Fernandez Martinez, M J V Fernoux, J Ferrando, A Ferrari, P Ferrari, R Ferrari, D Ferrere, C Ferretti, F Fiedler, P Fiedler, A Filipčič, E K Filmer, F Filthaut, M C N Fiolhais, L Fiorini, W C Fisher, T Fitschen, P M Fitzhugh, I Fleck, P Fleischmann, T Flick, M Flores, L R Flores Castillo, L Flores Sanz De Acedo, F M Follega, N Fomin, J H Foo, A Formica, A C Forti, E Fortin, A W Fortman, M G Foti, L Fountas, D Fournier, H Fox, P Francavilla, S Francescato, S Franchellucci, M Franchini, S Franchino, D Francis, L Franco, V Franco Lima, L Franconi, M Franklin, G Frattari, W S Freund, Y Y Frid, J Friend, N Fritzsche, A Froch, D Froidevaux, J A Frost, Y Fu, S Fuenzalida Garrido, M Fujimoto, K Y Fung, E Furtado De Simas Filho, M Furukawa, J Fuster, A Gabrielli, A Gabrielli, P Gadow, G Gagliardi, L G Gagnon, S Galantzan, E J Gallas, B J Gallop, K K Gan, S Ganguly, Y Gao, F M Garay Walls, B Garcia, C García, A Garcia Alonso, A G Garcia Caffaro, J E García Navarro, M Garcia-Sciveres, G L Gardner, R W Gardner, N Garelli, D Garg, R B Garg, J M Gargan, C A Garner, C M Garvey, P Gaspar, V K Gassmann, G Gaudio, V Gautam, P Gauzzi, I L Gavrilenko, A Gavrilyuk, C Gay, G Gaycken, E N Gazis, A A Geanta, C M Gee, A Gekow, C Gemme, M H Genest, A D Gentry, S George, W F George, T Geralis, P Gessinger-Befurt, M E Geyik, M Ghani, M Ghneimat, K Ghorbanian, A Ghosal, A Ghosh, A Ghosh, B Giacobbe, S Giagu, T Giani, P Giannetti, A Giannini, S M Gibson, M Gignac, D T Gil, A K Gilbert, B J Gilbert, D Gillberg, G Gilles, L Ginabat, D M Gingrich, M P Giordani, P F Giraud, G Giugliarelli, D Giugni, F Giuli, I Gkialas, L K Gladilin, C Glasman, G R Gledhill, G Glemža, M Glisic, I Gnesi, Y Go, M Goblirsch-Kolb, B Gocke, D Godin, B Gokturk, S Goldfarb, T Golling, M G D Gololo, D Golubkov, J P Gombas, A Gomes, G Gomes Da Silva, A J Gomez Delegido, R Gonçalo, L Gonella, A Gongadze, F Gonnella, J L Gonski, R Y González Andana, S González de la Hoz, R 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Yan, H J Yang, H T Yang, S Yang, T Yang, X Yang, X Yang, Y Yang, Y Yang, Z Yang, W-M Yao, H Ye, H Ye, J Ye, S Ye, X Ye, Y Yeh, I Yeletskikh, B K Yeo, M R Yexley, P Yin, K Yorita, S Younas, C J S Young, C Young, C Yu, Y Yu, M Yuan, R Yuan, L Yue, M Zaazoua, B Zabinski, E Zaid, Z K Zak, T Zakareishvili, N Zakharchuk, S Zambito, J A Zamora Saa, J Zang, D Zanzi, O Zaplatilek, C Zeitnitz, H Zeng, J C Zeng, D T Zenger Jr, O Zenin, T Ženiš, S Zenz, S Zerradi, D Zerwas, M Zhai, D F Zhang, J Zhang, J Zhang, K Zhang, L Zhang, P Zhang, R Zhang, S Zhang, S Zhang, T Zhang, X Zhang, X Zhang, Y Zhang, Y Zhang, Y Zhang, Z Zhang, Z Zhang, H Zhao, T Zhao, Y Zhao, Z Zhao, A Zhemchugov, J Zheng, K Zheng, X Zheng, Z Zheng, D Zhong, B Zhou, H Zhou, N Zhou, Y Zhou, Y Zhou, C G Zhu, J Zhu, Y Zhu, Y Zhu, X Zhuang, K Zhukov, N I Zimine, J Zinsser, M Ziolkowski, L Živković, A Zoccoli, K Zoch, T G Zorbas, O Zormpa, W Zou, L Zwalinski, The ATLAS Collaborationgabriela.navarro@uan.edu.co
The energy and mass measurements of jets are crucial tasks for the Large Hadron Collider experiments. This paper presents a new calibration method to simultaneously calibrate these quantities for large-radius jets measured with the ATLAS detector using a deep neural network (DNN). To address the specificities of the calibration problem, special loss functions and training procedures are employed, and a complex network architecture, which includes feature annotation and residual connection layers, is used. The DNN-based calibration is compared to the standard numerical approach in an extensive series of tests. The DNN approach is found to perform significantly better in almost all of the tests and over most of the relevant kinematic phase space. In particular, it consistently improves the energy and mass resolutions, with a 30% better energy resolution obtained for transverse momenta pT>500 GeV.
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Alves Cardoso, M G Alviggi, M Aly, Y Amaral Coutinho, A Ambler, C Amelung, M Amerl, C G Ames, D Amidei, S P Amor Dos Santos, K R Amos, V Ananiev, C Anastopoulos, T Andeen, J K Anders, S Y Andrean, A Andreazza, S Angelidakis, A Angerami, A V Anisenkov, A Annovi, C Antel, M T Anthony, E Antipov, M Antonelli, F Anulli, M Aoki, T Aoki, J A Aparisi Pozo, M A Aparo, L Aperio Bella, C Appelt, A Apyan, S J Arbiol Val, C Arcangeletti, A T H Arce, E Arena, J-F Arguin, S Argyropoulos, J-H Arling, O Arnaez, H Arnold, G Artoni, H Asada, K Asai, S Asai, N A Asbah, K Assamagan, R Astalos, S Atashi, R J Atkin, M Atkinson, H Atmani, P A Atmasiddha, K Augsten, S Auricchio, A D Auriol, V A Austrup, G Avolio, K Axiotis, G Azuelos, D Babal, H Bachacou, K Bachas, A Bachiu, F Backman, A Badea, T M Baer, P Bagnaia, M Bahmani, D Bahner, K Bai, A J Bailey, V R Bailey, J T Baines, L Baines, O K Baker, E Bakos, D Bakshi Gupta, V Balakrishnan, R Balasubramanian, E M Baldin, P Balek, E Ballabene, F Balli, L M 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Santos, A Santra, K A Saoucha, J G Saraiva, J Sardain, O Sasaki, K Sato, C Sauer, F Sauerburger, E Sauvan, P Savard, R Sawada, C Sawyer, L Sawyer, I Sayago Galvan, C Sbarra, A Sbrizzi, T Scanlon, J Schaarschmidt, U Schäfer, A C Schaffer, D Schaile, R D Schamberger, C Scharf, M M Schefer, V A Schegelsky, D Scheirich, F Schenck, M Schernau, C Scheulen, C Schiavi, M Schioppa, B Schlag, K E Schleicher, S Schlenker, J Schmeing, M A Schmidt, K Schmieden, C Schmitt, N Schmitt, S Schmitt, L Schoeffel, A Schoening, P G Scholer, E Schopf, M Schott, J Schovancova, S Schramm, T Schroer, H-C Schultz-Coulon, M Schumacher, B A Schumm, Ph Schune, A J Schuy, H R Schwartz, A Schwartzman, T A Schwarz, Ph Schwemling, R Schwienhorst, A Sciandra, G Sciolla, F Scuri, C D Sebastiani, K Sedlaczek, P Seema, S C Seidel, A Seiden, B D Seidlitz, C Seitz, J M Seixas, G Sekhniaidze, L Selem, N Semprini-Cesari, D Sengupta, V Senthilkumar, L Serin, L Serkin, M Sessa, H Severini, F Sforza, A Sfyrla, Q Sha, E Shabalina, R Shaheen, J D Shahinian, D Shaked Renous, L Y Shan, M Shapiro, A Sharma, A S Sharma, P Sharma, P B Shatalov, K Shaw, S M Shaw, A Shcherbakova, Q Shen, D J Sheppard, P Sherwood, L Shi, X Shi, C O Shimmin, J D Shinner, I P J Shipsey, S Shirabe, M Shiyakova, J Shlomi, M J Shochet, J Shojaii, D R Shope, B Shrestha, S Shrestha, E M Shrif, M J Shroff, P Sicho, A M Sickles, E Sideras Haddad, A Sidoti, F Siegert, Dj Sijacki, F Sili, J M Silva, M V Silva Oliveira, S B Silverstein, S Simion, R Simoniello, E L Simpson, H Simpson, L R Simpson, N D Simpson, S Simsek, S Sindhu, P Sinervo, S Singh, S Sinha, S Sinha, M Sioli, I Siral, E Sitnikova, J Sjölin, A Skaf, E Skorda, P Skubic, M Slawinska, V Smakhtin, B H Smart, S Yu Smirnov, Y Smirnov, L N Smirnova, O Smirnova, A C Smith, E A Smith, H A Smith, J L Smith, R Smith, M Smizanska, K Smolek, A A Snesarev, S R Snider, H L Snoek, S Snyder, R Sobie, A Soffer, C A Solans Sanchez, E Yu Soldatov, U Soldevila, A A Solodkov, S Solomon, A Soloshenko, K Solovieva, O V Solovyanov, V Solovyev, P Sommer, A Sonay, W Y Song, A Sopczak, A L Sopio, F Sopkova, J D Sorenson, I R Sotarriva Alvarez, V Sothilingam, O J Soto Sandoval, S Sottocornola, R Soualah, Z Soumaimi, D South, N Soybelman, S Spagnolo, M Spalla, D Sperlich, G Spigo, S Spinali, D P Spiteri, M Spousta, E J Staats, R Stamen, A Stampekis, M Standke, E Stanecka, M V Stange, B Stanislaus, M M Stanitzki, B Stapf, E A Starchenko, G H Stark, J Stark, P Staroba, P Starovoitov, S Stärz, R Staszewski, G Stavropoulos, J Steentoft, P Steinberg, B Stelzer, H J Stelzer, O Stelzer-Chilton, H Stenzel, T J Stevenson, G A Stewart, J R Stewart, M C Stockton, G Stoicea, M Stolarski, S Stonjek, A Straessner, J Strandberg, S Strandberg, M Stratmann, M Strauss, T Strebler, P Strizenec, R Ströhmer, D M Strom, R Stroynowski, A Strubig, S A Stucci, B Stugu, J Stupak, N A Styles, D Su, S Su, W Su, X Su, D Suchy, K Sugizaki, V V Sulin, M J Sullivan, D M S Sultan, L Sultanaliyeva, S Sultansoy, T Sumida, S Sun, S Sun, O Sunneborn Gudnadottir, N Sur, M R Sutton, H Suzuki, M Svatos, M Swiatlowski, T Swirski, I Sykora, M Sykora, T Sykora, D Ta, K Tackmann, A Taffard, R Tafirout, J S Tafoya Vargas, Y Takubo, M Talby, A A Talyshev, K C Tam, N M Tamir, A Tanaka, J Tanaka, R Tanaka, M Tanasini, Z Tao, S Tapia Araya, S Tapprogge, A Tarek Abouelfadl Mohamed, S Tarem, K Tariq, G Tarna, G F Tartarelli, P Tas, M Tasevsky, E Tassi, A C Tate, G Tateno, Y Tayalati, G N Taylor, W Taylor, A S Tee, R Teixeira De Lima, P Teixeira-Dias, J J Teoh, K Terashi, J Terron, S Terzo, M Testa, R J Teuscher, A Thaler, O Theiner, N Themistokleous, T Theveneaux-Pelzer, O Thielmann, D W Thomas, J P Thomas, E A Thompson, P D Thompson, E Thomson, Y Tian, V Tikhomirov, Yu A Tikhonov, S Timoshenko, D Timoshyn, E X L Ting, P Tipton, S H Tlou, A Tnourji, K Todome, S Todorova-Nova, S Todt, M Togawa, J Tojo, S Tokár, K Tokushuku, O Toldaiev, R Tombs, M Tomoto, L Tompkins, K W Topolnicki, E Torrence, H Torres, E Torró Pastor, M Toscani, C Tosciri, M Tost, D R Tovey, A Traeet, I S Trandafir, T Trefzger, A Tricoli, I M Trigger, S Trincaz-Duvoid, D A Trischuk, B Trocmé, L Truong, M Trzebinski, A Trzupek, F Tsai, M Tsai, A Tsiamis, P V Tsiareshka, S Tsigaridas, A Tsirigotis, V Tsiskaridze, E G Tskhadadze, M Tsopoulou, Y Tsujikawa, I I Tsukerman, V Tsulaia, S Tsuno, K Tsuri, D Tsybychev, Y Tu, A Tudorache, V Tudorache, A N Tuna, S Turchikhin, I Turk Cakir, R Turra, T Turtuvshin, P M Tuts, S Tzamarias, P Tzanis, E Tzovara, F Ukegawa, P A Ulloa Poblete, E N Umaka, G Unal, M Unal, A Undrus, G Unel, J Urban, P Urquijo, P Urrejola, G Usai, R Ushioda, M Usman, Z Uysal, V Vacek, B Vachon, K O H Vadla, T Vafeiadis, A Vaitkus, C Valderanis, E Valdes Santurio, M Valente, S Valentinetti, A Valero, E Valiente Moreno, A Vallier, J A Valls Ferrer, D R Van Arneman, T R Van Daalen, A Van Der Graaf, P Van Gemmeren, M Van Rijnbach, S Van Stroud, I Van Vulpen, M Vanadia, W Vandelli, E R Vandewall, D Vannicola, L Vannoli, R Vari, E W Varnes, C Varni, T Varol, D Varouchas, L Varriale, K E Varvell, M E Vasile, L Vaslin, G A Vasquez, A Vasyukov, R Vavricka, F Vazeille, T Vazquez Schroeder, J Veatch, V Vecchio, M J Veen, I Veliscek, L M Veloce, F Veloso, S Veneziano, A Ventura, S Ventura Gonzalez, A Verbytskyi, M Verducci, C Vergis, M Verissimo De Araujo, W Verkerke, J C Vermeulen, C Vernieri, M Vessella, M C Vetterli, A Vgenopoulos, N Viaux Maira, T Vickey, O E Vickey Boeriu, G H A Viehhauser, L Vigani, M Villa, M Villaplana Perez, E M Villhauer, E Vilucchi, M G Vincter, G S Virdee, A Vishwakarma, A Visibile, C Vittori, I Vivarelli, E Voevodina, F Vogel, J C Voigt, P Vokac, Yu Volkotrub, J Von Ahnen, E Von Toerne, B Vormwald, V Vorobel, K Vorobev, M Vos, K Voss, M Vozak, L Vozdecky, N Vranjes, M Vranjes Milosavljevic, M Vreeswijk, N K Vu, R Vuillermet, O Vujinovic, I Vukotic, S Wada, C Wagner, J M Wagner, W Wagner, S Wahdan, H Wahlberg, M Wakida, J Walder, R Walker, W Walkowiak, A Wall, E J Wallin, T Wamorkar, A Z Wang, C Wang, C Wang, H Wang, J Wang, R -J Wang, R Wang, R Wang, S M Wang, S Wang, T Wang, W T Wang, W Wang, X Wang, X Wang, X Wang, Y Wang, Y Wang, Z Wang, Z Wang, Z Wang, A Warburton, R J Ward, N Warrack, S Waterhouse, A T Watson, H Watson, M F Watson, E Watton, G Watts, B M Waugh, C Weber, H A Weber, M S Weber, S M Weber, C Wei, Y Wei, A R Weidberg, E J Weik, J Weingarten, M Weirich, C Weiser, C J Wells, T Wenaus, B Wendland, T Wengler, N S Wenke, N Wermes, M Wessels, A M Wharton, A S White, A White, M J White, D Whiteson, L Wickremasinghe, W Wiedenmann, M Wielers, C Wiglesworth, D J Wilbern, H G Wilkens, D M Williams, H H Williams, S Williams, S Willocq, B J Wilson, P J Windischhofer, F I Winkel, F Winklmeier, B T Winter, J K Winter, M Wittgen, M Wobisch, Z Wolffs, J Wollrath, M W Wolter, H Wolters, E L Woodward, S D Worm, B K Wosiek, K W Woźniak, S Wozniewski, K Wraight, C Wu, M Wu, M Wu, S L Wu, X Wu, Y Wu, Z Wu, J Wuerzinger, T R Wyatt, B M Wynne, S Xella, L Xia, M Xia, J Xiang, M Xie, X Xie, S Xin, A Xiong, J Xiong, D Xu, H Xu, L Xu, R Xu, T Xu, Y Xu, Z Xu, Z Xu, B Yabsley, S Yacoob, Y Yamaguchi, E Yamashita, H Yamauchi, T Yamazaki, Y Yamazaki, J Yan, S Yan, Z Yan, H J Yang, H T Yang, S Yang, T Yang, X Yang, X Yang, Y Yang, Y Yang, Z Yang, W-M Yao, H Ye, H Ye, J Ye, S Ye, X Ye, Y Yeh, I Yeletskikh, B K Yeo, M R Yexley, P Yin, K Yorita, S Younas, C J S Young, C Young, C Yu, Y Yu, M Yuan, R Yuan, L Yue, M Zaazoua, B Zabinski, E Zaid, Z K Zak, T Zakareishvili, N Zakharchuk, S Zambito, J A Zamora Saa, J Zang, D Zanzi, O Zaplatilek, C Zeitnitz, H Zeng, J C Zeng, D T Zenger Jr, O Zenin, T Ženiš, S Zenz, S Zerradi, D Zerwas, M Zhai, D F Zhang, J Zhang, J Zhang, K Zhang, L Zhang, P Zhang, R Zhang, S Zhang, S Zhang, T Zhang, X Zhang, X Zhang, Y Zhang, Y Zhang, Y Zhang, Z Zhang, Z Zhang, H Zhao, T Zhao, Y Zhao, Z Zhao, A Zhemchugov, J Zheng, K Zheng, X Zheng, Z Zheng, D Zhong, B Zhou, H Zhou, N Zhou, Y Zhou, Y Zhou, C G Zhu, J Zhu, Y Zhu, Y Zhu, X Zhuang, K Zhukov, N I Zimine, J Zinsser, M Ziolkowski, L Živković, A Zoccoli, K Zoch, T G Zorbas, O Zormpa, W Zou, L Zwalinski, The ATLAS Collaborationgabriela.navarro@uan.edu.co","doi":"10.1088/2632-2153/ad611e","DOIUrl":"https://doi.org/10.1088/2632-2153/ad611e","url":null,"abstract":"The energy and mass measurements of jets are crucial tasks for the Large Hadron Collider experiments. This paper presents a new calibration method to simultaneously calibrate these quantities for large-radius jets measured with the ATLAS detector using a deep neural network (DNN). To address the specificities of the calibration problem, special loss functions and training procedures are employed, and a complex network architecture, which includes feature annotation and residual connection layers, is used. The DNN-based calibration is compared to the standard numerical approach in an extensive series of tests. The DNN approach is found to perform significantly better in almost all of the tests and over most of the relevant kinematic phase space. In particular, it consistently improves the energy and mass resolutions, with a 30% better energy resolution obtained for transverse momenta <inline-formula>\u0000<tex-math><?CDATA $p_{text{T}}gt 500$?></tex-math><mml:math overflow=\"scroll\"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mrow><mml:mtext>T</mml:mtext></mml:mrow></mml:msub><mml:mo>></mml:mo><mml:mn>500</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"mlstad611eieqn1.gif\"></inline-graphic></inline-formula> GeV.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1088/2632-2153/ad7227
Yucheol Cho, Guenseok Choi, Gyeongdo Ham, Mincheol Shin, Daeshik Kim
Over the past decades, density functional theory (DFT) calculations have been utilized in various fields such as materials science and semiconductor devices. However, due to the inherent nature of DFT calculations, which rigorously consider interactions between atoms, they require significant computational cost. To address this, extensive research has recently focused on training neural networks to replace DFT calculations. However, previous methods for training neural networks necessitated an extensive number of DFT simulations to acquire the ground truth (Hamiltonians). Conversely, when dealing with a limited amount of training data, deep learning models often display increased errors in predicting Hamiltonians and band structures for testing data. This phenomenon poses the potential risk of generating inaccurate physical interpretations, including the emergence of unphysical branches within band structures. To tackle this challenge, we propose a novel deep learning-based method for calculating DFT Hamiltonians, specifically tailored to produce accurate results with limited training data. Our framework not only employs supervised learning with the calculated Hamiltonian but also generates pseudo Hamiltonians (targets for unlabeled data) and trains the neural networks on unlabeled data. Particularly, our approach, which leverages unlabeled data, is noteworthy as it marks the first attempt in the field of neural network Hamiltonians. Our framework showcases the superior performance of our framework compared to the state-of-the-art approach across various datasets, such as MoS2, Bi2Te3, HfO2, and InGaAs. Moreover, our framework demonstrates enhanced generalization performance by effectively utilizing unlabeled data, achieving noteworthy results when evaluated on data more complex than the training set, such as configurations with more atoms and temperature ranges outside the training data.
{"title":"SemiH: DFT Hamiltonian neural network training with semi-supervised learning","authors":"Yucheol Cho, Guenseok Choi, Gyeongdo Ham, Mincheol Shin, Daeshik Kim","doi":"10.1088/2632-2153/ad7227","DOIUrl":"https://doi.org/10.1088/2632-2153/ad7227","url":null,"abstract":"Over the past decades, density functional theory (DFT) calculations have been utilized in various fields such as materials science and semiconductor devices. However, due to the inherent nature of DFT calculations, which rigorously consider interactions between atoms, they require significant computational cost. To address this, extensive research has recently focused on training neural networks to replace DFT calculations. However, previous methods for training neural networks necessitated an extensive number of DFT simulations to acquire the ground truth (Hamiltonians). Conversely, when dealing with a limited amount of training data, deep learning models often display increased errors in predicting Hamiltonians and band structures for testing data. This phenomenon poses the potential risk of generating inaccurate physical interpretations, including the emergence of unphysical branches within band structures. To tackle this challenge, we propose a novel deep learning-based method for calculating DFT Hamiltonians, specifically tailored to produce accurate results with limited training data. Our framework not only employs supervised learning with the calculated Hamiltonian but also generates pseudo Hamiltonians (targets for unlabeled data) and trains the neural networks on unlabeled data. Particularly, our approach, which leverages unlabeled data, is noteworthy as it marks the first attempt in the field of neural network Hamiltonians. Our framework showcases the superior performance of our framework compared to the state-of-the-art approach across various datasets, such as MoS<sub>2</sub>, Bi<sub>2</sub>Te<sub>3</sub>, HfO<sub>2</sub>, and InGaAs. Moreover, our framework demonstrates enhanced generalization performance by effectively utilizing unlabeled data, achieving noteworthy results when evaluated on data more complex than the training set, such as configurations with more atoms and temperature ranges outside the training data.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1088/2632-2153/ad718e
Jaekak Yoo, Jae Won Choi, Eunha Kim, Eun-Jung Park, Ahruem Baek, Jaeseok Kim, Mun Seok Jeong, Youngwoo Cho, Tae Geol Lee, Min Beom Heo
This study investigated the applicability of the area of spheroids and hypoxic regions for efficient evaluation of drug efficacy using machine learning (ML). We initially developed a high-throughput detection method to obtain the area of spheroids and hypoxic regions that can handle over 10 000 images per hour with an error rate of 2%–3%. The ML models were trained using cell growth of six cell lines (i.e. HepG2, A549, Hep3B, BEAS-2B, HT-29, and HCT116) and hypoxic region variations of two cell lines (i.e. HepG2 and BEAS-2B); our model can predict the area of spheroids and hypoxic region of certain growth date with high precision. To demonstrate the applicability, HepG2 spheroids were treated with sorafenib, and the efficacy of the drug was evaluated through a comparison of differences in areas of cell size and hypoxic regions with the predicted results. Furthermore, our ML approach has been shown to be applicable to provide the model-driven evaluative criterion for toxicity and drug efficacy using spheroids.
本研究利用机器学习（ML）研究了球形和缺氧区域面积在高效评估药物疗效方面的适用性。我们最初开发了一种高通量检测方法来获取球形区和缺氧区的面积，该方法每小时可处理 10,000 多张图像，误差率为 2%-3%。我们使用六种细胞系（即 HepG2、A549、Hep3B、BEAS-2B、HT-29 和 HCT116）的细胞生长和两种细胞系（即 HepG2 和 BEAS-2B）的缺氧区域变化训练了 ML 模型；我们的模型可以高精度地预测特定生长日期的球形面积和缺氧区域。为了证明其适用性，我们用索拉非尼处理了 HepG2 球形细胞，并通过比较细胞大小和缺氧区域面积与预测结果的差异来评估药物的疗效。此外，我们的 ML 方法已被证明适用于为使用球形细胞的毒性和药物疗效提供模型驱动的评估标准。
{"title":"Evaluating cell growth and hypoxic regions of 3D spheroids via a machine learning approach","authors":"Jaekak Yoo, Jae Won Choi, Eunha Kim, Eun-Jung Park, Ahruem Baek, Jaeseok Kim, Mun Seok Jeong, Youngwoo Cho, Tae Geol Lee, Min Beom Heo","doi":"10.1088/2632-2153/ad718e","DOIUrl":"https://doi.org/10.1088/2632-2153/ad718e","url":null,"abstract":"This study investigated the applicability of the area of spheroids and hypoxic regions for efficient evaluation of drug efficacy using machine learning (ML). We initially developed a high-throughput detection method to obtain the area of spheroids and hypoxic regions that can handle over 10 000 images per hour with an error rate of 2%–3%. The ML models were trained using cell growth of six cell lines (<italic toggle=\"yes\">i.e.</italic> HepG2, A549, Hep3B, BEAS-2B, HT-29, and HCT116) and hypoxic region variations of two cell lines (<italic toggle=\"yes\">i.e.</italic> HepG2 and BEAS-2B); our model can predict the area of spheroids and hypoxic region of certain growth date with high precision. To demonstrate the applicability, HepG2 spheroids were treated with sorafenib, and the efficacy of the drug was evaluated through a comparison of differences in areas of cell size and hypoxic regions with the predicted results. Furthermore, our ML approach has been shown to be applicable to provide the model-driven evaluative criterion for toxicity and drug efficacy using spheroids.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1088/2632-2153/ad7226
Alexandre de Camargo, Ricky T Q Chen, Rodrigo A Vargas-Hernández
Orbital-free density functional theory (OF-DFT) for real-space systems has historically depended on Lagrange optimization techniques, primarily due to the inability of previously proposed electron density approaches to ensure the normalization constraint. This study illustrates how leveraging contemporary generative models, notably normalizing flows (NFs), can surmount this challenge. We develop a Lagrangian-free optimization framework by employing these machine learning models for the electron density. This diverse approach also integrates cutting-edge variational inference techniques and equivariant deep learning models, offering an innovative reformulation to the OF-DFT problem. We demonstrate the versatility of our framework by simulating a one-dimensional diatomic system, LiH, and comprehensive simulations of hydrogen, lithium hydride, water, and four hydrocarbon molecules. The inherent flexibility of NFs facilitates initialization with promolecular densities, markedly enhancing the efficiency of the optimization process.
{"title":"Leveraging normalizing flows for orbital-free density functional theory","authors":"Alexandre de Camargo, Ricky T Q Chen, Rodrigo A Vargas-Hernández","doi":"10.1088/2632-2153/ad7226","DOIUrl":"https://doi.org/10.1088/2632-2153/ad7226","url":null,"abstract":"Orbital-free density functional theory (OF-DFT) for real-space systems has historically depended on Lagrange optimization techniques, primarily due to the inability of previously proposed electron density approaches to ensure the normalization constraint. This study illustrates how leveraging contemporary generative models, notably normalizing flows (NFs), can surmount this challenge. We develop a Lagrangian-free optimization framework by employing these machine learning models for the electron density. This diverse approach also integrates cutting-edge variational inference techniques and equivariant deep learning models, offering an innovative reformulation to the OF-DFT problem. We demonstrate the versatility of our framework by simulating a one-dimensional diatomic system, LiH, and comprehensive simulations of hydrogen, lithium hydride, water, and four hydrocarbon molecules. The inherent flexibility of NFs facilitates initialization with promolecular densities, markedly enhancing the efficiency of the optimization process.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1088/2632-2153/ad7191
Toni Zimmermann, Florian Sammüller, Sophie Hermann, Matthias Schmidt, Daniel de las Heras
We combine power functional theory and machine learning to study non-equilibrium overdamped many-body systems of colloidal particles at the level of one-body fields. We first sample in steady state the one-body fields relevant for the dynamics from computer simulations of Brownian particles under the influence of randomly generated external fields. A neural network is then trained with this data to represent locally in space the formally exact functional mapping from the one-body density and velocity profiles to the one-body internal force field. The trained network is used to analyse the non-equilibrium superadiabatic force field and the transport coefficients such as shear and bulk viscosities. Due to the local learning approach, the network can be applied to systems much larger than the original simulation box in which the one-body fields are sampled. Complemented with the exact non-equilibrium one-body force balance equation and a continuity equation, the network yields viable predictions of the dynamics in time-dependent situations. Even though training is based on steady states only, the predicted dynamics is in good agreement with simulation results. A neural dynamical density functional theory can be straightforwardly implemented as a limiting case in which the internal force field is that of an equilibrium system. The framework is general and directly applicable to other many-body systems of interacting particles following Brownian dynamics.
{"title":"Neural force functional for non-equilibrium many-body colloidal systems","authors":"Toni Zimmermann, Florian Sammüller, Sophie Hermann, Matthias Schmidt, Daniel de las Heras","doi":"10.1088/2632-2153/ad7191","DOIUrl":"https://doi.org/10.1088/2632-2153/ad7191","url":null,"abstract":"We combine power functional theory and machine learning to study non-equilibrium overdamped many-body systems of colloidal particles at the level of one-body fields. We first sample in steady state the one-body fields relevant for the dynamics from computer simulations of Brownian particles under the influence of randomly generated external fields. A neural network is then trained with this data to represent locally in space the formally exact functional mapping from the one-body density and velocity profiles to the one-body internal force field. The trained network is used to analyse the non-equilibrium superadiabatic force field and the transport coefficients such as shear and bulk viscosities. Due to the local learning approach, the network can be applied to systems much larger than the original simulation box in which the one-body fields are sampled. Complemented with the exact non-equilibrium one-body force balance equation and a continuity equation, the network yields viable predictions of the dynamics in time-dependent situations. Even though training is based on steady states only, the predicted dynamics is in good agreement with simulation results. A neural dynamical density functional theory can be straightforwardly implemented as a limiting case in which the internal force field is that of an equilibrium system. The framework is general and directly applicable to other many-body systems of interacting particles following Brownian dynamics.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types of particles and interactions, are considered in our study as anomalies causing deviations in data with respect to expected background events. In this work, we perform a normal-only anomaly detection, which employs only background samples, to search for manifestations of a dark version of strong force applying (variational) auto-encoders on raw detector images, which are large and highly sparse, without leveraging any physics-based pre-processing or strong assumption on the signals. The proposed CoDAE has a dual-encoder design, which is general and can learn an auxiliary yet compact latent space through spatial conditioning, showing a neat improvement over competitive physics-based baselines and related approaches, therefore also reducing the gap with fully supervised models. It is the first time an unsupervised model is shown to exhibit excellent discrimination against multiple dark shower models, illustrating the suitability of this method as an accurate, fast, model-independent algorithm to deploy, e.g. in the real-time event triggering systems of large hadron collider experiments such as ATLAS and CMS.
{"title":"Triggering dark showers with conditional dual auto-encoders","authors":"Luca Anzalone, Simranjit Singh Chhibra, Benedikt Maier, Nadezda Chernyavskaya, Maurizio Pierini","doi":"10.1088/2632-2153/ad652b","DOIUrl":"https://doi.org/10.1088/2632-2153/ad652b","url":null,"abstract":"We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types of particles and interactions, are considered in our study as anomalies causing deviations in data with respect to expected background events. In this work, we perform a normal-only anomaly detection, which employs only background samples, to search for manifestations of a dark version of strong force applying (variational) auto-encoders on raw detector images, which are large and highly sparse, without leveraging any physics-based pre-processing or strong assumption on the signals. The proposed CoDAE has a dual-encoder design, which is general and can learn an auxiliary yet compact latent space through spatial conditioning, showing a neat improvement over competitive physics-based baselines and related approaches, therefore also reducing the gap with fully supervised models. It is the first time an unsupervised model is shown to exhibit excellent discrimination against multiple dark shower models, illustrating the suitability of this method as an accurate, fast, model-independent algorithm to deploy, e.g. in the real-time event triggering systems of large hadron collider experiments such as ATLAS and CMS.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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