Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling using Unsupervised Topic Models and Concept Hierarchies

Statistical topic models provide a general data-driven fra mework for automated discovery of highlevel knowledge from large collections of text documents. W hile topic models can potentially discover a broad range of themes in a data set, the interpretabil ity of the learned topics is not always ideal. Human-defined concepts, on the other hand, tend to be s emantically richer due to careful selection of words to define concepts but they tend not to cover t h themes in a data set exhaustively. In this paper, we propose a probabilistic framework to combi ne a hierarchy of human-defined semantic concepts with statistical topic models to seek the be st of both worlds. Experimental results using two different sources of concept hierarchies and two c olle tions of text documents indicate that this combination leads to systematic improvements in t he quality of the associated language models as well as enabling new techniques for inferring and v isualizing the semantics of a document.
Procedural Crowd Generation for Semantically Augmented Virtual Cities

Authoring realistic behaviors to populate a large virtual city can be a cumbersome, time-consuming and error-prone task. Believable crowds require the effort of storytellers and programming experts working together for long periods of time. In this work, we present a new framework to allow users to generate populated environments in an easier and faster way, by relying on the use of procedural techniques. Our framework consists of the procedural generation of semantically-augmented virtual cities to drive the procedural generation and simulation of crowds. The main novelty lies in the generation of agendas for each individual inhabitant (alone or as part of a family) by using a rule-based grammar that combines city semantics with the autonomous persons' characteristics. Real-world data can be used to accommodate the generation of a virtual population, thus enabling the recreation of more realistic scenarios. Users can author a new population or city by editing rule files with the flexibility of re-using, combining or extending the rules of previous populations. The results show how logical and consistent behaviors can be easily generated for a large crowd providing a good starting point to bring virtual cities to life.
Citation counts for research evaluation: standards of good practice for analyzing bibliometric data and presenting and interpreting results

With the ready accessibility of bibliometric data and the availability of ready-to-use tools for generating bibliometric indicators for evaluation purposes, there is the danger of inappropri- ate use. Here we present standards of good practice for analyzing bibliometric data and presenting and interpreting the results. Comparisons drawn between research groups as to research perfor- mance are valid only if (1) the scientific impact of the research groups or their publications are looked at by using box plots, Lorenz curves, and Gini coefficients to represent distribution characteristics of data (in other words, going beyond the usual arithmetic mean value), (2) different reference stan- dards are used to assess the impact of research groups, and the appropriateness of the reference stan- dards undergoes critical examination, and (3) statistical analyses comparing citation counts take into consideration that citations are a function of many influencing factors besides scientific quality.
S UPPLEMENTARY FOR PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos

The 3D sparse feature volume construction is detailed in Fig. 1. Feature representations of the key frames in a fragment obtained using a 2D neural network (MnasNet [3]) are back-projected to the 3D space through camera poses. The 3D space is divided into a set of voxels, where the feature representation of each voxel is an average of the representations of its observed pixels in different key frames. We build the 3D sparse feature volumes in a coarse-tofine pyramid manner, following the network hierarchy when computing image representations, where the 3D space is gradually divided into finer voxels. In each pyramid, we perform occupancy classification for each voxel. Only occupied voxels will be further processed at the next pyramid level. At the finest level, we end up having a set of occupied voxels, which are used to detect planes.
Secure Transmission With Multiple Antennas - Part II: The MIMOME Wiretap Channel

The capacity of the Gaussian wiretap channel model is analyzed when there are multiple antennas at the sender, intended receiver and eavesdropper. The associated channel matrices are fixed and known to all the terminals. A computable characterization of the secrecy capacity is established as the saddle point solution to a minimax problem. The converse is based on a Sato-type argument used in other broadcast settings, and the coding theorem is based on Gaussian wiretap codebooks. At high signal-to-noise ratio (SNR), the secrecy capacity is shown to be attained by simultaneously diagonalizing the channel matrices via the generalized singular value decomposition, and independently coding across the resulting parallel channels. The associated capacity is expressed in terms of the corresponding generalized singular values. It is shown that a semi-blind "masked" multi-input multi-output (MIMO) transmission strategy that sends information along directions in which there is gain to the intended receiver, and synthetic noise along directions in which there is not, can be arbitrarily far from capacity in this regime. Necessary and sufficient conditions for the secrecy capacity to be zero are provided, which simplify in the limit of many antennas when the entries of the channel matrices are independent and identically distributed. The resulting scaling laws establish that to prevent secure communication, the eavesdropper needs three times as many antennas as the sender and intended receiver have jointly, and that the optimum division of antennas between sender and intended receiver is in the ratio of 2:1.
Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in SAGIN

In this article, we investigate a computing task scheduling problem in space-air-ground integrated network (SAGIN) for delay-oriented Internet of Things (IoT) services. In the considered scenario, an unmanned aerial vehicle (UAV) collects computing tasks from IoT devices and then makes online offloading decisions, in which the tasks can be processed at the UAV or offloaded to the nearby base station or the remote satellite. Our objective is to design a task scheduling policy that minimizes offloading and computing delay of all tasks given the UAV energy capacity constraint. To this end, we first formulate the online scheduling problem as an energy-constrained Markov decision process (MDP). Then, considering the task arrival dynamics, we develop a novel deep risk-sensitive reinforcement learning algorithm. Specifically, the algorithm evaluates the risk, which measures the energy consumption that exceeds the constraint, for each state and searches the optimal parameter weighing the minimization of delay and risk while learning the optimal policy. Extensive simulation results demonstrate that the proposed algorithm can reduce the task processing delay by up to 30% compared to probabilistic configuration methods while satisfying the UAV energy capacity constraint.
Learning Compositional Representations for Few-Shot Recognition Supplementary Material

This supplementary material provides additional experimental results and details. We begin by evaluating the effect of the number of attributes used for training on the model’s few-shot performance in Section 1. Next, we explore the effect of plugging our compositional representations into existing few-shot learning methods, such as Prototypical Networks [4] and Matching Networks [5], as well as the effect of data augmentation on these methods in Section 2. We then provide a qualitative analysis of the learned representation using Network Dissection [7] in Section 3. In Section 4 we demonstrate how to apply our proposed regularization to deeper network architectures. Finally, we visualize the attributes used in our experiments on ImageNet together with their hierarchical structure in Sections 5, and provide additional implementation details in Section 6.
Relational inductive biases , deep learning , and graph networks ∗

We’ve seen a lot of progress in the research, but there is still a huge gap between humans and our best models. The authors claim that this is due to the lack of combinatorial generalisation, i.e. constructing new inferences, predictions, and behaviours from known building blocks. Humans are good at representing structure and reasoning about relations. We are also good at finding hierarchies and thinking about things as something composite of simpler blocks. Some time ago people thought the relational approach was the Thing. Recently we have seen a flood of DL research from the folks who do not care about this old Thing. There is a lot of critiques recently which shows that smart terminators are still too far away. What if we take both of these two worlds and get an amazing generalisation power of the relational algebra along with very powerful function approximators and all the recent advances in machine learning research.
D2D-U: Device-to-Device Communications in Unlicensed Bands for 5G System

Device-to-device (D2D) communication, which enables direct communication between nearby mobile devices, is an attractive add-on component to improve spectrum efficiency and user experience by reusing licensed cellular spectrum in 5G system. In this paper, we propose to enable D2D communication in unlicensed spectrum (D2D-U) as an underlay of the uplink LTE network for further booming the network capacity. A sensing-based protocol is designed to support the unlicensed channel access for both LTE and D2D users. We further investigate the subchannel allocation problem to maximize the sum rate of LTE and D2D users while considering their interference to the existing Wi-Fi systems. Specifically, we formulate the subchannel allocation as a many-to-many matching problem with externalities, and develop an iterative user-subchannel swap algorithm. Analytical and simulation results show that the proposed D2D-U scheme can significantly improve the system sum rate.
RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding Supplementary Material

In this supplementary, we provide more details of our method, extra analysis experiment results and visualizations. In Sec. B, we describe more details of the deep predictive model, including its network architecture, losses and its training process as well as more explanation of the input data transformations. In Sec. C, we provide more analysis experiment results on model latency, effects of the entropy encoder choices and effects of the context sizes. In Sec. D, we apply our method to compress lidar data attributes beyond the range values. Finally, in Sec. E, we provide more visualizations of our model’s predictions.
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games

This work studies an algorithm, which we call magnetic mirror descent, that is inspired by mirror descent and the non-Euclidean proximal gradient algorithm. Our contribution is demonstrating the virtues of magnetic mirror descent as both an equilibrium solver and as an approach to reinforcement learning in two-player zero-sum games. These virtues include: 1) Being the first quantal response equilibria solver to achieve linear convergence for extensive-form games with first order feedback; 2) Being the first standard reinforcement learning algorithm to achieve empirically competitive results with CFR in tabular settings; 3) Achieving favorable performance in 3x3 Dark Hex and Phantom Tic-Tac-Toe as a self-play deep reinforcement learning algorithm.
Rate-Optimal Streaming Codes for Channels With Burst and Random Erasures

In this paper, we design erasure-correcting codes for channels with burst and random erasures, when a strict decoding delay constraint is in place. We consider the sliding-window-based packet erasure model proposed by Badr et al., where any time-window of width <inline-formula> <tex-math notation="LaTeX">$w$ </tex-math></inline-formula> contains either up to <inline-formula> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> random erasures or an erasure burst of length at most <inline-formula> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula>. One needs to recover any erased packet with a strict decoding delay deadline of <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula>, where erasures are as per the channel model. Presently existing rate-optimal constructions in the literature require, in general, a field-size which grows exponential in <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula>, as long as <inline-formula> <tex-math notation="LaTeX">$\frac {a}{\tau }$ </tex-math></inline-formula> remains a constant. In this work, we present a new rate-optimal code construction covering all channel and delay parameters, which requires an <inline-formula> <tex-math notation="LaTeX">$O(\tau ^{2})$ </tex-math></inline-formula> field-size. As a special case, when <inline-formula> <tex-math notation="LaTeX">$(b-a)=1$ </tex-math></inline-formula>, we have a field-size linear in <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula>. We also present two other constructions having linear field-size, under certain constraints on channel and decoding delay parameters. As a corollary, we obtain low field-size, rate-optimal convolutional codes for any given column distance and column span. Simulations indicate that the newly proposed streaming code constructions offer lower packet-loss probabilities compared to existing schemes, for selected instances of Gilbert-Elliott and Fritchman channels.
A Survey of FPGA Based Neural Network Accelerator

Recent researches on neural network have shown significant advantage in machine learning over traditional algorithms based on handcrafted features and models. Neural network is now widely adopted in regions like image, speech and video recognition. But the high computation and storage complexity of neural network inference poses great difficulty on its application. CPU platforms are hard to offer enough computation capacity. GPU platforms are the first choice for neural network process because of its high computation capacity and easy to use development frameworks. On the other hand, FPGA-based neural network inference accelerator is becoming a research topic. With specifically designed hardware, FPGA is the next possible solution to surpass GPU in speed and energy efficiency. Various FPGA-based accelerator designs have been proposed with software and hardware optimization techniques to achieve high speed and energy efficiency. In this paper, we give an overview of previous work on neural network inference accelerators based on FPGA and summarize the main techniques used. An investigation from software to hardware, from circuit level to system level is carried out to complete analysis of FPGA-based neural network inference accelerator design and serves as a guide to future work.
Avoiding Communication in Primal and Dual Block Coordinate Descent Methods by Aditya Devarakonda Research Project

Primal and dual block coordinate descent methods are iterative methods for solving regularized and unregularized optimization problems. Distributed-memory parallel implementations of these methods have become popular in analyzing large machine learning datasets. However, existing implementations communicate at every iteration which, on modern data center and supercomputing architectures, often dominates the cost of floating-point computation. Recent results on communication-avoiding Krylov subspace methods suggest that large speedups are possible by re-organizing iterative algorithms to avoid communication. We show how applying similar algorithmic transformations can lead to primal and dual block coordinate descent methods that only communicate every s iterations–where s is a tuning parameter– instead of every iteration for the regularized least-squares problem. We derive communicationavoiding variants of the primal and dual block coordinate descent methods which reduce the number of synchronizations by a factor of s on distributed-memory parallel machines without altering the convergence rate. Our communication-avoiding algorithms attain modeled strong scaling speedups of 14× and 165× on a modern supercomputer using MPI and Apache Spark, respectively. Our algorithms attain modeled weak scaling speedups of 12× and 396× on the same machine using MPI and Apache Spark, respectively.
Avatars and computer-mediated communication: a review of the definitions, uses, and effects of digital representations

Avatars are growing in popularity and present in many interfaces used for computer-mediated communication (CMC) including social media, e-commerce, and education. Communication researchers have been investigating avatars for over twenty years, and an examination of this literature reveals similarities but also notable discrepancies in conceptual definitions. The goal of this review is to provide a general overview of current debates, methodological approaches, and trends in findings. Our review synthesizes previous research in four areas. First, we examine how scholars have conceptualized the term “avatar,” identify similarities and differences across these definitions, and recommend that scholars use the term consistently. Next, we review theoretical perspectives relevant to avatar perception (e.g., the computers as social actors framework). Then, we examine avatar characteristics that communicators use to discern the humanity and social potential of an avatar (anthropomorphism, form realism, behavioral realism, and perceived agency) and discuss implications for at-tributions and communication outcomes. We also review findings on the social categorization of avatars, such as when people apply categories like sex, gender, race, and ethnicity to their evaluations of digital representations. Finally, we examine research on avatar selection and design relevant to communication outcomes. Here, we review both motivations in CMC contexts (such as self-presentation and identity expression) and potential effects (e.g., persuasion). We conclude with a discussion of future directions for avatar research and propose that communication researchers consider avatars not just as a topic of study, but also as a tool for testing theories and understanding critical elements of human communication. Avatar-mediated environments provide researchers with a number of advantageous technological affordances that can enable manipulations that may be difficult or inadvisable to execute in natural environments. We conclude by discussing the use of avatar research to extend communication theory and our understanding of communication processes.
Personalized Sports Video Customization Based on Multi-modal Analysis for Mobile Devices

In this paper we have implemented a mobile personalized sports video customization system for mobile users by using a novel approach. The system is based on the B/S architecture, with this architecture, the whole system can be divided into two parts: a friendly client browser on smart phones and an intelligent multimedia analysis server. For the client browser, a friend UI is designed for mobile users to customize their favorite video clips. For the multimedia server, with the analysis of video content as well as user preference, semantics-specific video clips can be tailored to the particular users to effectively improve their visual enjoyment. The whole approach of the multimedia analysis is implemented in a two steps. First, sports video content is automatically indexed by detailed textual descriptions, which are derived from latent semantic analysis of web-casting text, based on the semantic annotation, a video database is constructed. And then video content and user preference are collaborated into event importance computation mechanism, after that the server can tailor the optimal set of video clips to the particular user. Particularly, we adopt basketball and football games as our initial sports genres because they not only widely adopted study bed but also globally popular sports, which possess great values in both research and application. Both quantitative and qualitative experiments validate the effectiveness of our system.
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection

This work is inspired by recent advances in hierarchical re-inforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efﬁciency from heuristic-based subgoal selection, experience replay (Lin 1993; Andrychowicz et al. 2017), and task-based curriculum learning (Bengio et al. 2009; Zaremba and Sutskever 2014). We propose a new method to integrate HRL, experience replay and effective subgoal selection through an implicit curriculum design based on human expertise to support sample-efﬁcient learning and enhance interpretability of the agent’s behavior. Human expertise remains indispensable in many areas such as medicine (Buch, Ahmed, and Maruthappu 2018) and law (Cath 2018), where interpretability, explain-ability and transparency are crucial in the decision making process, for ethical and legal reasons. Our method simpli-ﬁes the complex task sets for achieving the overall objectives by decomposing them into subgoals at different levels of abstraction. Incorporating relevant subjective knowledge also signiﬁcantly reduces the computational resources spent in exploration for RL, especially in high speed, changing, and complex environments where the transition dynamics cannot be effectively learned and modelled in a short time. Experimental results in two StarCraft II (SC2) (Vinyals et al. 2017) minigames demonstrate that our method can achieve better sample efﬁciency than ﬂat and end-to-end RL methods, and provides an effective method for explaining the agent’s performance.
Graph-based security and privacy analytics via collective classification

x CHAPTER 1. OVERVIEW . 1 1.1 Collective Classification . 1 1.1.1 RW-based methods . 1 1.1.2 LBP-based methods . 3 1.2 Modeling Security/Privacy Problems As Collective Classification . 4 1.3 Pros and Cons of Existing Collective Classification Methods . 7 1.4 Organization of The Dissertation . 8 CHAPTER 2. COLLECTIVE CLASSIFICATION UNDER A LOCAL RULE-BASED FRAMEWORK . 10 2.1 Our Local Rule-based Framework . 10 2.1.1 Additive Local Rule of RW-based Methods . 11 2.1.2 Multiplicative Local Rule of LBP-based Methods . 11 2.1.3 Comparing Additive Local Rule with Multiplicative Local Rule . 12 CHAPTER 3. COLLECTIVE CLASSIFICATION ON UNDIRECTED GRAPHS FOR SYBIL DETECTION IN SOCIAL NETWORKS . 13 3.
Approximate Message Passing for Bilinear Models

Approach: We take a Bayesian approach to the inference problems (in particular, posterior estimation) that revolve around the bilinear model (1). In particular, we leverage the approximate message passin g (AMP) framework of [2], [3] and extend it to the bilinear domain. Compared to Bayesian approaches that rely on Gibbs sampling methods or variational inference, the AMP framework allows us to fully exploit theblessings-of-dimensionality (e.g., the asymptotic normality and concentration-of-measures) to achieve salient advantages in com putation and estimation accuracy. Our “turbo AMP” framework also allows us to characterize the impact of our message scheduling using extrinsic information transfer (EXIT) charts, originally developed to predict the convergence of turbo decoding.