pwc-archive/papers-with-abstracts huggingface dataset:
Measuring Economic Policy Uncertainty Using an Unsupervised Word Embedding-based Method
Authors: Fatemeh Kaveh-Yazdy, Sajjad Zarifzadeh
Date: 2021-05-10 00:00:00
Tasks: Articles, Semantic Similarity, Semantic Textual Similarity

Economic Policy Uncertainty (EPU) is a critical indicator in economic studies, while it can be used to forecast a recession. Under higher levels of uncertainty, firms' owners cut their investment, which leads to a longer post-recession recovery. EPU index is computed by counting news articles containing pre-defined keywords related to policy-making and economy and convey uncertainty. Unfortunately, this method is sensitive to the original keyword set, its richness, and the news coverage. Thus, reproducing its results for different countries is challenging. In this paper, we propose an unsupervised text mining method that uses word-embedding representation space to select relevant keywords. This method is not strictly sensitive to the semantic similarity threshold applied to the word embedding vectors and does not require a pre-defined dictionary. Our experiments using a massive repository of Persian news show that the EPU series computed by the proposed method precisely follows major events affecting Iran's economy and is compatible with the World Uncertainty Index (WUI) of Iran.
Automatic Classification of Human Translation and Machine Translation: A Study from the Perspective of Lexical Diversity
Authors: Yingxue Fu, Mark-Jan Nederhof
Date: 2021-05-10 00:00:00
Proceeding: MoTra (NoDaLiDa) 2021 5
Tasks: Classification, Diversity, Machine Translation, Translation

By using a trigram model and fine-tuning a pretrained BERT model for sequence classification, we show that machine translation and human translation can be classified with an accuracy above chance level, which suggests that machine translation and human translation are different in a systematic way. The classification accuracy of machine translation is much higher than of human translation. We show that this may be explained by the difference in lexical diversity between machine translation and human translation. If machine translation has independent patterns from human translation, automatic metrics which measure the deviation of machine translation from human translation may conflate difference with quality. Our experiment with two different types of automatic metrics shows correlation with the result of the classification task. Therefore, we suggest the difference in lexical diversity between machine translation and human translation be given more attention in machine translation evaluation.
Reducing Streaming ASR Model Delay with Self Alignment
Authors: Jaeyoung Kim, Han Lu, Anshuman Tripathi, Qian Zhang, Hasim Sak
Date: 2021-05-06 00:00:00

Reducing prediction delay for streaming end-to-end ASR models with minimal performance regression is a challenging problem. Constrained alignment is a well-known existing approach that penalizes predicted word boundaries using external low-latency acoustic models. On the contrary, recently proposed FastEmit is a sequence-level delay regularization scheme encouraging vocabulary tokens over blanks without any reference alignments. Although all these schemes are successful in reducing delay, ASR word error rate (WER) often severely degrades after applying these delay constraining schemes. In this paper, we propose a novel delay constraining method, named self alignment. Self alignment does not require external alignment models. Instead, it utilizes Viterbi forced-alignments from the trained model to find the lower latency alignment direction. From LibriSpeech evaluation, self alignment outperformed existing schemes: 25% and 56% less delay compared to FastEmit and constrained alignment at the similar word error rate. For Voice Search evaluation,12% and 25% delay reductions were achieved compared to FastEmit and constrained alignment with more than 2% WER improvements.
Emergence of Structural Bias in Differential Evolution
Authors: Bas van Stein, Fabio Caraffini, Anna V. Kononova
Date: 2021-05-10 00:00:00

Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved. The complexity of these problems is well beyond the boundaries of applicability of exact optimisation algorithms and therefore require modern heuristics to find feasible solutions quickly. These heuristics and their effects are almost always evaluated and explained by particular problem instances. In previous works, it has been shown that many such algorithms show structural bias, by either being attracted to a certain region of the search space or by consistently avoiding regions of the search space, on a special test function designed to ensure uniform 'exploration' of the domain. In this paper, we analyse the emergence of such structural bias for Differential Evolution (DE) configurations and, specifically, the effect of different mutation, crossover and correction strategies. We also analyse the emergence of the structural bias during the run-time of each algorithm. We conclude with recommendations of which configurations should be avoided in order to run DE unbiased.
Soft BIBD and Product Gradient Codes
Authors: Animesh Sakorikar, Lele Wang
Date: 2021-05-11 00:00:00

Gradient coding is a coding theoretic framework to provide robustness against slow or unresponsive machines, known as stragglers, in distributed machine learning applications. Recently, Kadhe et al. proposed a gradient code based on a combinatorial design, called balanced incomplete block design (BIBD), which is shown to outperform many existing gradient codes in worst-case adversarial straggling scenarios. However, parameters for which such BIBD constructions exist are very limited. In this paper, we aim to overcome such limitations and construct gradient codes which exist for a wide range of system parameters while retaining the superior performance of BIBD gradient codes. Two such constructions are proposed, one based on a probabilistic construction that relax the stringent BIBD gradient code constraints, and the other based on taking the Kronecker product of existing gradient codes. The proposed gradient codes allow flexible choices of system parameters while retaining comparable error performance.
Learning Runge-Kutta Integration Schemes for ODE Simulation and Identification
Authors: Said Ouala, Laurent Debreu, Ananda Pascual, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, Ronan Fablet
Date: 2021-05-11 00:00:00

Deriving analytical solutions of ordinary differential equations is usually restricted to a small subset of problems and numerical techniques are considered. Inevitably, a numerical simulation of a differential equation will then always be distinct from a true analytical solution. An efficient integration scheme shall further not only provide a trajectory throughout a given state, but also be derived to ensure the generated simulation to be close to the analytical one. Consequently, several integration schemes were developed for different classes of differential equations. Unfortunately, when considering the integration of complex non-linear systems, as well as the identification of non-linear equations from data, this choice of the integration scheme is often far from being trivial. In this paper, we propose a novel framework to learn integration schemes that minimize an integration-related cost function. We demonstrate the relevance of the proposed learning-based approach for non-linear equations and include a quantitative analysis w.r.t. classical state-of-the-art integration techniques, especially where the latter may not apply.
Estimation and Quantization of Expected Persistence Diagrams
Authors: Vincent Divol, Théo Lacombe
Date: 2021-05-11 00:00:00
Tasks: Quantization, Time Series, Time Series Analysis

Persistence diagrams (PDs) are the most common descriptors used to encode the topology of structured data appearing in challenging learning tasks; think e.g. of graphs, time series or point clouds sampled close to a manifold. Given random objects and the corresponding distribution of PDs, one may want to build a statistical summary-such as a mean-of these random PDs, which is however not a trivial task as the natural geometry of the space of PDs is not linear. In this article, we study two such summaries, the Expected Persistence Diagram (EPD), and its quantization. The EPD is a measure supported on R 2 , which may be approximated by its empirical counterpart. We prove that this estimator is optimal from a minimax standpoint on a large class of models with a parametric rate of convergence. The empirical EPD is simple and efficient to compute, but possibly has a very large support, hindering its use in practice. To overcome this issue, we propose an algorithm to compute a quantization of the empirical EPD, a measure with small support which is shown to approximate with near-optimal rates a quantization of the theoretical EPD.
A Text Extraction-Based Smart Knowledge Graph Composition for Integrating Lessons Learned during the Microchip Design
Authors: H. Abu-Rasheed, C. Weber, J. Zenkert, P. Czerner, R. Krumm, M. Fathi
Date: 2021-05-11 00:00:00
Tasks: Retrieval

The production of microchips is a complex and thus well documented process. Therefore, available textual data about the production can be overwhelming in terms of quantity. This affects the visibility and retrieval of a certain piece of information when it is most needed. In this paper, we propose a dynamic approach to interlink the information extracted from multisource production-relevant documents through the creation of a knowledge graph. This graph is constructed in order to support searchability and enhance user's access to large-scale production information. Text mining methods are firstly utilized to extract data from multiple documentation sources. Document relations are then mined and extracted for the composition of the knowledge graph. Graph search functionality is then supported with a recommendation use-case to enhance users' access to information that is related to the initial documents. The proposed approach is tailored to and tested on microchip design-relevant documents. It enhances the visibility and findability of previous design-failure-cases during the process of a new chip design.
Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
Authors: Yongchun Zhu, Ruobing Xie, Fuzhen Zhuang, Kaikai Ge, Ying Sun, Xu Zhang, Leyu Lin, Juan Cao
Date: 2021-05-11 00:00:00
Tasks: Recommendation Systems

Recently, embedding techniques have achieved impressive success in recommender systems. However, the embedding techniques are data demanding and suffer from the cold-start problem. Especially, for the cold-start item which only has limited interactions, it is hard to train a reasonable item ID embedding, called cold ID embedding, which is a major challenge for the embedding techniques. The cold item ID embedding has two main problems: (1) A gap is existing between the cold ID embedding and the deep model. (2) Cold ID embedding would be seriously affected by noisy interaction. However, most existing methods do not consider both two issues in the cold-start problem, simultaneously. To address these problems, we adopt two key ideas: (1) Speed up the model fitting for the cold item ID embedding (fast adaptation). (2) Alleviate the influence of noise. Along this line, we propose Meta Scaling and Shifting Networks to generate scaling and shifting functions for each item, respectively. The scaling function can directly transform cold item ID embeddings into warm feature space which can fit the model better, and the shifting function is able to produce stable embeddings from the noisy embeddings. With the two meta networks, we propose Meta Warm Up Framework (MWUF) which learns to warm up cold ID embeddings. Moreover, MWUF is a general framework that can be applied upon various existing deep recommendation models. The proposed model is evaluated on three popular benchmarks, including both recommendation and advertising datasets. The evaluation results demonstrate its superior performance and compatibility.
Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models
Authors: Daniel Cohen, Bhaskar Mitra, Oleg Lesota, Navid Rekabsaz, Carsten Eickhoff
Date: 2021-05-10 00:00:00
Tasks: All, Reranking, Retrieval

In any ranking system, the retrieval model outputs a single score for a document based on its belief on how relevant it is to a given search query. While retrieval models have continued to improve with the introduction of increasingly complex architectures, few works have investigated a retrieval model's belief in the score beyond the scope of a single value. We argue that capturing the model's uncertainty with respect to its own scoring of a document is a critical aspect of retrieval that allows for greater use of current models across new document distributions, collections, or even improving effectiveness for down-stream tasks. In this paper, we address this problem via an efficient Bayesian framework for retrieval models which captures the model's belief in the relevance score through a stochastic process while adding only negligible computational overhead. We evaluate this belief via a ranking based calibration metric showing that our approximate Bayesian framework significantly improves a retrieval model's ranking effectiveness through a risk aware reranking as well as its confidence calibration. Lastly, we demonstrate that this additional uncertainty information is actionable and reliable on down-stream tasks represented via cutoff prediction.
Measurement of bioimpedance and application of Cole model to study the effect of moisturizing cream on human skin
Authors: Arijit Roy, Somnath Bhattacharjee, Soumyajit Podder, Advaita Ghosh
Date: 2020-09-10 00:00:00

A non-invasive method of measuring bioimpedance is presented to study the effect of moisturizing cream on human skin. A novel two-electrode system is developed to get electrical access at the area of interest on human skin. Controlled experiments are conducted for a duration of 47 days. Bioimpedance is measured by LCR meter in the frequency range of 1 Hz to 5 kHz. The measured impedance is then fitted with the Cole model and the relevant Cole parameters are extracted. Analysis of variance is performed on the Cole parameters to gain statistical relevance. It is found that one of the Cole parameters, namely the relaxation-time changes significantly due to the treatment of the skin with a moisturizing cream at more than 95% confidence level. The relaxation-time increases sharply during the treatment period and drops sharply in the post-treatment period. The effectiveness of the two types of cream is studied. The gender distinguishability during the application of a moisturizing cream is also studied. A very generic bioimpedance measurement method to assess the physiological condition of local skin is demonstrated here and the method has potential application in human skin related diseases and treatments.
Over-the-Air Computation via Reconfigurable Intelligent Surface
Authors: Wenzhi Fang, Yuning Jiang, Yuanming Shi, Yong Zhou, Wei Chen, Khaled B. Letaief
Date: 2021-05-11 00:00:00

Over-the-air computation (AirComp) is a disruptive technique for fast wireless data aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition property of multiple-access channels. However, the performance of AirComp is bottlenecked by the worst channel condition among all links between the IoT devices and the access point. In this paper, a reconfigurable intelligent surface (RIS) assisted AirComp system is proposed to boost the received signal power and thus mitigate the performance bottleneck by reconfiguring the propagation channels. With an objective to minimize the AirComp distortion, we propose a joint design of AirComp transceivers and RIS phase-shifts, which however turns out to be a highly intractable non-convex programming problem. To this end, we develop a novel alternating minimization framework in conjunction with the successive convex approximation technique, which is proved to converge monotonically. To reduce the computational complexity, we transform the subproblem in each alternation as a smooth convex-concave saddle point problem, which is then tackled by proposing a Mirror-Prox method that only involves a sequence of closed-form updates. Simulations show that the computation time of the proposed algorithm can be two orders of magnitude smaller than that of the state-of-the-art algorithms, while achieving a similar distortion performance.
Optimal Receive Beamforming for Over-the-Air Computation
Authors: Wenzhi Fang, Yinan Zou, Hongbin Zhu, Yuanming Shi, Yong Zhou
Date: 2021-05-11 00:00:00
Tasks: Denoising

In this paper, we consider fast wireless data aggregation via over-the-air computation (AirComp) in Internet of Things (IoT) networks, where an access point (AP) with multiple antennas aim to recover the arithmetic mean of sensory data from multiple IoT devices. To minimize the estimation distortion, we formulate a mean-squared-error (MSE) minimization problem that involves the joint optimization of the transmit scalars at the IoT devices as well as the denoising factor and the receive beamforming vector at the AP. To this end, we derive the transmit scalars and the denoising factor in closed-form, resulting in a non-convex quadratic constrained quadratic programming (QCQP) problem concerning the receive beamforming vector.Different from the existing studies that only obtain sub-optimal beamformers, we propose a branch and bound (BnB) algorithm to design the globally optimal receive beamformer.Extensive simulations demonstrate the superior performance of the proposed algorithm in terms of MSE. Moreover, the proposed BnB algorithm can serve as a benchmark to evaluate the performance of the existing sub-optimal algorithms.
Asymptotically Optimal Procedures for Sequential Joint Detection and Estimation
Authors: Dominik Reinhard, Michael Fauß, Abdelhak M. Zoubir
Date: 2021-05-11 00:00:00

We investigate the problem of jointly testing multiple hypotheses and estimating a random parameter of the underlying distribution in a sequential setup. The aim is to jointly infer the true hypothesis and the true parameter while using on average as few samples as possible and keeping the detection and estimation errors below predefined levels. Based on mild assumptions on the underlying model, we propose an asymptotically optimal procedure, i.e., a procedure that becomes optimal when the tolerated detection and estimation error levels tend to zero. The implementation of the resulting asymptotically optimal stopping rule is computationally cheap and, hence, applicable for high-dimensional data. We further propose a projected quasi-Newton method to optimally choose the coefficients that parameterize the instantaneous cost function such that the constraints are fulfilled with equality. The proposed theory is validated by numerical examples.
TANGO: Commonsense Generalization in Predicting Tool Interactions for Mobile Manipulators
Authors: Shreshth Tuli, Rajas Bansal, Rohan Paul, Mausam
Date: 2021-05-05 00:00:00
Tasks: Graph Neural Network

Robots assisting us in factories or homes must learn to make use of objects as tools to perform tasks, e.g., a tray for carrying objects. We consider the problem of learning commonsense knowledge of when a tool may be useful and how its use may be composed with other tools to accomplish a high-level task instructed by a human. We introduce a novel neural model, termed TANGO, for predicting task-specific tool interactions, trained using demonstrations from human teachers instructing a virtual robot. TANGO encodes the world state, comprising objects and symbolic relationships between them, using a graph neural network. The model learns to attend over the scene using knowledge of the goal and the action history, finally decoding the symbolic action to execute. Crucially, we address generalization to unseen environments where some known tools are missing, but alternative unseen tools are present. We show that by augmenting the representation of the environment with pre-trained embeddings derived from a knowledge-base, the model can generalize effectively to novel environments. Experimental results show a 60.5-78.9% absolute improvement over the baseline in predicting successful symbolic plans in unseen settings for a simulated mobile manipulator.
Cross-Corpora Language Recognition: A Preliminary Investigation with Indian Languages
Authors: Spandan Dey, Goutam Saha, Md Sahidullah
Date: 2021-05-10 00:00:00
Tasks: Language Identification, Spoken language identification

In this paper, we conduct one of the very first studies for cross-corpora performance evaluation in the spoken language identification (LID) problem. Cross-corpora evaluation was not explored much in LID research, especially for the Indian languages. We have selected three Indian spoken language corpora: IIITH-ILSC, LDC South Asian, and IITKGP-MLILSC. For each of the corpus, LID systems are trained on the state-of-the-art time-delay neural network (TDNN) based architecture with MFCC features. We observe that the LID performance degrades drastically for cross-corpora evaluation. For example, the system trained on the IIITH-ILSC corpus shows an average EER of 11.80 % and 43.34 % when evaluated with the same corpora and LDC South Asian corpora, respectively. Our preliminary analysis shows the significant differences among these corpora in terms of mismatch in the long-term average spectrum (LTAS) and signal-to-noise ratio (SNR). Subsequently, we apply different feature level compensation methods to reduce the cross-corpora acoustic mismatch. Our results indicate that these feature normalization schemes can help to achieve promising LID performance on cross-corpora experiments.
Energy-optimal Design and Control of Electric Vehicles' Transmissions
Authors: Juriaan van den Hurk, Mauro Salazar
Date: 2021-05-11 00:00:00
Tasks: Computational Efficiency

This paper presents models and optimization algorithms to jointly optimize the design and control of the transmission of electric vehicles equipped with one central electric motor (EM). First, considering the required traction power to be given, we identify a convex speed-dependent loss model for the EM. Second, we leverage such a model to devise computationally-efficient algorithms to determine the energy-optimal design and control strategies for the transmission. In particular, with the objective of minimizing the EM energy consumption on a given drive cycle, we analytically compute the optimal gear-ratio trajectory for a continuously variable transmission (CVT) and the optimal gear-ratio design for a fixed-gear transmission (FGT) in closed form, whilst efficiently solving the combinatorial joint gear-ratio design and control problem for a multiple-gear transmission (MGT), combining convex analysis and dynamic programming in an iterative fashion. Third, we validate our models with nonlinear simulations, and benchmark the optimality of our methods with mixed-integer quadratic programming. Finally, we showcase our framework in a case-study for a family vehicle, whereby we leverage the computational efficiency of our methods to jointly optimize the EM size via exhaustive search. Our numerical results show that by using a 2-speed MGT, the energy consumption of an electric vehicle can be reduced by 3% and 2.5% compared to an FGT and a CVT, respectively, whilst further increasing the number of gears may even be detrimental due to the additional weight