pwc-archive/papers-with-abstracts huggingface dataset:
Convolutional ProteinUnetLM competitive with long short-term memory-based protein secondary structure predictors
Authors: Krzysztof Kotowski, Piotr Fabian, Irena Roterman, Katarzyna Stapor
Date: 2022-11-30 00:00:00
Proceeding: Proteins 2022 11
Tasks: Multiple Sequence Alignment, Prediction, Protein Secondary Structure Prediction, UNET Segmentation

The protein secondary structure (SS) prediction plays an important role in the characterization of general protein structure and function. In recent years, a new generation of algorithms for SS prediction based on embeddings from protein language models (pLMs) is emerging. These algorithms reach state-of-the-art accuracy without the need for time-consuming multiple sequence alignment (MSA) calculations. Long short-term memory (LSTM)-based SPOT-1D-LM and NetSurfP-3.0 are the latest examples of such predictors. We present the ProteinUnetLM model using a convolutional Attention U-Net architecture that provides prediction quality and inference times at least as good as the best LSTM-based models for 8-class SS prediction (SS8). Additionally, we address the issue of the heavily imbalanced nature of the SS8 problem by extending the loss function with the Matthews correlation coefficient, and by proper assessment using previously introduced adjusted geometric mean (AGM) metric. ProteinUnetLM achieved better AGM and sequence overlap score than LSTM-based predictors, especially for the rare structures 310-helix (G), beta-bridge (B), and high curvature loop (S). It is also competitive on challenging datasets without homologs, free-modeling targets, and chameleon sequences. Moreover, ProteinUnetLM outperformed its previous MSA-based version ProteinUnet2, and provided better AGM than AlphaFold2 for 1/3 of proteins from the CASP14 dataset, proving its potential for making a significant step forward in the domain. To facilitate the usage of our solution by protein scientists, we provide an easy-to-use web interface under https://biolib.com/SUT/ProteinUnetLM/.
A Spatio-temporal Deep Learning Approach for Underwater Acoustic Signals Classification
Authors: Zakaria Alouani, Youssef Hmamouche, Btissam El Khamlichi, Amal El Fallah Seghrouchni
Date: 2022-11-24 00:00:00
Proceeding: AVSS 2022 11
Tasks: Audio Classification, Classification

Target recognition from underwater acoustic signals is a major challenge in surveillance systems, especially in military and defense fields. Deep learning models are increasingly used for the automatic classification of underwater signals, but many challenges remain due to the complexity of sound navigation and ranging networks, the noise present in the signals, and the difficulty of collecting large amounts of data for efficient training. In this paper, we propose two new architectures for underwater signal classification based on Spatio-temporal modeling. In experiments, evaluations on two real datasets show that the proposed approach achieves a classification accuracy of 98% which outperforms the state-of-the-art methods. In addition, the proposed end-to-end network is considerably faster than MFCC-based networks such as Yamnet and VGGish.
P4Neighbor: Efficient Link Failure Recovery With Programmable Switches
Authors: Jiarui Xu, Sihao Xie, and Jin Zhao
Date: 2021-01-11 00:00:00
Proceeding: IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2021 1

Programmable data plane hardware creates a possibility to solve network-related problems. Ensuring fault tolerance of link failures is a fundamental network issue. Link failure recovery mechanisms are widely used in traditional and software-defined networks. The proactive failure recovery mechanism usually requires a backup path to be installed in the switch in advance. When the link fails, the switch can quickly switch to the backup path to continue sending packets. However, storing a large number of backup paths consumes a lot of switch storage. In this article, we analyze why implementing traditional proactive failure recovery mechanism introduces huge switch storage overhead, and discuss the flexibility and limitations of the programmable data plane. Then, we present P4Neighbor, a proactive link failure recovery framework based on the programmable data plane. P4Neighbor encapsulates backup paths into the header of a packet when a link failed and leverages this information to achieve link failure recovery. By storing only the backup paths of the neighbor switches, P4Neighbor requires little switch storage to store backup paths. Besides, P4Neighbor also takes complex link failure situations into consideration, which makes the network's fault tolerance slightly increase. Experimental results show that compared with the traditional failure recovery mechanism, P4Neighbor achieves a reduction rate of 57.9%-84.5% in terms of stored switch entries. Meanwhile, P4Neighbor also has a higher failure recovery ratio than traditional proactive link failure recovery mechanisms.
Column sampling based discrete supervised hashing
Authors: Wang-Cheng Kang, Wu-Jun Li and Zhi-Hua Zhou
Date: 2016-02-21 00:00:00
Proceeding: AAAI 2016 2
Tasks: Image Retrieval, Retrieval, Semantic Similarity, Semantic Textual Similarity

By leveraging semantic (label) information, supervised hashing has demonstrated better accuracy than unsupervised hashing in many real applications. Because the hashing-code learning problem is essentially a discrete optimization problem which is hard to solve, most existing supervised hashing methods try to solve a relaxed continuous optimization problem by dropping the discrete constraints. However, these methods typically suffer from poor performance due to the errors caused by the relaxation. Some other methods try to directly solve the discrete optimization problem. However, they are typically time-consuming and unscalable. In this paper, we propose a novel method, called column sampling based discrete supervised hashing (COSDISH), to directly learn the discrete hashing code from semantic information. COSDISH is an iterative method, in each iteration of which several columns are sampled from the semantic similarity matrix and then the hashing code is decomposed into two parts which can be alternately optimized in a discrete way. Theoretical analysis shows that the learning (optimization) algorithm of COSDISH has a constant-approximation bound in each step of the alternating optimization procedure. Empirical results on datasets with semantic labels illustrate that COSDISH can outperform the state-of-the-art methods in real applications like image retrieval.
Convolutional Neural Networks for Text Hashing
Authors: Jiaming Xu, PengWang, Guanhua Tian, Bo Xu, Jun Zhao, Fangyuan Wang, HongWei Hao
Date: 2015-07-25 00:00:00
Proceeding: IJCAI 2015 7

Hashing, as a popular approximate nearest neighbor search, has been widely used for large-scale similarity search. Recently, a spectrum of machine learning methods are utilized to learn similarity-preserving binary codes. However, most of them directly encode the explicit features, keywords, which fail to preserve the accurate semantic similarities in binary code beyond keyword matching, especially on short texts. Here we propose a novel text hashing framework with convolutional neural networks. In particular, we first embed the keyword features into compact binary code with a locality preserving constraint. Meanwhile word features and position features are together fed into a convolutional network to learn the implicit features which are further incorporated with the explicit features to fit the pretrained binary code. Such base method can be successfully accomplished without any external tags/labels, and other three model variations are designed to integrate tags/labels. Experimental results show the superiority of our proposed approach over several state-of-the-art hashing methods when tested on one short text dataset as well as one normal text dataset.
Supervised hashing with latent factor models
Authors: Peichao Zhang, Wei zhang, Wu-Jun Li, and Minyi Guo
Date: 2014-07-03 00:00:00
Proceeding: SIGIR 2014 7

Due to its low storage cost and fast query speed, hashing has been widely adopted for approximate nearest neighbor search in large-scale datasets. Traditional hashing methods try to learn the hash codes in an unsupervised way where the metric (Euclidean) structure of the training data is preserved. Very recently, supervised hashing methods, which try to preserve the semantic structure constructed from the semantic labels of the training points, have exhibited higher accuracy than unsupervised methods. In this paper, we propose a novel supervised hashing method, called latent factor hashing(LFH), to learn similarity-preserving binary codes based on latent factor models. An algorithm with convergence guarantee is proposed to learn the parameters of LFH. Furthermore, a linear-time variant with stochastic learning is proposed for training LFH on large-scale datasets. Experimental results on two large datasets with semantic labels show that LFH can achieve superior accuracy than state-of-the-art methods with comparable training time.
Relative Timing Information and Orthology in Evolutionary Scenarios
Authors: David Schaller, Tom Hartmann, Manuel Lafond, Nicolas Wieseke, Peter F. Stadler, Marc Hellmuth
Date: 2022-12-05 00:00:00

Evolutionary scenarios describing the evolution of a family of genes within a collection of species comprise the mapping of the vertices of a gene tree $T$ to vertices and edges of a species tree $S$. The relative timing of the last common ancestors of two extant genes (leaves of $T$) and the last common ancestors of the two species (leaves of $S$) in which they reside is indicative of horizontal gene transfers (HGT) and ancient duplications. Orthologous gene pairs, on the other hand, require that their last common ancestors coincides with a corresponding speciation event. The relative timing information of gene and species divergences is captured by three colored graphs that have the extant genes as vertices and the species in which the genes are found as vertex colors: the equal-divergence-time (EDT) graph, the later-divergence-time (LDT) graph and the prior-divergence-time (PDT) graph, which together form an edge partition of the complete graph. Here we give a complete characterization in terms of informative and forbidden triples that can be read off the three graphs and provide a polynomial time algorithm for constructing an evolutionary scenario that explains the graphs, provided such a scenario exists. We show that every EDT graph is perfect. While the information about LDT and PDT graphs is necessary to recognize EDT graphs in polynomial-time for general scenarios, this extra information can be dropped in the HGT-free case. However, recognition of EDT graphs without knowledge of putative LDT and PDT graphs is NP-complete for general scenarios. In contrast, PDT graphs can be recognized in polynomial-time. We finally connect the EDT graph to the alternative definitions of orthology that have been proposed for scenarios with horizontal gene transfer. With one exception, the corresponding graphs are shown to be colored cographs.
Two-Player Incomplete Games of Resilient Multiagent Systems
Authors: Yurid Nugraha, Tomohisa Hayakawa, Hideaki Ishii, Ahmet Cetinkaya, Quanyan Zhu
Date: 2022-12-03 00:00:00
Tasks: Vocal Bursts Valence Prediction

Evolution of agents' dynamics of multiagent systems under consensus protocol in the face of jamming attacks is discussed, where centralized parties are able to influence the control signals of the agents. In this paper we focus on a game-theoretical approach of multiagent systems where the players have incomplete information on their opponents' strength. We consider repeated games with both simultaneous and sequential player actions where players update their beliefs of each other over time. The effect of the players' optimal strategies according to Bayesian Nash Equilibrium and Perfect Bayesian Equilibrium on agents' consensus is examined. It is shown that an attacker with incomplete knowledge may fail to prevent consensus despite having sufficient resources to do so.
Real-time Bidding Strategy in Display Advertising: An Empirical Analysis
Authors: Mengjuan Liu, Zhengning Hu, Zhi Lai, Daiwei Zheng, Xuyun Nie
Date: 2022-11-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Bidding strategies that help advertisers determine bidding prices are receiving increasing attention as more and more ad impressions are sold through real-time bidding systems. This paper first describes the problem and challenges of optimizing bidding strategies for individual advertisers in real-time bidding display advertising. Then, several representative bidding strategies are introduced, especially the research advances and challenges of reinforcement learning-based bidding strategies. Further, we quantitatively evaluate the performance of several representative bidding strategies on the iPinYou dataset. Specifically, we examine the effects of state, action, and reward function on the performance of reinforcement learning-based bidding strategies. Finally, we summarize the general steps for optimizing bidding strategies using reinforcement learning algorithms and present our suggestions.
Welfare and Fairness in Multi-objective Reinforcement Learning
Authors: Zimeng Fan, Nianli Peng, Muhang Tian, Brandon Fain
Date: 2022-11-30 00:00:00
Tasks: Fairness, Multi-Objective Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study fair multi-objective reinforcement learning in which an agent must learn a policy that simultaneously achieves high reward on multiple dimensions of a vector-valued reward. Motivated by the fair resource allocation literature, we model this as an expected welfare maximization problem, for some nonlinear fair welfare function of the vector of long-term cumulative rewards. One canonical example of such a function is the Nash Social Welfare, or geometric mean, the log transform of which is also known as the Proportional Fairness objective. We show that even approximately optimal optimization of the expected Nash Social Welfare is computationally intractable even in the tabular case. Nevertheless, we provide a novel adaptation of Q-learning that combines nonlinear scalarized learning updates and non-stationary action selection to learn effective policies for optimizing nonlinear welfare functions. We show that our algorithm is provably convergent, and we demonstrate experimentally that our approach outperforms techniques based on linear scalarization, mixtures of optimal linear scalarizations, or stationary action selection for the Nash Social Welfare Objective.
Band Relevance Factor (BRF): a novel automatic frequency band selection method based on vibration analysis for rotating machinery
Authors: Lucas Costa Brito, Gian Antonio Susto, Jorge Nei Brito, Marcus Antonio Viana Duarte
Date: 2022-12-04 00:00:00

The monitoring of rotating machinery has now become a fundamental activity in the industry, given the high criticality in production processes. Extracting useful information from relevant signals is a key factor for effective monitoring: studies in the areas of Informative Frequency Band selection (IFB) and Feature Extraction/Selection have demonstrated to be effective approaches. However, in general, typical methods in such areas focuses on identifying bands where impulsive excitations are present or on analyzing the relevance of the features after its signal extraction: both approaches lack in terms of procedure automation and efficiency. Typically, the approaches presented in the literature fail to identify frequencies relevant for the vibration analysis of a rotating machinery; moreover, with such approaches features can be extracted from irrelevant bands, leading to additional complexity in the analysis. To overcome such problems, the present study proposes a new approach called Band Relevance Factor (BRF). BRF aims to perform an automatic selection of all relevant frequency bands for a vibration analysis of a rotating machine based on spectral entropy. The results are presented through a relevance ranking and can be visually analyzed through a heatmap. The effectiveness of the approach is validated in a synthetically created dataset and two real dataset, showing that the BRF is able to identify the bands that present relevant information for the analysis of rotating machinery.
SINR Coverage Enhancement of 6G UAV-Assisted Networks Deploying IRS
Authors: Mobasshir Mahbub, Raed M. Shubair
Date: 2022-12-03 00:00:00

The objective of the work is to enhance the signal-to-interference-plus-noise ratio (SINR) coverage probability in UAV-assisted 6G wireless networks. Therefore, the work analyzed and compared the coverage probability of conventional and IRS-assisted UAV communications models. The research obtained that the employment of IRS in UAV-assisted wireless networks enhances the SINR coverage probability significantly. Moreover, the installment of IRS in UAV networks reduces the energy consumption of the network.
Tragic Talkers: A Shakespearean Sound- and Light-Field Dataset for Audio-Visual Machine Learning Research
Authors: Davide Berghi, Marco Volino, Philip J. B. Jackson
Date: 2022-12-04 00:00:00

3D audio-visual production aims to deliver immersive and interactive experiences to the consumer. Yet, faithfully reproducing real-world 3D scenes remains a challenging task. This is partly due to the lack of available datasets enabling audio-visual research in this direction. In most of the existing multi-view datasets, the accompanying audio is neglected. Similarly, datasets for spatial audio research primarily offer unimodal content, and when visual data is included, the quality is far from meeting the standard production needs. We present "Tragic Talkers", an audio-visual dataset consisting of excerpts from the "Romeo and Juliet" drama captured with microphone arrays and multiple co-located cameras for light-field video. Tragic Talkers provides ideal content for object-based media (OBM) production. It is designed to cover various conventional talking scenarios, such as monologues, two-people conversations, and interactions with considerable movement and occlusion, yielding 30 sequences captured from a total of 22 different points of view and two 16-element microphone arrays. Additionally, we provide voice activity labels, 2D face bounding boxes for each camera view, 2D pose detection keypoints, 3D tracking data of the mouth of the actors, and dialogue transcriptions. We believe the community will benefit from this dataset as it can assist multidisciplinary research. Possible uses of the dataset are discussed.
Objective Assessment of Spatial Audio Quality using Directional Loudness Maps
Authors: Pablo M. Delgado, Jürgen Herre
Date: 2022-12-02 00:00:00
Tasks: Bandwidth Extension

This work introduces a feature extracted from stereophonic/binaural audio signals aiming to represent a measure of perceived quality degradation in processed spatial auditory scenes. The feature extraction technique is based on a simplified stereo signal model considering auditory events positioned towards a given direction in the stereo field using amplitude panning (AP) techniques. We decompose the stereo signal into a set of directional signals for given AP values in the Short-Time Fourier Transform domain and calculate their overall loudness to form a directional loudness representation or maps. Then, we compare directional loudness maps of a reference signal and a deteriorated version to derive a distortion measure aiming to describe the associated perceived degradation scores reported in listening tests. The measure is then tested on an extensive listening test database with stereo signals processed by state-of-the-art perceptual audio codecs using non waveform-preserving techniques such as bandwidth extension and joint stereo coding, known for presenting a challenge to existing quality predictors. Results suggest that the derived distortion measure can be incorporated as an extension to existing automated perceptual quality assessment algorithms for improving prediction on spatially coded audio signals.
Connected Cruise and Traffic Control for Pairs of Connected Automated Vehicles
Authors: Sicong Guo, Gabor Orosz, Tamas G. Molnar
Date: 2022-12-04 00:00:00

This paper considers mixed traffic consisting of connected automated vehicles equipped with vehicle-to-everything (V2X) connectivity and human-driven vehicles. A control strategy is proposed for communicating pairs of connected automated vehicles, where the two vehicles regulate their longitudinal motion by responding to each other, and, at the same time, stabilize the human-driven traffic between them. Stability analysis is conducted to find stabilizing controllers, and simulations are used to show the efficacy of the proposed approach. The impact of the penetration of connectivity and automation on the string stability of traffic is quantified. It is shown that, even with moderate penetration, connected automated vehicle pairs executing the proposed controllers achieve significant benefits compared to when these vehicles are disconnected and controlled independently.
LMEC: Learnable Multiplicative Absolute Position Embedding Based Conformer for Speech Recognition
Authors: Yuguang Yang, Yu Pan, JingJing Yin, Heng Lu
Date: 2022-12-05 00:00:00
Tasks: Position, speech-recognition, Speech Recognition

This paper proposes a Learnable Multiplicative absolute position Embedding based Conformer (LMEC). It contains a kernelized linear attention (LA) module called LMLA to solve the time-consuming problem for long sequence speech recognition as well as an alternative to the FFN structure. First, the ELU function is adopted as the kernel function of our proposed LA module. Second, we propose a novel Learnable Multiplicative Absolute Position Embedding (LM-APE) based re-weighting mechanism that can reduce the well-known quadratic temporal-space complexity of softmax self-attention. Third, we use Gated Linear Units (GLU) to substitute the Feed Forward Network (FFN) for better performance. Extensive experiments have been conducted on the public LibriSpeech datasets. Compared to the Conformer model with cosFormer style linear attention, our proposed method can achieve up to 0.63% word-error-rate improvement on test-other and improve the inference speed by up to 13% (left product) and 33% (right product) on the LA module.