pwc-archive/papers-with-abstracts huggingface dataset:
Query Processing at Snapchat: How we Handle Query Completion, Suggestion and Localization
Authors: Vasyl Pihur
Date: 2022-11-04 00:00:00

From the Publisher:Software is a commodity being sold across diverse language and cultural groups, whether in the commercial marketplace, or as customized applications. Developers must structure their applications so that they can be readily and cheaply localized for sale in this range of markets. Obvious differences such as scripts and languages must be understood as well as a range of more subtle cultural conventions. Further topics covered include: the overall architecture for internationalized products and an outline of an internationalization API; the use of computational linguistics methods; quality assurance, testing and documentation. Appendices contain summaries of the facilities available for localization on major platforms, characteristics of European languages, commercial tools and further reading. The book is aimed at small and medium sized software producers, and the IT departments of multinational corporations.
Fuzzy Substring Matching: On-device Fuzzy Friend Search at Snapchat
Authors: Vasyl Pihur, Scott Thompson
Date: 2022-11-04 00:00:00
Tasks: Retrieval

About 50% of all queries on Snapchat app are targeted at finding the right friend to interact with. Since everyone has a unique list of friends and that list is not very large (maximum a few thousand), it makes sense to perform this search locally, on users' devices. In addition, the friend list is already available for other purposes, such as showing the chat feed, and the latency savings can be significant by avoiding a server round-trip call. Historically, we resorted to substring matching, ranking prefix matches at the top of the result list. Introducing the ability to perform fuzzy search on a resource-constrained device and in the environment where typo's are prevalent is both prudent and challenging. In this paper, we describe our efficient and accurate two-step approach to fuzzy search, characterized by a skip-bigram retrieval layer and a novel local Levenshtein distance computation used for final ranking.
Deep Learning for Improving Numerical Weather Prediction of Heavy Rainfall
Authors: Philipp Hess, Niklas Boers
Date: 2022-03-16 00:00:00
Proceeding: Journal of Advances in Modeling Earth Systems 2022 3
Tasks: Deep Learning

The accurate prediction of rainfall, and in particular of the heaviest rainfall events, remains challenging for numerical weather prediction (NWP) models. This may be due to subgrid-scale parameterizations of processes that play a crucial role in the multi-scale dynamics generating rainfall, as well as the strongly intermittent nature and the highly skewed, non-Gaussian distribution of rainfall. Here we show that a U-Net-based deep neural network can learn heavy rainfall events from a NWP ensemble. A frequency-based weighting of the loss function is proposed to enable the learning of heavy rainfall events in the distributions' tails. We apply our framework in a post-processing step to correct for errors in the model-predicted rainfall. Our method yields a much more accurate representation of relative rainfall frequencies and improves the forecast skill of heavy rainfall events by factors ranging from two to above six, depending on the event magnitude.
Speech enhancement using ego-noise references with a microphone array embedded in an unmanned aerial vehicle
Authors: Elisa Tengan, Thomas Dietzen, Santiago Ruiz, Mansour Alkmim, João Cardenuto, Toon van Waterschoot
Date: 2022-11-04 00:00:00
Tasks: Speech Enhancement

A method is proposed for performing speech enhancement using ego-noise references with a microphone array embedded in an unmanned aerial vehicle (UAV). The ego-noise reference signals are captured with microphones located near the UAV's propellers and used in the prior knowledge multichannel Wiener filter (PK-MWF) to obtain the speech correlation matrix estimate. Speech presence probability (SPP) can be estimated for detecting speech activity from an external microphone near the speech source, providing a performance benchmark, or from one of the embedded microphones, assuming a more realistic scenario. Experimental measurements are performed in a semi-anechoic chamber, with a UAV mounted on a stand and a loudspeaker playing a speech signal, while setting three distinct and fixed propeller rotation speeds, resulting in three different signal-to-noise ratios (SNRs). The recordings obtained and made available online are used to compare the proposed method to the use of the standard multichannel Wiener filter (MWF) estimated with and without the propellers' microphones being used in its formulation. Results show that compared to those, the use of PK-MWF achieves higher levels of improvement in speech intelligibility and quality, measured by STOI and PESQ, while the SNR improvement is similar.
Hopf insulators and their topologically protected surface states
Authors: Dong-Ling Deng, Sheng-Tao Wang, Chao Shen, Lu-Ming Duan
Date: 2013-11-18 00:00:00

Three-dimensional (3D) topological insulators in general need to be protected by certain kinds of symmetries other than the presumed U(1) charge conservation. A peculiar exception is the Hopf insulators which are 3D topological insulators characterized by an integer Hopf index. To demonstrate the existence and physical relevance of the Hopf insulators, we construct a class of tight-binding model Hamiltonians which realize all kinds of Hopf insulators with arbitrary integer Hopf index. These Hopf insulator phases have topologically protected surface states and we numerically demonstrate the robustness of these topologically protected states under general random perturbations without any symmetry other than the U(1) charge conservation that is implicit in all kinds of topological insulators.
Task Adaptive Network for Image Restoration With Combined Degradation Factors
Authors: Jingyuan Zhou, Chaktou Leong, Minyi Lin, Wantong Liao, Congduan Li
Date: 2022-02-15 00:00:00
Proceeding: IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) 2022 2
Tasks: Image Restoration, Rain Removal, Single Image Deraining, Single Image Haze Removal

Existing methods have achieved excellent performance on image restoration, but most of them are designed for one type of degradation. However, the weather is complex in the real world. So networks designed for single tasks are usually difficult to apply. Therefore, we propose a task-adaptive attention module to enable the network to restore images with multiple degradation factors. The task-adaptive attention module mainly includes three parts: Task-Adaptive sub-network, Task Channel Attention, and Task Operation Attention. To evaluate the model, we construct a mixed degradation factors dataset that combines three degradation factors of rain, haze, and raindrop. The experimental results show that our method not only better restores images with mixed degradation factors, but also show competitive results compared to the state-of-the-art models of each task.
Extremely-Fast, Energy-Efficient Massive MIMO Precoding with Analog RRAM Matrix Computing
Authors: Pushen Zuo, Zhong Sun, Ru Huang
Date: 2022-11-07 00:00:00

Signal processing in wireless communications, such as precoding, detection, and channel estimation, are basically about solving inverse matrix problems, which, however, are slow and inefficient in conventional digital computers, thus requiring a radical paradigm shift to achieve fast, real-time solutions. Here, for the first time, we apply the emerging analog matrix computing (AMC) to the linear precoding of massive MIMO. The real-valued AMC concept is extended to process complex-valued signals. In order to adapt the MIMO channel models to RRAM conductance mapping, a new matrix inversion circuit is developed. In addition, fully analog dataflow and optimized operational amplifiers are designed to support AMC precoding implementation. Simulation results show that the zero-forcing precoding is solved within 20 ns for a 16x128 MIMO system, which is two orders of magnitude faster than the conventional digital approach. Meanwhile, the energy efficiency is improved by 50x.
Electronics and Sensor Subsystem Design for Daedalus 2 on REXUS 29: An Autorotation Probe for Sub-Orbital Re-Entry
Authors: Jan M. Wolf, Lennart Werner, Philip Bergmann, Clemens Riegler, Frederik Dunschen
Date: 2022-11-05 00:00:00
Tasks: Pitch control

The Daedalus 2 mission aboard REXUS 29 is a technology demonstrator for an alternative descent mechanism for very high altitude drops based on auto-rotation. It consists of two probes that are ejected from a sounding rocket at an altitude of about 80 km and decelerate to a soft landing using only a passive rotor with pitch control. This type of autonomous, scientific experiment poses great challenges upon the electronics subsystem, which include mechanical stress, power system reliability, sensor redundancy, subsystem communication, and development procedures. Based on the data gathered in Daedalus 1 multiple new approaches were developed to fulfill these requirements, such as redundant communication links, mechanical decoupling of PCBs and fault-tolerant power source selection.
Distinguishable Speaker Anonymization based on Formant and Fundamental Frequency Scaling
Authors: Jixun Yao, Qing Wang, Yi Lei, Pengcheng Guo, Lei Xie, Namin Wang, Jie Liu
Date: 2022-11-06 00:00:00
Tasks: Speaker anonymization, Speaker Verification

Speech data on the Internet are proliferating exponentially because of the emergence of social media, and the sharing of such personal data raises obvious security and privacy concerns. One solution to mitigate these concerns involves concealing speaker identities before sharing speech data, also referred to as speaker anonymization. In our previous work, we have developed an automatic speaker verification (ASV)-model-free anonymization framework to protect speaker privacy while preserving speech intelligibility. Although the framework ranked first place in VoicePrivacy 2022 challenge, the anonymization was imperfect, since the speaker distinguishability of the anonymized speech was deteriorated. To address this issue, in this paper, we directly model the formant distribution and fundamental frequency (F0) to represent speaker identity and anonymize the source speech by the uniformly scaling formant and F0. By directly scaling the formant and F0, the speaker distinguishability degradation of the anonymized speech caused by the introduction of other speakers is prevented. The experimental results demonstrate that our proposed framework can improve the speaker distinguishability and significantly outperforms our previous framework in voice distinctiveness. Furthermore, our proposed method also can trade off the privacy-utility by using different scaling factors.
Deep Distance Sensitivity Oracles
Authors: Davin Jeong, Allison Gunby-Mann, Sarel Cohen, Maximilian Katzmann, Chau Pham, Arnav Bhakta, Tobias Friedrich, Sang Chin
Date: 2022-11-02 00:00:00
Tasks: Deep Learning, Sensitivity

One of the most fundamental graph problems is finding a shortest path from a source to a target node. While in its basic forms the problem has been studied extensively and efficient algorithms are known, it becomes significantly harder as soon as parts of the graph are susceptible to failure. Although one can recompute a shortest replacement path after every outage, this is rather inefficient both in time and/or storage. One way to overcome this problem is to shift computational burden from the queries into a pre-processing step, where a data structure is computed that allows for fast querying of replacement paths, typically referred to as a Distance Sensitivity Oracle (DSO). While DSOs have been extensively studied in the theoretical computer science community, to the best of our knowledge this is the first work to construct DSOs using deep learning techniques. We show how to use deep learning to utilize a combinatorial structure of replacement paths. More specifically, we utilize the combinatorial structure of replacement paths as a concatenation of shortest paths and use deep learning to find the pivot nodes for stitching shortest paths into replacement paths.
How Technology Impacts and Compares to Humans in Socially Consequential Arenas
Authors: Samuel Dooley
Date: 2022-11-02 00:00:00

One of the main promises of technology development is for it to be adopted by people, organizations, societies, and governments -- incorporated into their life, work stream, or processes. Often, this is socially beneficial as it automates mundane tasks, frees up more time for other more important things, or otherwise improves the lives of those who use the technology. However, these beneficial results do not apply in every scenario and may not impact everyone in a system the same way. Sometimes a technology is developed which produces both benefits and inflicts some harm. These harms may come at a higher cost to some people than others, raising the question: {\it how are benefits and harms weighed when deciding if and how a socially consequential technology gets developed?} The most natural way to answer this question, and in fact how people first approach it, is to compare the new technology to what used to exist. As such, in this work, I make comparative analyses between humans and machines in three scenarios and seek to understand how sentiment about a technology, performance of that technology, and the impacts of that technology combine to influence how one decides to answer my main research question.
Group Testing with Side Information via Generalized Approximate Message Passing
Authors: Shu-Jie Cao, Ritesh Goenka, Chau-Wai Wong, Ajit Rajwade, Dror Baron
Date: 2022-11-07 00:00:00

Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In a group testing setup, we are given n samples, one per individual. Each individual is either infected or uninfected. These samples are arranged into m < n pooled samples, where each pool is obtained by mixing a subset of the n individual samples. Infected individuals are then identified using a group testing algorithm. In this paper, we incorporate side information (SI) collected from contact tracing (CT) into nonadaptive/single-stage group testing algorithms. We generate different types of possible CT SI data by incorporating different possible characteristics of the spread of disease. These data are fed into a group testing framework based on generalized approximate message passing (GAMP). Numerical results show that our GAMP-based algorithms provide improved accuracy.
Error Performance of Rectangular Pulse-shaped OTFS with Practical Receivers
Authors: Cheng Shen, Jinhong Yuan, Hai Lin
Date: 2022-11-07 00:00:00

In this letter, we investigate error performance of rectangular pulse-shaped orthogonal time frequency space (OTFS) modulation with a practical receiver. Specifically, we consider an essential bandpass filter at receiver RF front-end, which has been ignored in existing works. We analyse the effect of rectangular pulses on practical OTFS receiver performance, and derive the exact forms of interference in delay-Doppler (DD) domain. We demonstrate that the transmitted information symbols in certain regions of the DD domain are severely contaminated. As a result, there is an error floor in the receiver error performance, which needs to be addressed for such OTFS waveform in practical systems.
Peak-First CTC: Reducing the Peak Latency of CTC Models by Applying Peak-First Regularization
Authors: Zhengkun Tian, Hongyu Xiang, Min Li, Feifei Lin, Ke Ding, Guanglu Wan
Date: 2022-11-07 00:00:00
Tasks: Knowledge Distillation

The CTC model has been widely applied to many application scenarios because of its simple structure, excellent performance, and fast inference speed. There are many peaks in the probability distribution predicted by the CTC models, and each peak represents a non-blank token. The recognition latency of CTC models can be reduced by encouraging the model to predict peaks earlier. Existing methods to reduce latency require modifying the transition relationship between tokens in the forward-backward algorithm, and the gradient calculation. Some of these methods even depend on the forced alignment results provided by other pretrained models. The above methods are complex to implement. To reduce the peak latency, we propose a simple and novel method named peak-first regularization, which utilizes a frame-wise knowledge distillation function to force the probability distribution of the CTC model to shift left along the time axis instead of directly modifying the calculation process of CTC loss and gradients. All the experiments are conducted on a Chinese Mandarin dataset AISHELL-1. We have verified the effectiveness of the proposed regularization on both streaming and non-streaming CTC models respectively. The results show that the proposed method can reduce the average peak latency by about 100 to 200 milliseconds with almost no degradation of recognition accuracy.
Robust Total Least Mean M-Estimate normalized subband filter Adaptive Algorithm for impulse noises and noisy inputs
Authors: Haiquan Zhao, Zian Cao, Yida Chen
Date: 2022-11-07 00:00:00

When the input signal is correlated input signals, and the input and output signal is contaminated by Gaussian noise, the total least squares normalized subband adaptive filter (TLS-NSAF) algorithm shows good performance. However, when it is disturbed by impulse noise, the TLS-NSAF algorithm shows the rapidly deteriorating convergence performance. To solve this problem, this paper proposed the robust total minimum mean M-estimator normalized subband filter (TLMM-NSAF) algorithm. In addition, this paper also conducts a detailed theoretical performance analysis of the TLMM-NSAF algorithm and obtains the stable step size range and theoretical steady-state mean squared deviation (MSD) of the algorithm. To further improve the performance of the algorithm, we also propose a new variable step size (VSS) method of the algorithm. Finally, the robustness of our proposed algorithm and the consistency of theoretical and simulated values are verified by computer simulations of system identification and echo cancellation under different noise models.
A Context-Aware Computational Approach for Measuring Vocal Entrainment in Dyadic Conversations
Authors: Rimita Lahiri, Md Nasir, Catherine Lord, So Hyun Kim, Shrikanth Narayanan
Date: 2022-11-07 00:00:00

Vocal entrainment is a social adaptation mechanism in human interaction, knowledge of which can offer useful insights to an individual's cognitive-behavioral characteristics. We propose a context-aware approach for measuring vocal entrainment in dyadic conversations. We use conformers(a combination of convolutional network and transformer) for capturing both short-term and long-term conversational context to model entrainment patterns in interactions across different domains. Specifically we use cross-subject attention layers to learn intra- as well as inter-personal signals from dyadic conversations. We first validate the proposed method based on classification experiments to distinguish between real(consistent) and fake(inconsistent/shuffled) conversations. Experimental results on interactions involving individuals with Autism Spectrum Disorder also show evidence of a statistically-significant association between the introduced entrainment measure and clinical scores relevant to symptoms, including across gender and age groups.
An Empirical Study on L2 Accents of Cross-lingual Text-to-Speech Systems via Vowel Space
Authors: JIhwan Lee, Jae-Sung Bae, Seongkyu Mun, Heejin Choi, Joun Yeop Lee, Hoon-Young Cho, Chanwoo Kim
Date: 2022-11-06 00:00:00
Tasks: text-to-speech, Text to Speech

With the recent developments in cross-lingual Text-to-Speech (TTS) systems, L2 (second-language, or foreign) accent problems arise. Moreover, running a subjective evaluation for such cross-lingual TTS systems is troublesome. The vowel space analysis, which is often utilized to explore various aspects of language including L2 accents, is a great alternative analysis tool. In this study, we apply the vowel space analysis method to explore L2 accents of cross-lingual TTS systems. Through the vowel space analysis, we observe the three followings: a) a parallel architecture (Glow-TTS) is less L2-accented than an auto-regressive one (Tacotron); b) L2 accents are more dominant in non-shared vowels in a language pair; and c) L2 accents of cross-lingual TTS systems share some phenomena with those of human L2 learners. Our findings imply that it is necessary for TTS systems to handle each language pair differently, depending on their linguistic characteristics such as non-shared vowels. They also hint that we can further incorporate linguistics knowledge in developing cross-lingual TTS systems.
Preserving background sound in noise-robust voice conversion via multi-task learning
Authors: Jixun Yao, Yi Lei, Qing Wang, Pengcheng Guo, Ziqian Ning, Lei Xie, Hai Li, Junhui Liu, Danming Xie
Date: 2022-11-06 00:00:00
Tasks: Multi-Task Learning, Voice Conversion

Background sound is an informative form of art that is helpful in providing a more immersive experience in real-application voice conversion (VC) scenarios. However, prior research about VC, mainly focusing on clean voices, pay rare attention to VC with background sound. The critical problem for preserving background sound in VC is inevitable speech distortion by the neural separation model and the cascade mismatch between the source separation model and the VC model. In this paper, we propose an end-to-end framework via multi-task learning which sequentially cascades a source separation (SS) module, a bottleneck feature extraction module and a VC module. Specifically, the source separation task explicitly considers critical phase information and confines the distortion caused by the imperfect separation process. The source separation task, the typical VC task and the unified task shares a uniform reconstruction loss constrained by joint training to reduce the mismatch between the SS and VC modules. Experimental results demonstrate that our proposed framework significantly outperforms the baseline systems while achieving comparable quality and speaker similarity to the VC models trained with clean data.
Breaking the trade-off in personalized speech enhancement with cross-task knowledge distillation
Authors: Hassan Taherian, Sefik Emre Eskimez, Takuya Yoshioka
Date: 2022-11-05 00:00:00
Tasks: Knowledge Distillation, Speech Enhancement

Personalized speech enhancement (PSE) models achieve promising results compared with unconditional speech enhancement models due to their ability to remove interfering speech in addition to background noise. Unlike unconditional speech enhancement, causal PSE models may occasionally remove the target speech by mistake. The PSE models also tend to leak interfering speech when the target speaker is silent for an extended period. We show that existing PSE methods suffer from a trade-off between speech over-suppression and interference leakage by addressing one problem at the expense of the other. We propose a new PSE model training framework using cross-task knowledge distillation to mitigate this trade-off. Specifically, we utilize a personalized voice activity detector (pVAD) during training to exclude the non-target speech frames that are wrongly identified as containing the target speaker with hard or soft classification. This prevents the PSE model from being too aggressive while still allowing the model to learn to suppress the input speech when it is likely to be spoken by interfering speakers. Comprehensive evaluation results are presented, covering various PSE usage scenarios.