Pub Date : 2025-01-13DOI: 10.1109/JSAIT.2025.3528825
{"title":"2024 Index IEEE Journal on Selected Areas in Information Theory Vol. 5","authors":"","doi":"10.1109/JSAIT.2025.3528825","DOIUrl":"https://doi.org/10.1109/JSAIT.2025.3528825","url":null,"abstract":"","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"702-714"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10839060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"JSAIT Issue on Information-Theoretic Methods for Trustworthy and Reliable Machine Learning","authors":"Lalitha Sankar;Oliver Kosut;Flavio Calmon;Ayfer Ozgur;Lele Wang;Ofer Shayevitz;Parastoo Sadeghi","doi":"10.1109/JSAIT.2024.3508492","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3508492","url":null,"abstract":"","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"xii-xv"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10830758","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1109/JSAIT.2024.3499012
Jun Chen;Jerry Gibson;Ioannis Kontoyiannis;Yingbin Liang;S. Sandeep Pradhan;Andreas Winter;Ram Zamir;Richard E. Blahut;Yasutada Oohama;Aaron B. Wagner;Raymond W. Yeung
{"title":"Editorial Data, Physics, and Life Through the Lens of Information Theory","authors":"Jun Chen;Jerry Gibson;Ioannis Kontoyiannis;Yingbin Liang;S. Sandeep Pradhan;Andreas Winter;Ram Zamir;Richard E. Blahut;Yasutada Oohama;Aaron B. Wagner;Raymond W. Yeung","doi":"10.1109/JSAIT.2024.3499012","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3499012","url":null,"abstract":"","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"iv-xi"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10826512","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1109/JSAIT.2024.3519913
{"title":"Board of Governors","authors":"","doi":"10.1109/JSAIT.2024.3519913","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3519913","url":null,"abstract":"","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10826513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper studies the rate-distortion-perception (RDP) tradeoff for a Gaussian vector source coding problem where the goal is to compress the multi-component source subject to distortion and perception constraints. Specifically, the RDP setting with either the Kullback-Leibler (KL) divergence or Wasserstein-2 metric as the perception loss function is examined, and it is shown that for Gaussian vector sources, jointly Gaussian reconstructions are optimal. We further demonstrate that the optimal tradeoff can be expressed as an optimization problem, which can be explicitly solved. An interesting property of the optimal solution is as follows. Without the perception constraint, the traditional reverse water-filling solution for characterizing the rate-distortion (RD) tradeoff of a Gaussian vector source states that the optimal rate allocated to each component depends on a constant, called the water level. If the variance of a specific component is below the water level, it is assigned a zero compression rate. However, with active distortion and perception constraints, we show that the optimal rates allocated to the different components are always positive. Moreover, the water levels that determine the optimal rate allocation for different components are unequal. We further treat the special case of perceptually perfect reconstruction and study its RDP function in the high-distortion and low-distortion regimes to obtain insight to the structure of the optimal solution.
{"title":"Rate-Distortion-Perception Tradeoff for Gaussian Vector Sources","authors":"Jingjing Qian;Sadaf Salehkalaibar;Jun Chen;Ashish Khisti;Wei Yu;Wuxian Shi;Yiqun Ge;Wen Tong","doi":"10.1109/JSAIT.2024.3509420","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3509420","url":null,"abstract":"This paper studies the rate-distortion-perception (RDP) tradeoff for a Gaussian vector source coding problem where the goal is to compress the multi-component source subject to distortion and perception constraints. Specifically, the RDP setting with either the Kullback-Leibler (KL) divergence or Wasserstein-2 metric as the perception loss function is examined, and it is shown that for Gaussian vector sources, jointly Gaussian reconstructions are optimal. We further demonstrate that the optimal tradeoff can be expressed as an optimization problem, which can be explicitly solved. An interesting property of the optimal solution is as follows. Without the perception constraint, the traditional reverse water-filling solution for characterizing the rate-distortion (RD) tradeoff of a Gaussian vector source states that the optimal rate allocated to each component depends on a constant, called the water level. If the variance of a specific component is below the water level, it is assigned a zero compression rate. However, with active distortion and perception constraints, we show that the optimal rates allocated to the different components are always positive. Moreover, the water levels that determine the optimal rate allocation for different components are unequal. We further treat the special case of perceptually perfect reconstruction and study its RDP function in the high-distortion and low-distortion regimes to obtain insight to the structure of the optimal solution.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"6 ","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-11DOI: 10.1109/JSAIT.2024.3496197
Yasutada Oohama
We consider the one helper source coding problem posed and investigated by Ahlswede, Körner, and Wyner for a class of information sources with memory. For this class of information sources we give explicit inner and outer bounds of the admissible rate region. We also give a certain nontrivial class of information sources where the inner and outer bounds match.
{"title":"Source Coding for Markov Sources With Partial Memoryless Side Information at the Decoder","authors":"Yasutada Oohama","doi":"10.1109/JSAIT.2024.3496197","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3496197","url":null,"abstract":"We consider the one helper source coding problem posed and investigated by Ahlswede, Körner, and Wyner for a class of information sources with memory. For this class of information sources we give explicit inner and outer bounds of the admissible rate region. We also give a certain nontrivial class of information sources where the inner and outer bounds match.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"675-693"},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-31DOI: 10.1109/JSAIT.2024.3487856
Yichen Huang
In a spin chain governed by a local Hamiltonian, we consider a microcanonical ensemble in the middle of the energy spectrum and a contiguous subsystem whose length is a constant fraction of the system size. We prove that if the bandwidth of the ensemble is greater than a certain constant, then the average entanglement entropy (between the subsystem and the rest of the system) of eigenstates in the ensemble deviates from the maximum entropy by at least a positive constant. This result highlights the difference between the entanglement entropy of mid-spectrum eigenstates of (chaotic) local Hamiltonians and that of random states. We also prove that the former deviates from the thermodynamic entropy at the same energy by at least a positive constant.
{"title":"Deviation From Maximal Entanglement for Mid-Spectrum Eigenstates of Local Hamiltonians","authors":"Yichen Huang","doi":"10.1109/JSAIT.2024.3487856","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3487856","url":null,"abstract":"In a spin chain governed by a local Hamiltonian, we consider a microcanonical ensemble in the middle of the energy spectrum and a contiguous subsystem whose length is a constant fraction of the system size. We prove that if the bandwidth of the ensemble is greater than a certain constant, then the average entanglement entropy (between the subsystem and the rest of the system) of eigenstates in the ensemble deviates from the maximum entropy by at least a positive constant. This result highlights the difference between the entanglement entropy of mid-spectrum eigenstates of (chaotic) local Hamiltonians and that of random states. We also prove that the former deviates from the thermodynamic entropy at the same energy by at least a positive constant.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"694-701"},"PeriodicalIF":0.0,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1109/JSAIT.2024.3481296
Tomer Berg;Or Ordentlich;Ofer Shayevitz
The problem of statistical inference in its various forms has been the subject of decades-long extensive research. Most of the effort has been focused on characterizing the behavior as a function of the number of available samples, with far less attention given to the effect of memory limitations on performance. Recently, this latter topic has drawn much interest in the engineering and computer science literature. In this survey paper, we attempt to review the state-of-the-art of statistical inference under memory constraints in several canonical problems, including hypothesis testing, parameter estimation, and distribution property testing/estimation. We discuss the main results in this developing field, and by identifying recurrent themes, we extract some fundamental building blocks for algorithmic construction, as well as useful techniques for lower bound derivations.
{"title":"Statistical Inference With Limited Memory: A Survey","authors":"Tomer Berg;Or Ordentlich;Ofer Shayevitz","doi":"10.1109/JSAIT.2024.3481296","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3481296","url":null,"abstract":"The problem of statistical inference in its various forms has been the subject of decades-long extensive research. Most of the effort has been focused on characterizing the behavior as a function of the number of available samples, with far less attention given to the effect of memory limitations on performance. Recently, this latter topic has drawn much interest in the engineering and computer science literature. In this survey paper, we attempt to review the state-of-the-art of statistical inference under memory constraints in several canonical problems, including hypothesis testing, parameter estimation, and distribution property testing/estimation. We discuss the main results in this developing field, and by identifying recurrent themes, we extract some fundamental building blocks for algorithmic construction, as well as useful techniques for lower bound derivations.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"623-644"},"PeriodicalIF":0.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-30DOI: 10.1109/JSAIT.2024.3469929
Michael G. Jabbour;Nilanjana Datta
Uniform continuity bounds on entropies are generally expressed in terms of a single distance measure between probability distributions or quantum states, typically, the total variation- or trace distance. However, if an additional distance measure is known, the continuity bounds can be significantly strengthened. Here, we prove a tight uniform continuity bound for the Shannon entropy in terms of both the local- and total variation distances, sharpening an inequality in (Sason, 2013). We also obtain a uniform continuity bound for the von Neumann entropy in terms of both the operator norm- and trace distances. We then apply our results to compute upper bounds on channel capacities. We first refine the concept of approximate degradable channels by introducing $(varepsilon ,nu)$ –degradable channels. These are $varepsilon $ –close in diamond norm and $nu $ –close in completely bounded spectral norm to their complementary channel when composed with a degrading channel. This leads to improved upper bounds to the quantum- and private classical capacities of such channels. Moreover, these bounds can be further improved by considering certain unstabilized versions of the above norms. We show that upper bounds on the latter can be efficiently expressed as semidefinite programs. As an application, we obtain a new upper bound on the quantum capacity of the qubit depolarizing channel.
{"title":"Tightening Continuity Bounds for Entropies and Bounds on Quantum Capacities","authors":"Michael G. Jabbour;Nilanjana Datta","doi":"10.1109/JSAIT.2024.3469929","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3469929","url":null,"abstract":"Uniform continuity bounds on entropies are generally expressed in terms of a single distance measure between probability distributions or quantum states, typically, the total variation- or trace distance. However, if an additional distance measure is known, the continuity bounds can be significantly strengthened. Here, we prove a tight uniform continuity bound for the Shannon entropy in terms of both the local- and total variation distances, sharpening an inequality in (Sason, 2013). We also obtain a uniform continuity bound for the von Neumann entropy in terms of both the operator norm- and trace distances. We then apply our results to compute upper bounds on channel capacities. We first refine the concept of approximate degradable channels by introducing \u0000<inline-formula> <tex-math>$(varepsilon ,nu)$ </tex-math></inline-formula>\u0000–degradable channels. These are \u0000<inline-formula> <tex-math>$varepsilon $ </tex-math></inline-formula>\u0000–close in diamond norm and \u0000<inline-formula> <tex-math>$nu $ </tex-math></inline-formula>\u0000–close in completely bounded spectral norm to their complementary channel when composed with a degrading channel. This leads to improved upper bounds to the quantum- and private classical capacities of such channels. Moreover, these bounds can be further improved by considering certain unstabilized versions of the above norms. We show that upper bounds on the latter can be efficiently expressed as semidefinite programs. As an application, we obtain a new upper bound on the quantum capacity of the qubit depolarizing channel.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"645-658"},"PeriodicalIF":0.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1109/JSAIT.2024.3466649
Sundara Rajan Srinivasavaradhan;Pavlos Nikolopoulos;Christina Fragouli;Suhas Diggavi
Proactive testing and interventions are crucial for disease containment during a pandemic until widespread vaccination is achieved. However, a key challenge remains: Can we accurately identify all new daily infections with only a fraction of tests needed compared to testing everyone, everyday? Group testing reduces the number of tests but overlooks infection dynamics and non i.i.d nature of infections in a community, while on the other hand traditional SIR (Susceptible-Infected-Recovered) models address these dynamics but don’t integrate discrete-time testing and interventions. This paper bridges the gap. We propose a “discrete-time SIR stochastic block model” that incorporates group testing and daily interventions, as a discrete counterpart to the well-known continuous-time SIR model that reflects community structure through a specific weighted graph. We analyze the model to determine the minimum number of daily group tests required to identify all infections with vanishing error probability. We find that one can leverage the knowledge of the community and the model to inform nonadaptive group testing algorithms that are order-optimal, and therefore achieve the same performance as complete testing using a much smaller number of tests.
在实现大范围疫苗接种之前，主动检测和干预对于大流行期间的疾病控制至关重要。然而，关键的挑战依然存在：与每天对每个人进行检测相比，我们能否只用一小部分检测就能准确识别每天所有的新感染病例？集体检测减少了检测次数，但却忽略了社区中的感染动态和感染的非即期性，而另一方面，传统的 SIR（易感者-感染者-康复者）模型解决了这些动态问题，但却没有整合离散时间检测和干预措施。本文弥补了这一不足。我们提出了一种 "离散时间 SIR 随机区块模型"，该模型结合了分组检测和日常干预，是著名的连续时间 SIR 模型的离散对应模型，后者通过特定的加权图反映社区结构。我们对该模型进行了分析，以确定以消失的误差概率识别所有感染所需的最小每日分组测试次数。我们发现，我们可以利用对群体和模型的了解，为非适应性群体测试算法提供信息，这种算法是阶次最优的，因此可以用更少的测试次数达到与完全测试相同的性能。
{"title":"Dynamic Group Testing to Control and Monitor Disease Progression in a Population","authors":"Sundara Rajan Srinivasavaradhan;Pavlos Nikolopoulos;Christina Fragouli;Suhas Diggavi","doi":"10.1109/JSAIT.2024.3466649","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3466649","url":null,"abstract":"Proactive testing and interventions are crucial for disease containment during a pandemic until widespread vaccination is achieved. However, a key challenge remains: Can we accurately identify all new daily infections with only a fraction of tests needed compared to testing everyone, everyday? Group testing reduces the number of tests but overlooks infection dynamics and non i.i.d nature of infections in a community, while on the other hand traditional SIR (Susceptible-Infected-Recovered) models address these dynamics but don’t integrate discrete-time testing and interventions. This paper bridges the gap. We propose a “discrete-time SIR stochastic block model” that incorporates group testing and daily interventions, as a discrete counterpart to the well-known continuous-time SIR model that reflects community structure through a specific weighted graph. We analyze the model to determine the minimum number of daily group tests required to identify all infections with vanishing error probability. We find that one can leverage the knowledge of the community and the model to inform nonadaptive group testing algorithms that are order-optimal, and therefore achieve the same performance as complete testing using a much smaller number of tests.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"609-622"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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