Miracle-dz/UniFAR-FTUs huggingface dataset:
A Lite Microphone Array Beamforming Scheme with Maximum Signal-to-Noise Ratio Filter

Since space-domain information can be utilized, microphone array beamforming is often used to enhance the quality of the speech by suppressing directional disturbance. However, with the increasing number of microphone, the complexity would be increased. In this paper, a concise beamforming scheme using Maximum Signal-to-Noise Ratio (SNR) filter is proposed to reduce the beamforming complexity. The maximum SNR filter is implemented by using the estimated direction-of-arrival (DOA) of the speech source localization (SSL) and the solving method of independent vector analysis (IVA). Our experiments show that when compared with other widely-used algorithms, the proposed algorithm obtain higher gain of signal-to-interference and noise ratio (SINR).
Dice: Compiling Discrete Probabilistic Programs for Scalable Inference

Probabilistic programming languages (PPLs) are an expressive means of representing and reasoning about probabilistic models. The computational challenge of probabilistic inference remains the primary roadblock for applying PPLs in practice. Inference is fundamentally hard, so there is no one-size-fits all solution. In this work, we target scalable inference for an important class of probabilistic programs: those whose probability distributions are discrete. Discrete distributions are common in many fields, including text analysis, network verification, artificial intelligence, and graph analysis, but they prove to be challenging for existing PPLs. We develop a domain-specific probabilistic programming language called Dice that features a new approach to exact discrete probabilistic program inference. Dice exploits program structure in order to factorize inference, enabling us to perform exact inference on probabilistic programs with hundreds of thousands of random variables. Our key technical contribution is a new reduction from discrete probabilistic programs to weighted model counting (WMC). This reduction separates the structure of the distribution from its parameters, enabling logical reasoning tools to exploit that structure for probabilistic inference. We (1) show how to compositionally reduce Dice inference to WMC, (2) prove this compilation correct with respect to a denotational semantics, (3) empirically demonstrate the performance benefits over prior approaches, and (4) analyze the types of structure that allowDice to scale to large probabilistic programs.
DALock: Distribution Aware Password Throttling

Large-scale online password guessing attacks are wide-spread and continuously qualified as one of the top cyber-security risks. The common method for mitigating the risk of online cracking is to lock out the user after a fixed number ($K$) of consecutive incorrect login attempts. Selecting the value of $K$ induces a classic security-usability trade-off. When $K$ is too large a hacker can (quickly) break into a significant fraction of user accounts, but when $K$ is too low we will start to annoy honest users by locking them out after a few mistakes. Motivated by the observation that honest user mistakes typically look quite different than the password guesses of an online attacker, we introduce DALock a {\em distribution aware} password lockout mechanism to reduce user annoyance while minimizing user risk. As the name suggests, DALock is designed to be aware of the frequency and popularity of the password used for login attacks while standard throttling mechanisms (e.g., $K$-strikes) are oblivious to the password distribution. In particular, DALock maintains an extra "hit count" in addition to "strike count" for each user which is based on (estimates of) the cumulative probability of {\em all} login attempts for that particular account. We empirically evaluate DALock with an extensive battery of simulations using real world password datasets. In comparison with the traditional $K$-strikes mechanism we find that DALock offers a superior security/usability trade-off. For example, in one of our simulations we are able to reduce the success rate of an attacker to $0.05\%$ (compared to $1\%$ for the $10$-strikes mechanism) whilst simultaneously reducing the unwanted lockout rate for accounts that are not under attack to just $0.08\%$ (compared to $4\%$ for the $3$-strikes mechanism).
Achieving Online Regression Performance of LSTMs With Simple RNNs

Recurrent neural networks (RNNs) are widely used for online regression due to their ability to generalize nonlinear temporal dependencies. As an RNN model, long short-term memory networks (LSTMs) are commonly preferred in practice, as these networks are capable of learning long-term dependencies while avoiding the vanishing gradient problem. However, due to their large number of parameters, training LSTMs requires considerably longer training time compared to simple RNNs (SRNNs). In this article, we achieve the online regression performance of LSTMs with SRNNs efficiently. To this end, we introduce a first-order training algorithm with a linear time complexity in the number of parameters. We show that when SRNNs are trained with our algorithm, they provide very similar regression performance with the LSTMs in two to three times shorter training time. We provide strong theoretical analysis to support our experimental results by providing regret bounds on the convergence rate of our algorithm. Through an extensive set of experiments, we verify our theoretical work and demonstrate significant performance improvements of our algorithm with respect to LSTMs and the other state-of-the-art learning models.
Human-like general language processing

Using language makes human beings surpass animals in wisdom. To let machines understand, learn, and use language flexibly, we propose a human-like general language processing (HGLP) architecture, which contains sensorimotor, association, and cognitive systems. The HGLP network learns from easy to hard like a child, understands word meaning by coactivating multimodal neurons, comprehends and generates sentences by real-time constructing a virtual world model, and can express the whole thinking process verbally. HGLP rapidly learned 10+ different tasks including object recognition, sentence comprehension, imagination, attention control, query, inference, motion judgement, mixed arithmetic operation, digit tracing and writing, and human-like iterative thinking process guided by language. Language in the HGLP framework is not matching nor correlation statistics, but a script that can describe and control the imagination.
Deep Learning Guided Building Reconstruction from Satellite Imagery-derived Point Clouds

3D urban reconstruction of buildings from remotely sensed imagery has drawn significant attention during the past two decades. While aerial imagery and LiDAR provide higher resolution, satellite imagery is cheaper and more efficient to acquire for large scale need. However, the high, orbital altitude of satellite observation brings intrinsic challenges, like unpredictable atmospheric effect, multi view angles, significant radiometric differences due to the necessary multiple views, diverse land covers and urban structures in a scene, small base-height ratio or narrow field of view, all of which may degrade 3D reconstruction quality. To address these major challenges, we present a reliable and effective approach for building model reconstruction from the point clouds generated from multi-view satellite images. We utilize multiple types of primitive shapes to fit the input point cloud. Specifically, a deep-learning approach is adopted to distinguish the shape of building roofs in complex and yet noisy scenes. For points that belong to the same roof shape, a multi-cue, hierarchical RANSAC approach is proposed for efficient and reliable segmenting and reconstructing the building point cloud. Experimental results over four selected urban areas (0.34 to 2.04 sq km in size) demonstrate the proposed method can generate detailed roof structures under noisy data environments. The average successful rate for building shape recognition is 83.0%, while the overall completeness and correctness are over 70% with reference to ground truth created from airborne lidar. As the first effort to address the public need of large scale city model generation, the development is deployed as open source software.
Addressing Class-Imbalance Problem in Personalized Ranking

Pairwise ranking models have been widely used to address recommendation problems. The basic idea is to learn the rank of users' preferred items through separating items into \emph{positive} samples if user-item interactions exist, and \emph{negative} samples otherwise. Due to the limited number of observable interactions, pairwise ranking models face serious \emph{class-imbalance} issues. Our theoretical analysis shows that current sampling-based methods cause the vertex-level imbalance problem, which makes the norm of learned item embeddings towards infinite after a certain training iterations, and consequently results in vanishing gradient and affects the model inference results. We thus propose an efficient \emph{\underline{Vi}tal \underline{N}egative \underline{S}ampler} (VINS) to alleviate the class-imbalance issue for pairwise ranking model, in particular for deep learning models optimized by gradient methods. The core of VINS is a bias sampler with reject probability that will tend to accept a negative candidate with a larger degree weight than the given positive item. Evaluation results on several real datasets demonstrate that the proposed sampling method speeds up the training procedure 30\% to 50\% for ranking models ranging from shallow to deep, while maintaining and even improving the quality of ranking results in top-N item recommendation.
The stabilizing effect of gravity made simple

A new approach to vacuum decay in quantum field theory, based on a simple variational formulation in field space using a tunneling potential, is ideally suited to study the effects of gravity on such decays. The method allows to prove in new and simple ways many results, among others, that gravitational corrections tend to make Minkowski or Anti de Sitter false vacua more stable semiclassically or that higher barriers increase vacuum lifetime. The approach also offers a very clean picture of gravitational quenching of vacuum decay and its parametric dependence on the features of a potential and allows to study the BPS domain-walls between vacua in critical cases. Special attention is devoted to supersymmetric potentials and to the discussion of near-critical vacuum decays, for which it is shown how the new method can be usefully applied beyond the thin-wall approximation.
Asymmetric scale functions for t-digests

The t-digest is a data structure that can be queried for approximate quantiles, with greater accuracy near the minimum and maximum of the distribution. We develop a t-digest variant with accuracy asymmetric about the median, thereby making possible alternative trade-offs between computational resources and accuracy which may be of particular interest for distributions with significant skew. After establishing some theoretical properties of scale functions for t-digests, we show that a tangent line construction on the familiar scale functions preserves the crucial properties that allow t-digests to operate online and be mergeable. We conclude with an empirical study demonstrating the asymmetric variant preserves accuracy on one side of the distribution with a much smaller memory footprint.
Improving Accent Conversion with Reference Encoder and End-To-End Text-To-Speech

Accent conversion (AC) transforms a non-native speaker's accent into a native accent while maintaining the speaker's voice timbre. In this paper, we propose approaches to improving accent conversion applicability, as well as quality. First of all, we assume no reference speech is available at the conversion stage, and hence we employ an end-to-end text-to-speech system that is trained on native speech to generate native reference speech. To improve the quality and accent of the converted speech, we introduce reference encoders which make us capable of utilizing multi-source information. This is motivated by acoustic features extracted from native reference and linguistic information, which are complementary to conventional phonetic posteriorgrams (PPGs), so they can be concatenated as features to improve a baseline system based only on PPGs. Moreover, we optimize model architecture using GMM-based attention instead of windowed attention to elevate synthesized performance. Experimental results indicate when the proposed techniques are applied the integrated system significantly raises the scores of acoustic quality (30$\%$ relative increase in mean opinion score) and native accent (68$\%$ relative preference) while retaining the voice identity of the non-native speaker.
Reinforcement Learning for Caching with Space-Time Popularity Dynamics

With the tremendous growth of data traffic over wired and wireless networks along with the increasing number of rich-media applications, caching is envisioned to play a critical role in next-generation networks. To intelligently prefetch and store contents, a cache node should be able to learn what and when to cache. Considering the geographical and temporal content popularity dynamics, the limited available storage at cache nodes, as well as the interactive in uence of caching decisions in networked caching settings, developing effective caching policies is practically challenging. In response to these challenges, this chapter presents a versatile reinforcement learning based approach for near-optimal caching policy design, in both single-node and network caching settings under dynamic space-time popularities. The herein presented policies are complemented using a set of numerical tests, which showcase the merits of the presented approach relative to several standard caching policies.
Learning Joint Articulatory-Acoustic Representations with Normalizing Flows

The articulatory geometric configurations of the vocal tract and the acoustic properties of the resultant speech sound are considered to have a strong causal relationship. This paper aims at finding a joint latent representation between the articulatory and acoustic domain for vowel sounds via invertible neural network models, while simultaneously preserving the respective domain-specific features. Our model utilizes a convolutional autoencoder architecture and normalizing flow-based models to allow both forward and inverse mappings in a semi-supervised manner, between the mid-sagittal vocal tract geometry of a two degrees-of-freedom articulatory synthesizer with 1D acoustic wave model and the Mel-spectrogram representation of the synthesized speech sounds. Our approach achieves satisfactory performance in achieving both articulatory-to-acoustic as well as acoustic-to-articulatory mapping, thereby demonstrating our success in achieving a joint encoding of both the domains.
Free2Shard: Adaptive-adversary-resistant sharding via Dynamic Self Allocation

Propelled by the growth of large-scale blockchain deployments, much recent progress has been made in designing sharding protocols that achieve throughput scaling linearly in the number of nodes. However, existing protocols are not robust to an adversary adaptively corrupting a fixed fraction of nodes. In this paper, we propose Free2Shard -- a new architecture that achieves near-linear scaling while being secure against a fully adaptive adversary. The focal point of this architecture is a dynamic self-allocation algorithm that lets users allocate themselves to shards in response to adversarial action, without requiring a central or cryptographic proof. This architecture has several attractive features unusual for sharding protocols, including: (a) the ability to handle the regime of large number of shards (relative to the number of nodes); (b) heterogeneous shard demands; (c) requiring only a small minority to follow the self-allocation; (d) asynchronous shard rotation; (e) operation in a purely identity-free proof-of-work setting. The key technical contribution is a deep mathematical connection to the classical work of Blackwell in dynamic game theory.
Tree-Chain: A Fast Lightweight Consensus Algorithm for IoT Applications

Blockchain has received tremendous attention in non-monetary applications including the Internet of Things (IoT) due to its salient features including decentralization, security, auditability, and anonymity. Most conventional blockchains rely on computationally expensive validator selection and consensus algorithms, have limited throughput, and high transaction delays. In this paper, we propose tree-chain a scalable fast blockchain instantiation that introduces two levels of randomization among the validators: i) transaction level where the validator of each transaction is selected randomly based on the most significant characters of the hash function output (known as consensus code), and ii) blockchain level where validator is randomly allocated to a particular consensus code based on the hash of their public key. Tree-chain introduces parallel chain branches where each validator commits the corresponding transactions in a unique ledger.
Privileged Information Dropout in Reinforcement Learning

Using privileged information during training can improve the sample efficiency and performance of machine learning systems. This paradigm has been applied to reinforcement learning (RL), primarily in the form of distillation or auxiliary tasks, and less commonly in the form of augmenting the inputs of agents. In this work, we investigate Privileged Information Dropout (\pid) for achieving the latter which can be applied equally to value-based and policy-based RL algorithms. Within a simple partially-observed environment, we demonstrate that \pid outperforms alternatives for leveraging privileged information, including distillation and auxiliary tasks, and can successfully utilise different types of privileged information. Finally, we analyse its effect on the learned representations.
SDSS-IV MaNGA: spatially resolved star formation in barred galaxies

Bars inhabit the majority of local-Universe disc galaxies and may be important drivers of galaxy evolution through the redistribution of gas and angular momentum within discs. We investigate the star formation and gas properties of bars in galaxies spanning a wide range of masses, environments, and star formation rates using the Mapping Nearby Galaxies at APO galaxy survey. Using a robustly defined sample of 684 barred galaxies, we find that fractional (or scaled) bar length correlates with the host’s offset from the star formation main sequence. Considering the morphology of the Hα emission we separate barred galaxies into different categories, including barred, ringed, and central configurations, together with Hα detected at the ends of a bar. We find that only low-mass galaxies host star formation along their bars, and that this is located predominantly at the leading edge of the bar itself. Our results are supported by recent simulations of massive galaxies, which show that the position of star formation within a bar is regulated by a combination of shear forces, turbulence, and gas flows. We conclude that the physical properties of a bar are mostly governed by the existing stellar mass of the host galaxy, but that they also play an important role in the galaxy’s ongoing star formation.
Analysis of Diffusion Based Molecular Communication System with Multiple Transmitters

Due to the limited capabilities of a single bionanomachine, complicated tasks can be performed only with the co-operation of multiple bio-nanomachines. In this work, we consider a diffusion-based molecular communication system with a transmitter bio-nanomachine (TBN) communicating with a fully-absorbing spherical receiver bio-nanomachine (RBN) in the presence of other TBNs. The bits transmitted by each of the TBNs are considered as random in each time slot and different for each TBNs contrary to the past works in literature with deterministic bits, which are the same to all TBNs. The TBNs are modeled using a marked Poisson point process (PPP) with the location of TBNs as points of PPP, and the transmit bits as marks. In this paper, we derive the expected number of molecules observed at the RBN and the bit error probability of the system. We validate our analysis using numerical results and provide various design insights about the system.
A simple model for the macroscopic fluctuations of temporal networks

Many real-world social networks constantly change their global properties over time, such as the number of edges, size and density. While temporal and local properties of social networks have been extensively studied, the origin of their global fluctuations is not yet well understood. A network may grow or shrink if a) the total population of nodes, including resting ones, changes and/or b) the chance of two nodes being connected varies over time. Here, we develop a method that classifies the source of global fluctuations of temporal networks according to these two mechanisms. We propose a dynamic hidden-variable model to formally define the two dynamical classes, with which we show that the global fluctuations in a real-world dynamical system can be explained by either of the two simple mechanisms. Our findings will contribute to a better understanding of the origin of the time-varying nature of complex networks.
Lasso formulation of the shortest path problem

The shortest path problem is formulated as an l1-regularized regression problem, known as lasso. Based on this formulation, a connection is established between Dijkstra’s shortest path algorithm and the least angle regression (LARS) for the lasso problem. Specifically, the solution path of the lasso problem, obtained by varying the regularization parameter from infinity to zero (the regularization path), corresponds to shortest path trees that appear in the bi-directional Dijkstra algorithm.
Interplay of edge fracture and shear banding in complex fluids

We explore theoretically the interplay between shear banding and edge fracture in complex fluids, by performing a detailed simulation study within two constitutive models: the Johnson-Segalman model and the Giesekus model. We consider separately parameter regimes in which the underlying constitutive curve is monotonic and non-monotonic, such that the bulk flow (in the absence of any edge effects) is respectively homogeneous and shear banded. Phase diagrams of the levels of edge disturbance and of bulk (or quasi-bulk) shear banding are mapped as a function of the surface tension of the fluid-air interface, the wetting angle where this interface meets the walls of the flow cell, and the imposed shear rate. In particular, we explore in more detail the basic result recently announced in Hemingway et al. (2018): that precursors to edge fracture can induce quasi-bulk shear banding. We also appraise analytical predictions that shear banding can induce edge fracture, Skorski and Olmsted (2011). Although a study of remarkable early insight, that study made some strong assumptions about the nature of the "base state", which we examine using direct numerical simulation. The basic prediction that shear banding can cause edge fracture remains valid, but with qualitatively modified phase boundaries.