The 12-lead electrocardiogram (ECG) measures the electrical activity of the heart for physicians to use in diagnosing cardiac disorders. This paper investigates the multi-label, multi-class classification of ECG records into one or more of 27 possible medical diagnoses. Our multi-step approach uses conventional physiological algorithms for segmentation of heartbeats from the baseline signals. We stack a heartbeat autoencoder over heartbeat windows to make embeddings, then we encode this sequence of embeddings to make an ECG embedding which we then classify on. We utilize the public dataset of 43,101 available ECG records provided by the PhysioNet/CinC 2020 challenge, performing repeated random subsampling and splitting the available records into 80% training, 10% validation, and 10% test splits, 20 times. We attain a mean test split challenge score of 0.248 with an overall macro F1score of 0.260 across the 27 labels.

One problem when studying how to find and fix syntax errors is how to get natural and representative examples of syntax errors. Most syntax error datasets are not free, open, and public, or they are extracted from novice programmers and do not represent syntax errors that the general population of developers would make. Programmers of all skill levels post questions and answers to Stack Overflow which may contain snippets of source code along with corresponding text and tags. Many snippets do not parse, thus they are ripe for forming a corpus of syntax errors and corrections. Our primary contribution is an approach for extracting natural syntax errors and their corresponding human made fixes to help syntax error research. A Python abstract syntax tree parser is used to determine preliminary errors and corrections on code blocks extracted from the SOTorrent data set. We further analyzed our code by executing the corrections in a Python interpreter. We applied our methodology to produce a public data set of 62,965 Python Stack Overflow code snippets with corresponding tags, errors, and stack traces. We found that errors made by Stack Overflow users do not match errors made by student developers or random mutations, implying there is a serious representativeness risk within the field. Finally we share our dataset openly so that future researchers can re-use and extend our syntax errors and fixes.

Communication over channels with state is a classical problem extensively studied. Herein, the problem statement is extended to consider a multihop channel where the channel state information in hops, unavailable to neither the source, the destination, nor the relay(s), is to be estimated at the destination, along with reliable information transmission. Each relay in the multihop channel forwards the source's transmitted signal, estimates the preceding hops' channel states, and assists in the destination's reliable decoding of the transmitted message and reconstruction of the channel states. For a two-hop channel with independent states over the two hops, it is shown that a decode-(indirectly)-compress-and-forward strategy achieves the capacity-distortion function.

The notion of orbit-entropy cone is introduced. Specifically, orbit-entropy cone equation is the projection of equation induced by G, where equation is the closure of entropy region for n random variables and G is a permutation group over {0; 1;...; n-1}. For symmetric group Sn(with arbitrary n) and cyclic group Cn(with n ≤ 5), the associated orbit-entropy cones are shown to be characterized by the Shannon type inequalities. Moreover, the extremal pairwise relationship between orbit-entropies is determined completely for partitioned symmetric groups and partially for cyclic groups.

An explicit characterization of the capacity region of the general network coding problem is one of the best known open problems in information theory. A simple set of bounds that is often used in the literature to show that certain rate tuples are infeasible are based on the graph-theoretic notion of cut. The standard cut-set bounds, however, are known to be loose in general when there are multiple messages to be communicated in the network. This paper focuses on broadcast networks, for which the standard cut-set bounds are closely related to union as a specific set operation to combine different simple cuts of the network. A new set of explicit network coding bounds, which combine different simple cuts of the network via a variety of set operations (not just the union), are established via their connections to extremal inequalities for submodular functions. The tightness of these bounds are demonstrated via applications to combination networks.

This paper provides a polyhedral description of the symmetrical latency capacity region of broadcast channels. The converse result is established via the recently proposed generalized cut-set bounds for broadcast networks. The achievability result is proved by considering a “pairwise exchange” scheme and successive encoding.

In this paper we prove a new capacity upper bound for a class of relay channels known as Relay-With-Delay channels. The proposed bound is general and holds for any arbitrary amount of delay. We have shown that the previously introduced bounds can be considered as special cases of our proposed upper bound. The new introduced bound is, in some cases, tighter than the previously established bounds. It is also notable that the proposed bound coincides with the previously known capacity of ¿Relay-With-Delay¿ channels.