Belief propagation with quantum messages (BPQM) provides a low-complexity alternative to collective measurements for communication over classical--quantum channels. Prior BPQM constructions and density-evolution (DE) analyses have focused on binary alphabets. Here, we generalize BPQM to symmetric q-ary pure-state channels (PSCs) whose output Gram matrix is circulant. For this class, we show that bit-node and check-node combining can be tracked efficiently via closed-form recursions on the Gram-matrix eigenvalues, independent of the particular physical realization of the output states. These recursions yield explicit BPQM unitaries and analytic bounds on the fidelities of the combined channels in terms of the input-channel fidelities. This provides a DE framework for symmetric q-ary PSCs that allows one to estimate BPQM decoding thresholds for LDPC codes and to construct polar codes on these channels.

The question of whether Reed-Muller (RM) codes achieve capacity on binary memoryless symmetric (BMS) channels has drawn attention since it was resolved positively for the binary erasure channel by Kudekar et al. in 2016. In 2021, Reeves and Pfister extended this to prove the bit-error probability vanishes on BMS channels when the code rate is less than capacity. In 2023, Abbe and Sandon improved this to show the block-error probability also goes to zero. These results analyze decoding functions using symmetry and the nested structure of RM codes. In this work, we focus on binary-input symmetric classicalquantum (BSCQ) channels and the Holevo capacity. For a BSCQ, we consider observables that estimate the channel input in the sense of minimizing the mean-squared error (MSE). Using the orthogonal decomposition of these observables under a weighted inner product, we establish a recursive relation for the minimum MSE estimate of a single bit in the RM code. Our results show that any set of$2^{o(\sqrt{\log N})}$bits can be decoded with high probability when the code rate is less than the Holevo capacity.

In the quantum compression scheme proposed by Schumacher, Alice compresses a message that Bob decompresses. In that approach, there is some probability of failure and, even when successful, some distortion of the state. For sufficiently large blocklengths, both of these imperfections can be made arbitrarily small while achieving a compression rate that asymptotically approaches the source coding bound. However, direct implementation of Schumacher compression suffers from poor circuit complexity. In this paper, we consider a slightly different approach based on classical syndrome source coding. The idea is to use a linear error-correcting code and treat the state to be compressed as a superposition of error patterns. Then, Alice can use quantum gates to apply the parity-check matrix to her message state. This will convert it into a superposition of syndromes. If the original superposition was supported on correctable errors (e.g., coset leaders), then this process can be reversed by decoding. An implementation of this based on polar codes is described and simulated. As in classical source coding based on polar codes, Alice maps the information into the “frozen” qubits that constitute the syndrome. To decompress, Bob utilizes a quantum version of successive cancellation coding.

Facial expressions have been considered a metric reflecting a person’s engagement with a task. While the evolution of expression detection methods is consequential, the foundation remains mostly on image processing techniques that suffer from occlusion, ambient light, and privacy concerns. In this paper, we propose ExpresSense, a lightweight application for standalone smartphones that relies on near-ultrasound acoustic signals for detecting users’ facial expressions. ExpresSense has been tested on different users in lab-scaled and large-scale studies for both posed as well as natural expressions. By achieving a classification accuracy of over various basic expressions, we discuss the potential of a standalone smartphone to sense expressions through acoustic sensing.

This paper considers the design and decoding of polar codes for general classical-quantum (CQ) channels. It focuses on decoding via belief-propagation with quantum messages (BPQM) and, in particular, the idea of paired-measurement BPQM (PM-BPQM) decoding. Since the PM-BPQM decoder admits a classical density evolution (DE) analysis, one can use DE to design a polar code for any CQ channel and then efficiently compute the trade-off between code rate and error probability. We have also implemented and tested a classical simulation of our PM-BPQM decoder for polar codes. While the decoder can be implemented efficiently on a quantum computer, simulating the decoder on a classical computer actually has exponential complexity. Thus, simulation results for the decoder are somewhat limited and are included primarily to validate our theoretical results.

Detecting dangerous driving has been of critical interest for the past few years. However, a practical yet minimally intrusive solution remains challenging as existing technologies heavily rely on visual features or physical proximity. With this motivation, we explore the feasibility of purely using mm Wave radars to detect dangerous driving behaviors. We first study characteristics of dangerous driving and find some unique patterns of range-doppler caused by 9 typical dangerous driving actions. We then develop a novel Fused-CNN model to detect dangerous driving instances from regular driving and classify 9 different dangerous driving actions. Through extensive experiments with 5 volunteer drivers in real driving environments, we observe that our system can distinguish dangerous driving actions with an average accuracy of 97(±2)%. We also compare our approach with existing state-of-the-art baselines to establish their significance,

We consider maximizing the long-term average reward in a single server queuing system, where the reward obtained for a job is a non-increasing, possibly nonlinear function of its sojourn time. The motivation behind this work comes from delay-sensitive applications, including quantum information processing and multimedia streaming. Although the goal of optimizing the total sojourn time is well-studied, optimizing a nonlinear function of the sojourn times remains unexplored to the best of our knowledge. We consider two arrival models - the first is a ‘burst arrival’ model, wherein all jobs arrive at the server at the same instant. We show that shortest job first (SJF) maximizes the average reward for any monotonic function of the sojourn times. In the second setting, jobs arrive according to some stochastic process with i.i.d. service requirements. This setting is significantly more challenging to analyze, and identifying an optimal discipline remains elusive. We introduce a new service discipline, shortest predicted sojourn time (SPST), and provide analytical guarantees under specific settings. Numerically, we demonstrate that SPST outperforms well-known disciplines across multiple settings.

Abstract Newly released robot programming tools have made it feasible for end‐users to program industrial robots by combining block‐based languages and lead‐through programming. To use these systems effectively, end‐users, who usually have limited or no programming experience, require training. To train users, tutoring systems are often used for block‐based programming—some even for lead‐through programming—but no tutorial system combines these two types of programming. We present CoBlox Interactive Tutorials (CITs), a novel tutoring approach that teaches how to use both the hardware and software components that comprise a typical end‐user robot programming environment. As users switch between the two programming styles, CITs provide them with extensive scaffolding, give users immediate feedback on missteps, and provide guidance on next steps. To evaluate CITs, we conducted a study with 79 industrial end‐users using a programming environment released by ABB Robotics that compares our approach to training with training videos, the most commonly used training in industry. This study, one of the largest to date on training professional end‐users, found that CIT‐trained users authored more correct programs in less time than video‐trained users. This shows that a tight integration of hardware and software concepts is crucial to training end‐users to program industrial robots.

Belief propagation (BP) is a classical algorithm that approximates the marginal distribution associated with a factor graph by passing messages between adjacent nodes in the graph. It gained popularity in the 1990’s as a powerful decoding algorithm for LDPC codes. In 2016, Renes introduced a belief propagation with quantum messages (BPQM) and described how it could be used to decode classical codes defined by tree factor graphs that are sent over the classical-quantum pure-state channel. In this work, we propose an extension of BPQM to general binary-input symmetric classical-quantum (BSCQ) channels based on the implementation of a symmetric "paired measurement". While this new paired-measurement BPQM (PMBPQM) approach is suboptimal in general, it provides a concrete BPQM decoder that can be implemented with local operations. Finally, we demonstrate that density evolution can be used to analyze the performance of PMBPQM on tree factor graphs. As an application, we compute noise thresholds of some LDPC codes with BPQM decoding for a class of BSCQ channels.

In recent times, there has been significant interest in collaborative robots where the tasks performed by a robot are non-repetitive and complex, and humans and robots share an overlapping workspace. In such a case, the robot controller must necessarily be safety-aware. In this paper, we propose a mechanism to evaluate a robot program and compute a safety score for each action that the robot is about to perform. To this end, we have implemented a code analyzer that examines the robot's Move instructions and assigns a safety score. A subjective logic based approach is used to compute the safety score for each instruction. We have evaluated the approach through ABB's RobotStudio®simulator. We simulate two scenarios: First, where two robots share a workspace and second, where a robot moves along a path with an obstacle. The simulations show that using our code analyzer and safety score formalism; it is possible to evaluate the application code for safety and enable avoidance of potentially unsafe behavior.

Software used to monitor and control operations within an automation system is defined using domain-specific languages. Latent errors in the control code, if left undetected, can lead to unexpected system failures compromising the safety and the security of the automation system. Traditional analysis techniques are insufficient to detect such errors as they do not cater specifically to the underlying domain-specific language. However, given the diversity of different automation domains, there is no standard platform for analysis of these languages. This paper proposes a generic static analysis framework for domain-specific languages used in the automation domain. The analysis approach exhaustively detects runtime errors in control code and ensures compliance to good programming practices. These runtime errors and coding violations are checked against abstract syntax trees and control flow graphs derived from the code. Data Flow Analysis (DFA), Abstract interpretation and pattern-based matching techniques are used to identify domain specific errors and coding violations for control languages.