Automated Essay Scoring (AES) and Automatic Essay Feedback (AEF) systems aim to reduce the workload of human raters in educational assessment. However, most existing systems prioritize numeric scoring accuracy over feedback quality and are primarily evaluated on pre-secondary school level writing. This paper presents Multi-Agent Argumentation and Grammar Integrated Critiquer (MAGIC), a framework using five specialized agents to evaluate prompt adherence, persuasiveness, organization, vocabulary, and grammar for both holistic scoring and detailed feedback generation. To support evaluation at the college level, we collated a dataset of Graduate Record Examination (GRE) practice essays with expert-evaluated scores and feedback. MAGIC achieves substantial to near-perfect scoring agreement with humans on the GRE data, outperforming baseline LLM models while providing enhanced interpretability through its multi-agent approach. We also compare MAGIC's feedback generation capabilities against ground truth human feedback and baseline models, finding that MAGIC achieves strong feedback quality and naturalness.

This paper considers the development of an AI-based provably-correct mathematical proof tutor. While Large Language Models (LLMs) allow seamless communication in natural language, they are error prone. Theorem provers such as Lean allow for provable-correctness, but these are hard for students to learn. We present a proof-of-concept system (LeanTutor) by combining the complementary strengths of LLMs and theorem provers. LeanTutor is composed of three modules: (i) an autoformalizer/proof-checker, (ii) a next-step generator, and (iii) a natural language feedback generator. To evaluate the system, we introduce PeanoBench, a dataset of 371 Peano Arithmetic proofs in human-written natural language and formal language, derived from the Natural Numbers Game.

Large CS1 courses routinely need several versions of the same idea, such as practice items, make ups, and multi-form exams that target the same learning objective. he manual authoring of these isomorphic items is slow, and automatic generation of one shot often wanders off concept, changes difficulty, or produces code that does not run. We address this problem with a verification first, self-healing framework for generating CS1 exercise variants. The design is generator agnostic. In our framework, we use three separate role instances of a Large Language Model (LLM): a Generator that proposes candidate items, an Evaluator that checks them against constraints extracted from the base problem, and a Solver that produces a reference solution. Acceptance is determined only by executable docstring tests (doctests). When tests fail, a lightweight controller turns failure traces into targeted repairs and retries. In short, generation proposes and tests decide.

Undergraduate research experiences have been shown to increase student retention rates in STEM pathways, with a notable impact on students from Historically Underrepresented Groups (HUGs). However, undergraduate research experiences are often inaccessible to students, particularly in high-demand research areas and at large institutions with low faculty-to-student ratios.

While Large Language Models (LLMs) have emerged as promising methods for automated student question-answering, guaranteeing consistent instructional effectiveness of the response remains a key challenge. Therefore, there is a need for fine-grained analysis of State-Of-The-Art (SOTA) LLM-powered educational assistants.

Large Language Models (LLMs) can be used to automate many aspects of the educational field. In this paper, we look into the benefits of automating responses to student questions in course discussion forums using our Retrieval-Augmented Generation (RAG)-based LLM pipeline (Edison). Our research questions are:

This paper focuses on using Large Language Models to support teaching assistants in answering questions on large student forums such as Piazza and EdSTEM. Since student questions on these forums are often closely tied to specific aspects of the institution, instructor, and course delivery, general-purpose LLMs do not directly do well on this task. We introduce RetLLM-E, a method that combines text-retrieval and prompting approaches to enable LLMs to provide precise and high-quality answers to student questions. When presented with a student question, our system initiates a two-step process. First, it retrieves relevant context from (i) a dataset of student questions addressed by course instructors (Q&A Retrieval) and (ii) relevant segments of course materials (Document Retrieval). RetLLM-E then prompts LLM using the retrieved text and an engineered prompt structure to yield an answer optimized for the student question. We present a set of quantitative and human evaluation experiments, comparing our method to ground truth answers to questions in a test set of actual student questions. Our results demonstrate that our approach provides higher-quality responses to course-related questions than an LLM operating without context or relying solely on retrieval-based context. RetLLM-E can easily be adopted in different courses, providing instructors and students with context-aware automatic responses.

Recent advancements in instruction-tuned large language models offer new potential for enhancing students' experiences in large-scale classes. Deploying LLMs as student-facing assistants, however, presents challenges. Key issues include integrating class-specific content into responses and applying effective pedagogical techniques. This study addresses these challenges through retrieval and prompting techniques, focusing on mitigating hallucinations in LLM-generated responses, a crucial concern in education. Furthermore, practical deployment brings further challenges related to student data privacy and computational constraints. This research strives to enhance the quality and relevance of LLM responses while addressing practical deployment issues, with an emphasis on creating a versatile system for diverse domains and teaching styles.

Student-led study groups often play a key role in augmenting the classroom experience, both academically and inter-personally, through the formation of academic and personal communities. However, many students, especially underrepresented minority (URM) students, often report challenges in finding and maintaining study groups.

This paper considers the stabilization of a discrete-time linear system in the presence of multiplicative observation noise, i.e., when the controller observes the state multiplied by a random variable with a continuous density. In the case where this multiplicative observation noise has zero mean, the controller does not even have access to the sign of the state. Given this lack of sign information, a linear memoryless control strategy that optimizes the second moment would do nothing, i.e., would choose the control to be equal to zero. It is known that non-linear strategies can unboundedly outperform linear strategies for this system, yet the optimal strategy for this simple problem remains unknown.This paper provides a new achievable scheme based on the maximum a-posteriori (MAP) estimation of the state that provably stabilizes the system in any moment sense. Additionally, we can compute an explicit convergence rate for the system state. This MAP-controller emerges from a study of the evolution of the conditional density of the state. These densities illustrate the dual nature of the MAP-controller: it extracts information about the system while also driving the state towards zero. Simulations show that the MAP-controller outperforms neural-network-based control strategies as well as the previously best-known non-linear strategies.

Underrepresented students face many significant challenges in their education. In particular, they often have a harder time than their peers from majority groups in building long-term high-quality study groups. This challenge is exacerbated in remote-learning scenarios, where students are unable to meet face-to-face and must rely on pre-existing networks for social support.

Integrity 2023 is the fourth edition of the successful Workshop on Integrity in Social Networks and Media, held in conjunction with the ACM Conference on Web Search and Data Mining (WSDM) in the past three years. The goal of the workshop is to bring together researchers and practitioners to discuss content and interaction integrity challenges in social networks and social media platforms. The event consists of a combination of invited talks by reputed members of the Integrity community from both academia and industry and peer-reviewed contributed talks and posters solicited via an open call-for-papers.

Control strategies for vector systems typically depend on the controller's ability to plan out future control actions. However, in the case where model parameters are random and time-varying, this planning might not be possible. This paper explores the fundamental limits of a simple system, inspired by the intermittent Kalman filtering model, where the actuation direction is drawn uniformly from the unit hypersphere. The model allows us to focus on a fundamental tension in the control of underactuated vector systems - the need to balance the growth of the system in different dimensions. We characterize the stabilizability of$d$-dimensional systems with symmetric gain matrices by providing tight necessary and sufficient conditions that depend on the eigenvalues of the system. The proof technique is slightly different from the standard dynamic programming approach and relies on the fact that the second moment stability of the system can also be understood by examining any arbitrary weighted two-norm of the state.

The second Workshop on Integrity in Social Networks and Media is held in conjunction with the 14th ACM Conference on Web Search and Data Mining (WSDM) in Jerusalem, Israel. The goal of the workshop is to bring together researchers and practitioners to discuss content and interaction integrity challenges in social networks and social media platforms.

We study the stabilization of a linear control system with an unbounded random system gain where the controller must act based on a rate-limited observation of the state. More precisely, we consider the system Xn+1=AnXn+Wn-Un, where the An's are drawn independently at random at each time n from a known distribution with unbounded support, and where the controller receives at most R bits about the system state at each time from an encoder. We provide a time-varying achievable strategy to stabilize the system in a second-moment sense with fixed, finite R. While our previous result provided a strategy to stabilize this system using a variable-rate code, this work provides an achievable strategy using a fixed-rate code. The strategy we employ to achieve this is time-varying and takes different actions depending on the value of the state. It proceeds in two modes: a normal mode (or zoom-in), where the realization of Anis typical, and an emergency mode (or zoom-out), where the realization of Anis exceptionally large. To analyze the performance of the scheme we construct an auxiliary sequence that bounds the state Xn, and then bound auxiliary sequence in both the zoom-in and zoom-out modes.

We consider the stabilization of a linear control system with multiplicative observation noise. Linear strategies have been proven to be ineffective in this system, and non-linear strategies can unboundedly outperform the best linear strategies. The current best-known control strategy is hand-crafted and far from optimal.In this paper, we use neural-network-based controllers to narrow the gap between the achievability and the converse for this problem. We propose a periodic controller structure and a greedy training procedure. This enables us to train our controller on a finite horizon problem but learn strategies that outperform the best-known hand-crafted strategy and also generalize, i.e., provide stabilizing control at times beyond the training horizon. Further, we show that for our periodic approach, the learned strategies display a piecewise linear structure and are well approximated by interpretable functions.

The Russia-based Internet Research Agency (IRA) carried out a broad information campaign in the U.S. before and after the 2016 presidential election. The organization created an expansive set of internet properties: web domains, Facebook pages, and Twitter bots, which received traffic via purchased Facebook ads, tweets, and search engines indexing their domains. In this paper, we focus on IRA activities that received exposure through search engines, by joining data from Facebook and Twitter with logs from the Internet Explorer 11 and Edge browsers and the Bing.com search engine.

Recent work in networked systems has shown that using aerial imagery for farm monitoring can enable precision agriculture by lowering the cost and reducing the overhead of large scale sensor deployment. However, acquiring aerial imagery requires a drone, which has high capital and operational costs, often beyond the reach of farmers in the developing world. In this paper, we present TYE (Tethered eYE), an inexpensive platform for aerial imagery. It consists of a tethered helium balloon with a custom mount that can hold a smartphone (or a camera) with a battery pack. The balloon can be carried using a tether by a person or a vehicle. We incorporate various techniques to increase the operational time of the system, and to provide actionable insights even with unstable imagery. We develop path-planning algorithms and use that to develop an interactive mobile phone application that provides the user instant feedback to guide users to efficiently traverse large areas of land. We use computer vision algorithms to stitch orthomosaics by effectively countering wind-induced motion of the camera. We have used TYE for aerial imaging of agricultural land for over a year, and envision it as a low-cost aerial imaging platform for similar applications.

Interactive, immersive, and other timing-critical applications demand ultra-reliable low-latency communication (URLLC). To build wireless communication systems that can support these applications, understanding the relevant characteristics of the wireless medium is paramount. Although wireless channel characteristics and dynamics have been extensively studied, it is important to revisit these concepts in the context of the strict demands of low-latency and ultra-high reliability. In this paper, we bring a modeling approach from robust control to wireless communication-the wireless channel characteristics are given a nominal model around which we allow for some quantified uncertainty. We propose certain key URLLC-relevant parameters along which the model uncertainty is to be bounded. To validate the nominal model of the spatially independent quasi-static Rayleigh fading, we take an in-depth look at the spatial and temporal correlations based on Jakes' model. We find that although the Rayleigh fading process is band-limited, the quasi-static assumption is not safe for relay selection even well within a single coherence time. We also find that under reasonable conditions, the spatial correlation of channels provide a fading distribution that is not too far off from an independent spatial fading model. In addition, we look at the impact of these channel models on cooperative communication-based systems. We find that while spatial-diversity-based techniques are necessary to combat the adverse effects of fading, time-diversity-based techniques are necessary to be robust against unmodeled errors. Robust URLLC systems need to operate with both an adequate SNR margin and a time margin through repetitions.

Feedback control actively dissipates uncertainty from a dynamical system by means of actuation. We develop a notion of “control capacity” that gives a fundamental limit (in bits) on the rate at which a controller can dissipate the uncertainty from a system, i.e., stabilize to a known fixed point. We give a computable single-letter characterization of control capacity for memoryless stationary scalar multiplicative actuation channels. Control capacity allows us to answer questions of stabilizability for scalar linear systems: a system with actuation uncertainty is stabilizable if and only if the control capacity is larger than the log of the unstable open-loop eigenvalue. For second-moment senses of stability, we recover the classic uncertainty threshold principle result. However, our definition of control capacity can quantify the stabilizability limits for any moment of stability. Our formulation parallels the notion of Shannon's communication capacity and thus yields both a strong converse and a way to compute the value of side information in control.

Location data from mobile devices is a sensitive yet valuable commodity for location-based services and advertising. We investigate the intrinsic value of location data in the context of strong privacy, where location information is only available from end users via purchase. We present an algorithm to compute the expected value of location data from a user, without access to the specific coordinates of the location data point. We use decision-theoretic techniques to provide a principled way for a potential buyer to make purchasing decisions about private user location data. We illustrate our approach in two scenarios: the delivery of targeted ads specific to a user's home location and the estimation of traffic speed. In both cases, the methodology leads to quantifiably better purchasing decisions than competing methods.

Recent successes in reinforcement learning have lead to the development of complex controllers for realworld robots. As these robots are deployed in safety-critical applications and interact with humans, it becomes critical to ensure safety in order to avoid causing harm. A first step in this direction is to test the controllers in simulation. To be able to do this, we need to capture what we mean by safety and then efficiently search the space of all behaviors to see if they are safe. In this paper, we present an active-testing framework based on Bayesian Optimization. We specify safety constraints using logic and exploit structure in the problem in order to test the system for adversarial counter examples that violate the safety specifications. These specifications are defined as complex boolean combinations of smooth functions on the trajectories and, unlike reward functions in reinforcement learning, are expressive and impose hard constraints on the system. In our framework, we exploit regularity assumptions on individual functions in form of a Gaussian Process (GP) prior. We combine these into a coherent optimization framework using problem structure. The resulting algorithm is able to provably verify complex safety specifications or alternatively find counter examples. Experimental results show that the proposed method is able to find adversarial examples quickly.

We study the stabilization of an unpredictable linear control system where the controller must act based on a rate-limited observation of the state. More precisely, we consider the system X_(n+1) = A_n X_n +W_n –U_n, where the A_n's are drawn independently at random at each time n from a known distribution with unbounded support, and where the controller receives at most R bits about the system state at each time from an encoder. We provide a time-varying achievable strategy to stabilize the system in a second-moment sense with fixed, finite R. While our previous result provided a strategy to stabilize this system using a variable-rate code, this work provides an achievable strategy using a fixed-rate code. The strategy we employ to achieve this is time-varying and takes different actions depending on the value of the state. It proceeds in two modes: a normal mode (or zoom-in), where the realization of A_n is typical, and an emergency mode (or zoom-out), where the realization of A_n is exceptionally large.

Ultra-reliable, low-latency wireless communication is essential to enable critical and interactive applications. The cooperative communication schemes for such ultra-reliable communication must harvest multi-user diversity to achieve their specifications. The underlying low-latency space-time codes for a large number of users (> 10) place burdens on practical implementations due to the large number of simultaneous relays they must use. To address this, we propose an adaptive relay selection technique that selects a small set of good relays, instead of using every available radio to relay. Using our simple relay-selection schemes, we can support a network with 30 nodes requiring system failure probability under 10 -9and 2ms latency with only 3 simultaneously active relays per message. In contrast, in the absence of adaptive relay selection, we must rely on 13 relays to achieve the same reliability. To arrive at such relay selection schemes, we revisit the fading dynamics of wireless channels in the context of ultra-high reliability. Contrary to what has been claimed in the literature, we find that standard Rayleigh fading processes are not bandlimited. However, these fading processes are fairly predictable on the short time scales of the regime of interest.

We present an analysis of traffic to websites known for publishing fake news in the months preceding the 2016 US presidential election. The study is based on the combined instrumentation data from two popular desktop web browsers: Internet Explorer 11 and Edge. We find that social media was the primary outlet for the circulation of fake news stories and that aggregate voting patterns were strongly correlated with the average daily fraction of users visiting websites serving fake news. This correlation was observed both at the state level and at the county level, and remained stable throughout the main election season. We propose a simple model based on homophily in social networks to explain the linear association. Finally, we highlight examples of different types of fake news stories: while certain stories continue to circulate in the population, others are short-lived and die out in a few days.

The budgeted information gathering problem - where a robot with a fixed fuel budget is required to maximize the amount of information gathered from the world - appears in practice across a wide range of applications in autonomous exploration and inspection with mobile robots. Although there is an extensive amount of prior work investigating effective approximations of the problem, these methods do not address the fact that their performance is heavily dependent on distribution of objects in the world. In this paper, we attempt to address this issue by proposing a novel data-driven imitation learning framework. We present an efficient algorithm, EXPLORE, that trains a policy on the target distribution to imitate a clairvoyant oracle - an oracle that has full information about the world and computes non-myopic solutions to maximize information gathered. We validate the approach on a spectrum of results on a number of 2D and 3D exploration problems that demonstrates the ability of EXPLORE to adapt to different object distributions. Additionally, our analysis provides theoretical insight into the behavior of EXPLORE. Our approach paves the way forward for efficiently applying data-driven methods to the domain of information gathering.

This paper explores the problem of path planning under uncertainty. Specifically, we consider online receding horizon based planners that need to operate in a latent environment where the latent information can be modelled via Gaussian Processes. Online path planning in latent environments is challenging since the robot needs to explore the environment to get a more accurate model of latent information for better planning later and also achieves the task as quick as possible. We propose UCB style algorithms that are popular in the bandit settings and show how those analyses can be adapted to the online robotic path planning problems. The proposed algorithm trades-off exploration and exploitation in near-optimal manner and has appealing no-regret properties. We demonstrate the efficacy of the framework on the application of aircraft flight path planning when the winds are partially observed.

High-performance industrial automation systems rely on tens of simultaneously active sensors and actuators and have stringent communication latency and reliability requirements. Current wireless technologies, such as Wi-Fi, Bluetooth, and LTE are unable to meet these requirements, forcing the use of wired communication in industrial control systems. This paper introduces a wireless communication protocol that capitalizes on multiuser diversity and cooperative communication to achieve the ultra-reliability with a low-latency constraint. Our protocol is analyzed using the communication-theoretic delay-limitedcapacity framework and compared with baseline schemes that primarily exploit frequency diversity. For a scenario inspired by an industrial printing application with 30 nodes in the control loop, 20-B messages transmitted between pairs of nodes and a cycle time of 2 ms, an idealized protocol can achieve a cycle failure probability (probability that any packet in a cycle is not successfully delivered) lower than 10-9with nominal SNR below 5 dB in a 20-MHz wide channel.

This paper considers the stabilization of an unstable discrete-time linear system that is observed over a channel corrupted by continuous multiplicative noise. The main result is a converse bound that shows that if the system growth is large enough the system cannot be stabilized in a mean-squared sense. This is done by showing that the probability of the state magnitude remains bounded must go to zero with time. It was known that a system with multiplicative observation noise can be stabilized using a simple linear strategy if the system growth is suitably bounded. However, it was not clear whether non-linear controllers could overcome arbitrarily large growth factors. One difficulty with using the standard approach for a data-rate theorem style converse is that the mutual information per round between the system state and the observation is potentially unbounded with a multiplicative noise observation channel. Our proof technique recursively bounds the conditional density of the system state (instead of focusing on the second moment) to bound the progress the controller can make.

Cooperative communication to extract multi-user diversity and network coding are two ideas for improving wireless protocols. These ideas can be exploited to design protocols for low-latency high-reliability communication for control. Given the high-performance constraints for this communication, it is critical, to understand how sensitive such protocols are to modeling assumptions. We examine the impact of channel reciprocity, quasi-static fading, and the spatial independence of channel fades in this paper. This paper uses simple models to explore the performance sensitivity to assumptions. It turns out that wireless network-coding is moderately sensitive to channel reciprocity and non-reciprocity costs about 2dB SNR. The loss of the quasi-static fading assumption has a similar cost for the network coding based protocol but has a negligible effect on the protocol that doesn't use network coding. The real sensitivity of cooperative communication protocols is to the spatial independence assumptions. Capping the amount of independence to a small number degrades performance but perhaps more surprisingly, a simple Gilbert-Elliott-inspired model shows that having a random amount of independence can also severely impact performance.

We explore a novel, free-space optics based approach for building data center interconnects. It uses a digital micromirror device (DMD) and mirror assembly combination as a transmitter and a photodetector on top of the rack as a receiver (Figure 1). Our approach enables all pairs of racks to establish direct links, and we can reconfigure such links (i.e., connect different rack pairs) within 12 us. To carry traffic from a source to a destination rack, transmitters and receivers in our interconnect can be dynamically linked in millions of ways. We develop topology construction and routing methods to exploit this flexibility, including a flow scheduling algorithm that is a constant factor approximation to the offline optimal solution. Experiments with a small prototype point to the feasibility of our approach. Simulations using realistic data center workloads show that, compared to the conventional folded-Clos interconnect, our approach can improve mean flow completion time by 30-95% and reduce cost by 25-40%.

The Internet of Things (IoT) envisions simultaneous sensing and actuation of numerous wirelessly connected devices. Emerging human-in-the-loop applications demand low-latency high-reliability communication protocols, paralleling the requirements for high-performance industrial control. This paper introduces a wireless communication protocol based on network coding that in conjunction with cooperative communication techniques builds the necessary diversity to achieve the target reliability. The proposed protocol, XOR-CoW, is analyzed by using a communication theoretic delay-limited-capacity framework and compared to different realizations of previously proposed protocols without network coding. The results show that as the network size or payload increases, XOR-CoW gains advantage in minimum SNR to achieve the target latency. For a scenario inspired by an industrial printing application with 30 nodes in the control loop, total information throughput of 4.8 Mb/s, 20MHz of bandwidth and cycle time under 2 ms, the protocol can robustly achieve a system probability of error better than 10-9with a nominal SNR less than 2 dB with Rayleigh fading.

The Internet of Things envisions not only sensing but also actuation of numerous wirelessly connected devices. Seamless control with humans in the loop requires latencies on the order of a millisecond with very high reliabilities, paralleling the requirements for high-performance industrial control. Today's practical wireless systems cannot meet these reliability and latency requirements, forcing the use of wired systems. This paper introduces a wireless communication protocol, dubbed “Occupy CoW,” based on cooperative communication among nodes in the network to build the diversity necessary for the target reliability. Simultaneous retransmission by many relays achieves this without significantly decreasing throughput or increasing latency. The protocol is analyzed using the communication theoretic delay-limited-capacity framework and compared to baseline schemes that primarily exploit frequency diversity. In particular, we develop a novel “diversity meter” designed to measure “effective diversity” in the non-asymptotic regime. For a scenario inspired by an industrial printing application with 30 nodes in the control loop, total information throughput of 4.8 Mb/s, and cycle time under 2 ms, the protocol can robustly achieve a system probability of error better than 10−9with nominal SNR below 5 dB.

This paper presents a notion of “control capacity” that gives a fundamental limit on the control of a system through an unreliable actuation channel. It tells us how fast we can reliably actively dissipate uncertainty in a system through that actuation channel. We give a computable single-letter characterization for scalar systems with memoryless stationary multiplicative actuation channels. The sense of control capacity is tight for answering questions of stabilizability for scalar linear systems - a system is stabilizable through an actuation channel if and only if the control capacity of that actuation channel is larger than the log of the unstable open-loop eigenvalue. For second-moment senses of stability, our result recovers the classic uncertainty-threshold principle result. However, our formulation can also deal with any other moment. The limits of higher and higher moment senses of stability correspond to a “zero-error” sense of control capacity and taking the limit to weaker-andweaker moments corresponds to a “Shannon” sense of control capacity.

As in portfolio theory, we can think of the value of side-information in a control system as the change in the “growth rate” due to side-information. A scalar counterexample (motivated by carry-free deterministic models) shows the value of side-information for control does not exactly parallel the value of side-information for portfolios. Mutual-information does not seem to be a bound here. The concept is further explored through a spinning vector control system that is re-oriented at each time so that the control or observation direction is partially unknown. The value of side-information can be calculated in this setup and it behaves quite differently in a control vs. estimation context. A second example considers the problem of vector control over a (scalar) erasure channel, the dual problem to the estimation problem of intermittent Kalman Filtering. The value of information here is measured through the change in the critical packet-drop probability for the system. While non-causal side-information regarding the packet arrivals does not affect the critical probability for the estimation problem, we find that it can generically be very valuable for the control problem - it seems to change the scaling behavior for the control counterpart to what would be considered the “high SNR limit” in communication problems.

The generalized deterministic models recently proposed by Niesen and Maddah-Ali [1] successfully capture real-interference alignment as observed in Gaussian models. Simpler deterministic models, like ADT models [2], cannot demonstrate this phenomenon because they are limited in the set of channel gains they can model. This paper reinterprets the Niesen and Maddah-Ali models through the lens of carry-free operations. We further explore these carry-free models by considering i.i.d. unknown fading networks. In the unknown fading context, a carry-free model can be further simplified to a max-superposition model, where signals are superposed by a nonlinear max operation. Unlike in relay-networks with known fading and linear superposition, we find that decode-and-forward can perform arbitrarily better than compress-and-forward in max-superposition relay networks with unknown fading.

The idea of modeling an unknown channel using a broadcast channel was first introduced by Cover1in 1972. This paper builds on his line of thought to consider priority encoding of communication over unknown channels without feedback, using fixed-length codes and from a single-shot, individual channel perspective. A ratio-regret metric is used to understand how well we can perform with respect to the actual channel realization.

Optimal control strategies for decentralized control problems may involve internal communication between controllers. We think of such internal communication as implicit, since the messages being sent are endogenous to the system and not externally specified. Recently, Grover and Sahai [1] applied information-theoretic techniques to provide an approximately optimal scheme for the Witsenhausen counterexample: one of the simplest models of a decentralized control system. This paper examines a MAC-inspired extension of the Witsenhausen counterexample. Deterministic modeling techniques based on the work by Avestimehr at al. [2] feature centrally in the strategy development. This example illustrates that “Information is in the eye of the beholder”, and we find “rate gains” in the context of implicit communication. These are not observed in the original Witsenhausen counterexample.