pwc-archive/papers-with-abstracts huggingface dataset:
ASVspoof 2019: spoofing countermeasures for the detection of synthesized, converted and replayed speech
Authors: Andreas Nautsch, Xin Wang, Nicholas Evans, Tomi Kinnunen, Ville Vestman, Massimiliano Todisco, Héctor Delgado, Md Sahidullah, Junichi Yamagishi, Kong Aik Lee
Date: 2021-02-11 00:00:00
Tasks: Speaker Verification, Speech Synthesis, Voice Anti-spoofing, Voice Conversion

The ASVspoof initiative was conceived to spearhead research in anti-spoofing for automatic speaker verification (ASV). This paper describes the third in a series of bi-annual challenges: ASVspoof 2019. With the challenge database and protocols being described elsewhere, the focus of this paper is on results and the top performing single and ensemble system submissions from 62 teams, all of which out-perform the two baseline systems, often by a substantial margin. Deeper analyses shows that performance is dominated by specific conditions involving either specific spoofing attacks or specific acoustic environments. While fusion is shown to be particularly effective for the logical access scenario involving speech synthesis and voice conversion attacks, participants largely struggled to apply fusion successfully for the physical access scenario involving simulated replay attacks. This is likely the result of a lack of system complementarity, while oracle fusion experiments show clear potential to improve performance. Furthermore, while results for simulated data are promising, experiments with real replay data show a substantial gap, most likely due to the presence of additive noise in the latter. This finding, among others, leads to a number of ideas for further research and directions for future editions of the ASVspoof challenge.
On the Fragility of Third-party Punishment: The Context Effect of a Dominated Risky Investment Option
Authors: Changkuk Im, Jinkwon Lee
Date: 2021-02-11 00:00:00

Experimental studies regularly show that third-party punishment (TPP) substantially exists in various settings. This study further investigates the robustness of TPP under an environment where context effects are involved. In our experiment, we offer a third party an additional but unattractive risky investment option. We find that, when the dominated investment option irrelevant to prosocial behavior is available, the demand for punishment decreases, whereas the demand for investment increases. These findings support our hypothesis that the seemingly unrelated and dominated investment option may work as a compromise and suggest the fragility of TPP in this setting.
Suppressing evolution through environmental switching
Authors: Bryce Morsky, Dervis Can Vural
Date: 2021-02-11 00:00:00

Ecology and evolution under changing environments are important in many subfields of biology with implications for medicine. Here, we explore an example: the consequences of fluctuating environments on the emergence of antibiotic resistance, which is an immense and growing problem. Typically, high doses of antibiotics are employed to eliminate the infection quickly and minimize the time under which resistance may emerge. However, this strategy may not be optimal. Since competition can reduce fitness and resistance typically has a reproductive cost, resistant mutants' fitness can depend on their environment. Here we show conditions under which environmental varying fitness can be exploited to prevent the emergence of resistance. We develop a stochastic Lotka-Volterra model of a microbial system with competing phenotypes: a wild strain susceptible to the antibiotic, and a mutant strain that is resistant. We investigate the impact of various pulsed applications of antibiotics on population suppression. Leveraging competition, we show how a strategy of environmental switching can suppress the infection while avoiding resistant mutants. We discuss limitations of the procedure depending on the microbe and pharmacodynamics and methods to ameliorate them.
On Energy Optimal Speed Trajectories: The Impact of Electric Powertrain Efficiency Characteristics
Authors: Arian Ahmadi, Peter H. Bauer
Date: 2021-02-11 00:00:00

This paper investigates the effect of the powertrain efficiency map on energy optimal speed trajectories, especially stop-to-stop trajectories. A variety of different efficiency maps are explored and the energy optimization process is carried out. It is shown that while the efficiency maps can have a significant impact on the overall transportation energy, they have little impact on the shape of the optimal speed profiles. A number of very different efficiency maps and their effects on speed trajectories are illustrated. Also, the impact that vehicle and segment parameters as well as average speeds have on the savings are investigated. This paper also presents the impact of transient penalty on the shape of the energy optimal trajectories and associated savings. It is observed that, by adding an additional transient penalty to the energy balance, the shape of trajectories changes and energy savings decrease.
Tech Report: A Homogeneity-Based Multiscale Hyperspectral Image Representation for Sparse Spectral Unmixing
Authors: L. C. Ayres, S. J. M. de Almeida, J. C. M. Bermudez, R. A. Borsoi
Date: 2021-02-11 00:00:00
Tasks: Hyperspectral image analysis, Superpixels

Several approaches have been proposed to solve the spectral unmixing problem in hyperspectral image analysis. Among them the use of sparse regression techniques aims to characterize the abundances in pixels based on a large library of spectral signatures known a priori. Recently, the integration of image spatial-contextual information significantly enhanced the performance of sparse unmixing. In this work, we propose a computationally efficient multiscale representation method for hyperspectral data adapted to the unmixing problem. The proposed method is based on a hierarchical extension of the SLIC oversegmentation algorithm constructed using a robust homogeneity testing. The image is subdivided into a set of spectrally homogeneous regions formed by pixels with similar characteristics (superpixels). This representation is then used to provide prior spatial regularity information for the abundances of materials present in the scene, improving the conditioning of the unmixing problem. Simulation results illustrate that the method is capable of estimating abundances with high quality and low computational cost, especially in noisy scenarios.
Labor Informality and Credit Market Accessibility
Authors: Alina Malkova, Klara Sabirianova Peter, Jan Svejnar
Date: 2021-02-11 00:00:00
Tasks: Credit score

The paper investigates the effects of the credit market development on the labor mobility between the informal and formal labor sectors. In the case of Russia, due to the absence of a credit score system, a formal lender may set a credit limit based on the verified amount of income. To get a loan, an informal worker must first formalize his or her income (switch to a formal job), and then apply for a loan. To show this mechanism, the RLMS data was utilized, and the empirical method is the dynamic multinomial logit model of employment. The empirical results show that a relaxation of credit constraints increases the probability of transition from an informal to a formal job, and improved CMA (by one standard deviation) increases the chances of informal sector workers to formalize by 5.4 ppt. These results are robust in different specifications of the model. Policy simulations show strong support for a reduction in informal employment in response to better CMA in credit-constrained communities.
Portfolio Performance Attribution via Shapley Value
Authors: Nicholas Moehle, Stephen Boyd, Andrew Ang
Date: 2021-02-11 00:00:00
Tasks: Attribute

We consider an investment process that includes a number of features, each of which can be active or inactive. Our goal is to attribute or decompose an achieved performance to each of these features, plus a baseline value. There are many ways to do this, which lead to potentially different attributions in any specific case. We argue that a specific attribution method due to Shapley is the preferred method, and discuss methods that can be used to compute this attribution exactly, or when that is not practical, approximately.
State Synchronization of Discrete-time Multi-agent Systems in Presence of Unknown Nonuniform Communication Delays: A Scale-free Protocol Design
Authors: Zhenwei Liu, Donya Nojavanzadeh, Ali Saberi, Anton A. Stoorvogel
Date: 2021-02-11 00:00:00

In this paper, we study scale-free state synchronization of discrete-time homogeneous multi-agent systems (MAS) subject to unknown, nonuniform, and arbitrarily large communication delays. The scale-free protocol utilizes localized information exchange and is designed solely based on the knowledge of the agents' model and does not require any information about the communication network and the size of the network (i.e. number of agents).
Peptides of H. sapiens and P. falciparum that are predicted to bind strongly to HLA-A*24:02 and homologous to a SARS-CoV-2 peptide
Authors: Yekbun Adiguzel
Date: 2021-01-18 00:00:00

Aim: This study is looking for a common pathogenicity between SARS-CoV-2 and plasmodium species, in individuals with certain HLA serotypes. Methods: 1-) Tblastx searches of SARS-CoV-2 are performed by limiting searches to plasmodium species that infect human. 2-) Aligned sequences in the respective organisms' proteomes are searched with blastp. 3-) Binding predictions of the identified SARS-CoV-2 peptide to MHC class I supertype representatives are performed. 4-) Blastp searches of predicted-epitopes that bind strongly to the identified HLA allele are performed by limiting searches to human and to the plasmodium species. 5-) Peptides with minimum 60 % identity to the predicted-epitopes are found in results. 6-) Peptides among those, which bind strongly to the same HLA allele, are predicted. 7-) Step-4 is repeated by limiting searches to human, for peptides sourced by limiting searches to plasmodium species at step-4. 8-) Step-5 and 6 are performed with results of 7. Results: CFLGYFCTCYFGLFC peptide of SARS-CoV-2 has the highest identity to P. vivax. Its GYFCTCYFGLF and YFCTCYFGLF parts are predicted to bind strongly to HLA-A*24:02. Results obtained only for peptides homologous to YFCTCYFGLF, as follows: YYCARRFGLF, YYCHCPFGVF, and YYCQQYFFLF are potential HLA-A*24:02 epitopes in the human proteome. Among FFYTFYFELF, YFVACLFILF, and YFPTITFHLF peptides in the plasmodium species' proteomes with strong binding affinity to HLA-A*24:02, only FFYTFYFELF of P. falciparum is homologous to the potential HLA-A*24:02 epitope YFYLFSLELF in the human proteome. Conclusion: Immune responses to the identified-peptides with similar sequences and strong binding affinities to HLA-A*24:02 may lead to autoimmune response risk in individuals with HLA-A*24:02 serotypes, upon getting infected with SARS-CoV-2 or P. falciparum.
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection
Authors: Amir Abbasi, Erfan Miahi, Seyed Abolghasem Mirroshandel
Date: 2021-11-21 00:00:00
Tasks: Anomaly Detection, Multi-Task Learning, Transfer Learning

Analyzing the abnormality of morphological characteristics of male human sperm has been studied for a long time mainly because it has many implications on the male infertility problem, which accounts for approximately half of the infertility problems in the world. Yet, detecting such abnormalities by embryologists has several downsides. To clarify, analyzing sperms through visual inspection of an expert embryologist is a highly subjective and biased process. Furthermore, it takes much time for a specialist to make a diagnosis. Hence, in this paper, we proposed two deep learning algorithms that are able to automate this process. The first algorithm uses a network-based deep transfer learning approach, while the second technique, named Deep Multi-task Transfer Learning (DMTL), employs a novel combination of network-based deep transfer learning and multi-task learning to classify sperm's head, vacuole, and acrosome as either normal or abnormal. This DMTL technique is capable of classifying all the aforementioned parts of the sperm in a single prediction. Moreover, this is the first time that the concept of multi-task learning has been introduced to the field of Sperm Morphology Analysis (SMA). To benchmark our algorithms, we employed a freely-available SMA dataset named MHSMA. During our experiments, our algorithms reached the state-of-the-art results on the accuracy, precision, and f0.5, as well as other important metrics, such as the Matthews Correlation Coefficient on one, two, or all three labels. Notably, our algorithms increased the accuracy of the head, acrosome, and vacuole by 6.66%,3.00% , and 1.33%, and reached the accuracy of 84.00% ,80.66%, and 94.00% on these labels, respectively. Consequently, our algorithms can be used in health institutions, such as fertility clinics, with further recommendations to practically improve the performance of our algorithms.
A reproduction of Apple's bi-directional LSTM models for language identification in short strings
Authors: Mads Toftrup, Søren Asger Sørensen, Manuel R. Ciosici, Ira Assent
Date: 2021-02-11 00:00:00
Proceeding: EACL 2021 2
Tasks: Language Identification

Language Identification is the task of identifying a document's language. For applications like automatic spell checker selection, language identification must use very short strings such as text message fragments. In this work, we reproduce a language identification architecture that Apple briefly sketched in a blog post. We confirm the bi-LSTM model's performance and find that it outperforms current open-source language identifiers. We further find that its language identification mistakes are due to confusion between related languages.
CloTH-VTON: Clothing Three-dimensional reconstruction for Hybrid image-based Virtual Try-ON
Authors: Matiur Rahman Minar, Heejune Ahn
Date: 2020-11-30 00:00:00
Tasks: Image Generation, Virtual Try-on

Virtual clothing try-on, transferring a clothing image onto a target person image, is drawing industrial and research attention. Both 2D image-based and 3D model-based methods proposed recently have their benefits and limitations. Whereas 3D model-based methods provide realistic deformations of the clothing, it needs a difficult 3D model construction process and cannot handle the non-clothing areas well. Image-based deep neural network methods are good at generating disclosed human parts, retaining the unchanged area, and blending image parts, but cannot handle large deformation of clothing. In this paper, we propose CloTH-VTON that utilizes the high-quality image synthesis of 2D image-based methods and the 3D model-based deformation to the target human pose. For this 2D and 3D combination, we propose a novel 3D cloth reconstruction method from a single 2D cloth image, leveraging a 3D human body model, and transfer to the shape and pose of the target person. Our cloth reconstruction method can be easily applied to diverse cloth categories. Our method produces final try-on output with naturally deformed clothing and preserving details in high resolution.
A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages
Authors: Jivnesh Sandhan, Amrith Krishna, Ashim Gupta, Laxmidhar Behera, Pawan Goyal
Date: 2021-02-12 00:00:00
Proceeding: EACL 2021 2
Tasks: Dependency Parsing, Morphological Disambiguation

Neural dependency parsing has achieved remarkable performance for many domains and languages. The bottleneck of massive labeled data limits the effectiveness of these approaches for low resource languages. In this work, we focus on dependency parsing for morphological rich languages (MRLs) in a low-resource setting. Although morphological information is essential for the dependency parsing task, the morphological disambiguation and lack of powerful analyzers pose challenges to get this information for MRLs. To address these challenges, we propose simple auxiliary tasks for pretraining. We perform experiments on 10 MRLs in low-resource settings to measure the efficacy of our proposed pretraining method and observe an average absolute gain of 2 points (UAS) and 3.6 points (LAS). Code and data available at: https://github.com/jivnesh/LCM
Proximal and Federated Random Reshuffling
Authors: Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik
Date: 2021-02-12 00:00:00
Proceeding: NeurIPS 2021 12

Random Reshuffling (RR), also known as Stochastic Gradient Descent (SGD) without replacement, is a popular and theoretically grounded method for finite-sum minimization. We propose two new algorithms: Proximal and Federated Random Reshuffing (ProxRR and FedRR). The first algorithm, ProxRR, solves composite convex finite-sum minimization problems in which the objective is the sum of a (potentially non-smooth) convex regularizer and an average of $n$ smooth objectives. We obtain the second algorithm, FedRR, as a special case of ProxRR applied to a reformulation of distributed problems with either homogeneous or heterogeneous data. We study the algorithms' convergence properties with constant and decreasing stepsizes, and show that they have considerable advantages over Proximal and Local SGD. In particular, our methods have superior complexities and ProxRR evaluates the proximal operator once per epoch only. When the proximal operator is expensive to compute, this small difference makes ProxRR up to $n$ times faster than algorithms that evaluate the proximal operator in every iteration. We give examples of practical optimization tasks where the proximal operator is difficult to compute and ProxRR has a clear advantage. Finally, we corroborate our results with experiments on real data sets.
Explaining Neural Scaling Laws
Authors: Yasaman Bahri, Ethan Dyer, Jared Kaplan, Jaehoon Lee, Utkarsh Sharma
Date: 2021-02-12 00:00:00

The population loss of trained deep neural networks often follows precise power-law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains the origins of and connects these scaling laws. We identify variance-limited and resolution-limited scaling behavior for both dataset and model size, for a total of four scaling regimes. The variance-limited scaling follows simply from the existence of a well-behaved infinite data or infinite width limit, while the resolution-limited regime can be explained by positing that models are effectively resolving a smooth data manifold. In the large width limit, this can be equivalently obtained from the spectrum of certain kernels, and we present evidence that large width and large dataset resolution-limited scaling exponents are related by a duality. We exhibit all four scaling regimes in the controlled setting of large random feature and pretrained models and test the predictions empirically on a range of standard architectures and datasets. We also observe several empirical relationships between datasets and scaling exponents under modifications of task and architecture aspect ratio. Our work provides a taxonomy for classifying different scaling regimes, underscores that there can be different mechanisms driving improvements in loss, and lends insight into the microscopic origins of and relationships between scaling exponents.
On the Paradox of Certified Training
Authors: Nikola Jovanović, Mislav Balunović, Maximilian Baader, Martin Vechev
Date: 2021-02-12 00:00:00

Certified defenses based on convex relaxations are an established technique for training provably robust models. The key component is the choice of relaxation, varying from simple intervals to tight polyhedra. Counterintuitively, loose interval-based training often leads to higher certified robustness than what can be achieved with tighter relaxations, which is a well-known but poorly understood paradox. While recent works introduced various improvements aiming to circumvent this issue in practice, the fundamental problem of training models with high certified robustness remains unsolved. In this work, we investigate the underlying reasons behind the paradox and identify two key properties of relaxations, beyond tightness, that impact certified training dynamics: continuity and sensitivity. Our extensive experimental evaluation with a number of popular convex relaxations provides strong evidence that these factors can explain the drop in certified robustness observed for tighter relaxations. We also systematically explore modifications of existing relaxations and discover that improving unfavorable properties is challenging, as such attempts often harm other properties, revealing a complex tradeoff. Our findings represent an important first step towards understanding the intricate optimization challenges involved in certified training.
Matching Point Sets with Quantum Circuit Learning
Authors: Mohammadreza Noormandipour, Hanchen Wang
Date: 2021-02-12 00:00:00
Tasks: set matching

In this work, we propose a parameterised quantum circuit learning approach to point set matching problem. In contrast to previous annealing-based methods, we propose a quantum circuit-based framework whose parameters are optimised via descending the gradients w.r.t a kernel-based loss function. We formulate the shape matching problem into a distribution learning task; that is, to learn the distribution of the optimal transformation parameters. We show that this framework is able to find multiple optimal solutions for symmetric shapes and is more accurate, scalable and robust than the previous annealing-based method. Code, data and pre-trained weights are available at the project page: \href{https://hansen7.github.io/qKC}{https://hansen7.github.io/qKC}
Efficient Conditional GAN Transfer with Knowledge Propagation across Classes
Authors: Mohamad Shahbazi, Zhiwu Huang, Danda Pani Paudel, Ajad Chhatkuli, Luc van Gool
Date: 2021-02-12 00:00:00
Proceeding: CVPR 2021 1
Tasks: Conditional Image Generation, Image Generation, Transfer Learning

Generative adversarial networks (GANs) have shown impressive results in both unconditional and conditional image generation. In recent literature, it is shown that pre-trained GANs, on a different dataset, can be transferred to improve the image generation from a small target data. The same, however, has not been well-studied in the case of conditional GANs (cGANs), which provides new opportunities for knowledge transfer compared to unconditional setup. In particular, the new classes may borrow knowledge from the related old classes, or share knowledge among themselves to improve the training. This motivates us to study the problem of efficient conditional GAN transfer with knowledge propagation across classes. To address this problem, we introduce a new GAN transfer method to explicitly propagate the knowledge from the old classes to the new classes. The key idea is to enforce the popularly used conditional batch normalization (BN) to learn the class-specific information of the new classes from that of the old classes, with implicit knowledge sharing among the new ones. This allows for an efficient knowledge propagation from the old classes to the new ones, with the BN parameters increasing linearly with the number of new classes. The extensive evaluation demonstrates the clear superiority of the proposed method over state-of-the-art competitors for efficient conditional GAN transfer tasks. The code is available at: https://github.com/mshahbazi72/cGANTransfer