pwc-archive/papers-with-abstracts huggingface dataset:
Voluntary control of semantic neural representations by imagery with conflicting visual stimulation
Authors: Ryohei Fukuma, Takufumi Yanagisawa, Shinji Nishimoto, Hidenori Sugano, Kentaro Tamura, Shota Yamamoto, Yasushi Iimura, Yuya Fujita, Satoru Oshino, Naoki Tani, Naoko Koide-Majima, Yukiyasu Kamitani, Haruhiko Kishima
Date: 2021-11-07 00:00:00

Neural representations of visual perception are affected by mental imagery and attention. Although attention is known to modulate neural representations, it is unknown how imagery changes neural representations when imagined and perceived images semantically conflict. We hypothesized that imagining an image would activate a neural representation during its perception even while watching a conflicting image. To test this hypothesis, we developed a closed-loop system to show images inferred from electrocorticograms using a visual semantic space. The successful control of the feedback images demonstrated that the semantic vector inferred from electrocorticograms became closer to the vector of the imagined category, even while watching images from different categories. Moreover, modulation of the inferred vectors by mental imagery depended on both the image category and time from the initiation of imagery. The closed-loop control of the semantic vectors revealed an asymmetrical interaction between visual perception and imagery.
Attaining scalable storage-expansion dualism for bioartificial tissues
Authors: Sammy Sambu
Date: 2021-12-02 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

The untenable dependence of cryopreservation on cytotoxic cryoprotectants has motivated the mining of biochemical libraries to identify molecular features marking cryoprotectants that may prevent ice crystal growth. It is hypothesized that such molecules may be useful across all temperatures eliminating cellular destruction due to equilibration cytotoxicity before and after cryopreservation. By probing the biochemical space using solvation-associated molecular topology and partition-distribution measures we developed an analytic formalism for ice crystal inhibition. By probing the union between a heat-shock protein cluster and an anti-freeze glycoprotein cluster, the model development process generated distinct regions of interest for anti-freeze glycoprotein and proved robust across different classes of proteins. These results confirm that there is a chemical space within which efficacious Ice Crystal Inhibitor molecular libraries can be constructed. These spaces contain latent projections drawn from solvent accessibility, hydrogen bonding capacity and molecular geometry. They also showed that molecular design can be a useful tool in the development of new-age cryoactive matrices to enable the smooth transition across culture, preservation and expansion. These chemical spaces provide low cytotoxicity since such amphipathic molecules occupy a continuum between solubizing and membrane stabilizing regions as shown by the free energy of translocation calculations. These biochemical design spaces are fundamentally the solution to efficient scale-up and deployment of cell therapies. Consequently, this article proposes the use of a molecular knowledge-mining approach in the development of a class of non-cytotoxic cryoprotective agents in the class of Ice Crystal Inhibitor compatible with continuous cryothermic and normothermic cell storage and expansion.
The effects of local homogeneity assumptions in metapopulation models of infectious disease
Authors: Cameron Zachreson, Sheryl Chang, Nathan Harding, Mikhail Prokopenko
Date: 2021-12-02 00:00:00

Computational models of infectious disease can be broadly categorized into two types: individual-based (Agent-based), or compartmental models. While compartmental models can be structured to separate distinct sectors of a population, they are conceptually distinct from individual-based models in which population structure emerges from micro-scale interactions. While the conceptual distinction is straightforward, a fair comparison of the approaches is difficult to achieve. Here, we carry out such a comparison by building a set of compartmental metapopulation models from an agent-based representation of a real population. By adjusting the compartmental model to approximately match the dynamics of the Agent-based model, we identify two key qualitative properties of the individual-based dynamics which are lost upon aggregation into metapopulations. These are (1) the local depletion of susceptibility to infection, and (2) decoupling of different regional groups due to correlation between commuting behaviors and contact rates. The first of these effects is a general consequence of aggregating small, closely connected groups (i.e., families) into larger homogeneous metapopulations. The second can be interpreted as a consequence of aggregating two distinct types of individuals: school children, who travel short distances but have many potentially infectious contacts, and adults, who travel further but tend to have fewer contacts capable of transmitting infection. Our results could be generalised to other types of correlations between the characteristics of individuals and the behaviors that distinguish them.
Omicron (B.1.1.529): Infectivity, vaccine breakthrough, and antibody resistance
Authors: Jiahui Chen, Rui Wang, Nancy Benovich Gilby, Guo-Wei Wei
Date: 2021-12-01 00:00:00

The latest severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant Omicron (B.1.1.529) has ushered panic responses around the world due to its contagious and vaccine escape mutations. The essential infectivity and antibody resistance of the SARS-CoV-2 variant are determined by its mutations on the spike (S) protein receptor-binding domain (RBD). However, a complete experimental evaluation of Omicron might take weeks or even months. Here, we present a comprehensive quantitative analysis of Omicron's infectivity, vaccine-breakthrough, and antibody resistance. An artificial intelligence (AI) model, which has been trained with tens of thousands of experimental data points and extensively validated by experimental data on SARS-CoV-2, reveals that Omicron may be over ten times more contagious than the original virus or about twice as infectious as the Delta variant. Based on 132 three-dimensional (3D) structures of antibody-RBD complexes, we unveil that Omicron may be twice more likely to escape current vaccines than the Delta variant. The Food and Drug Administration (FDA)-approved monoclonal antibodies (mAbs) from Eli Lilly may be seriously compromised. Omicron may also diminish the efficacy of mAbs from Celltrion and Rockefeller University. However, its impact on Regeneron mAb cocktail appears to be mild.
Method of lines for valuation and sensitivities of Bermudan options
Authors: Purba Banerjee, Vasudeva Murthy, Shashi Jain
Date: 2021-12-02 00:00:00

In this paper, we present a computationally efficient technique based on the \emph{Method of Lines} (MOL) for the approximation of the Bermudan option values via the associated partial differential equations (PDEs). The MOL converts the Black Scholes PDE to a system of ordinary differential equations (ODEs). The solution of the system of ODEs so obtained only requires spatial discretization and avoids discretization in time. Additionally, the exact solution of the ODEs can be obtained efficiently using the exponential matrix operation, making the method computationally attractive and straightforward to implement. An essential advantage of the proposed approach is that the associated Greeks can be computed with minimal additional computations. We illustrate, through numerical experiments, the efficacy of the proposed method in pricing and computation of the sensitivities for a European call, cash-or-nothing, powered option, and Bermudan put option.
A Feedback Integrated Web-Based Multi-Criteria Group Decision Support Model for Contractor Selection using Fuzzy Analytic Hierarchy Process
Authors: Abimbola Helen Afolayan, Bolanle Adefowoke Ojokoh, Adebayo Adetunmbi
Date: 2021-11-19 00:00:00

In this paper, a feedback integrated multi-criteria group decision support model for contractor selection was proposed.
Onssen: an open-source speech separation and enhancement library
Authors: Zhaoheng Ni, Michael I Mandel
Date: 2019-11-03 00:00:00
Tasks: Deep Clustering, speech-recognition, Speech Recognition, Speech Separation

Speech separation is an essential task for multi-talker speech recognition. Recently many deep learning approaches are proposed and have been constantly refreshing the state-of-the-art performances. The lack of algorithm implementations limits researchers to use the same dataset for comparison. Building a generic platform can benefit researchers by easily implementing novel separation algorithms and comparing them with the existing ones on customized datasets. We introduce "onssen": an open-source speech separation and enhancement library. onssen is a library mainly for deep learning separation and enhancement algorithms. It uses LibRosa and NumPy libraries for the feature extraction and PyTorch as the back-end for model training. onssen supports most of the Time-Frequency mask-based separation algorithms (e.g. deep clustering, chimera net, chimera++, and so on) and also supports customized datasets. In this paper, we describe the functionality of modules in onssen and show the algorithms implemented by onssen achieve the same performances as reported in the original papers.
Adaptive Drift-Diffusion Process to Learn Time Intervals
Authors: Francois Rivest, Yoshua Bengio
Date: 2011-03-11 00:00:00

Animals learn the timing between consecutive events very easily. Their precision is usually proportional to the interval to time (Weber's law for timing). Most current timing models either require a central clock and unbounded accumulator or whole pre-defined populations of delay lines, decaying traces or oscillators to represent elapsing time. Current adaptive recurrent neural networks fail at learning to predict the timing of future events (the 'when') in a realistic manner. In this paper, we present a new model of interval timing, based on simple temporal integrators, derived from drift-diffusion models. We develop a simple geometric rule to learn 'when' instead of 'what'. We provide an analytical proof that the model can learn inter-event intervals in a number of trials independent of the interval size and that the temporal precision of the system is proportional to the timed interval. This new model uses no clock, no gradient, no unbounded accumulators, no delay lines, and has internal noise allowing generations of individual trials. Three interesting predictions are made.
PLUTO: a Numerical Code for Computational Astrophysics
Authors: A. Mignone, G. Bodo, S. Massaglia, T. Matsakos, O. Tesileanu, C. Zanni, A. Ferrari
Date: 2007-01-30 00:00:00

We present a new numerical code, PLUTO, for the solution of hypersonic flows in 1, 2 and 3 spatial dimensions and different systems of coordinates. The code provides a multi-physics, multi-algorithm modular environment particularly oriented towards the treatment of astrophysical flows in presence of discontinuities. Different hydrodynamic modules and algorithms may be independently selected to properly describe Newtonian, relativistic, MHD or relativistic MHD fluids. The modular structure exploits a general framework for integrating a system of conservation laws, built on modern Godunov-type shock-capturing schemes. Although a plethora of numerical methods has been successfully developed over the past two decades, the vast majority shares a common discretization recipe, involving three general steps: a piecewise polynomial reconstruction followed by the solution of Riemann problems at zone interfaces and a final evolution stage. We have checked and validated the code against several benchmarks available in literature. Test problems in 1, 2 and 3 dimensions are discussed.
Crossed-Time Delay Neural Network for Speaker Recognition
Authors: Liang Chen, Yanchun Liang, Xiaohu Shi, You Zhou, Chunguo Wu
Date: 2020-05-31 00:00:00
Tasks: Speaker Recognition, Speaker Verification

Time Delay Neural Network (TDNN) is a well-performing structure for DNN-based speaker recognition systems. In this paper we introduce a novel structure Crossed-Time Delay Neural Network (CTDNN) to enhance the performance of current TDNN. Inspired by the multi-filters setting of convolution layer from convolution neural network, we set multiple time delay units each with different context size at the bottom layer and construct a multilayer parallel network. The proposed CTDNN gives significant improvements over original TDNN on both speaker verification and identification tasks. It outperforms in VoxCeleb1 dataset in verification experiment with a 2.6% absolute Equal Error Rate improvement. In few shots condition CTDNN reaches 90.4% identification accuracy, which doubles the identification accuracy of original TDNN. We also compare the proposed CTDNN with another new variant of TDNN, FTDNN, which shows that our model has a 36% absolute identification accuracy improvement under few shots condition and can better handle training of a larger batch in a shorter training time, which better utilize the calculation resources. The code of the new model is released at https://github.com/chenllliang/CTDNN
CSVC-Net: Code-Switched Voice Command Classification using Deep CNN-LSTM Network
Authors: Arowa Yasmeen, Fariha Ishrat Rahman, Sabbir Ahmed, Md. Hasanul Kabir
Date: 2021-08-17 00:00:00
Proceeding: International Conference on Informatics, Electronics & Vision (ICIEV) 2021 8
Tasks: Voice Query Recognition

Colloquial Bengali has adopted many English words due to colonial influence. In conversational Bengali, it is quite common to speak in a mixture of English and Bengali, a phenomenon termed Code-switching (CS). To build a Voice Command Classifier in this era, when the usage of CS is ever-increasing, it is often necessary to map a single base command to its many different variants - spoken in multiple mixtures of languages. The works done with Bengali Speech have been primarily focused on single word classification and mostly incompetent in understanding the complex semantic relationships displayed in sentences. This paper proposes ‘CSVC-Net’, a CNN-LSTM based architecture for classifying spoken commands that exhibit code-switching between Bengali and English. To effectively reflect the scenario, it also presents a newly curated dataset named ‘Banglish’ containing 3,840 audio files of spoken computer commands belonging to 11 classes, considering 64 variations in total. The proposed pipeline passes the input audio signal through a series of appropriate transformation and augmentation steps enabling the model to achieve an accuracy of 92.08% on the curated dataset. Furthermore, the robustness of the proposed model has been justified by comparing with different architectures and tested under different noise levels with promising accuracy, which shows the applicability of the model in real-life scenarios.
Self-organizing traffic lights: A realistic simulation
Authors: Seung-Bae Cools, Carlos Gershenson, Bart D'Hooghe
Date: 2006-10-18 00:00:00

We have previously shown in an abstract simulation (Gershenson, 2005) that self-organizing traffic lights can improve greatly traffic flow for any density. In this paper, we extend these results to a realistic setting, implementing self-organizing traffic lights in an advanced traffic simulator using real data from a Brussels avenue. On average, for different traffic densities, travel waiting times are reduced by 50% compared to the current green wave method.
A Knowledge Compilation Map
Authors: A. Darwiche, P. Marquis
Date: 2011-06-09 00:00:00

We propose a perspective on knowledge compilation which calls for analyzing different compilation approaches according to two key dimensions: the succinctness of the target compilation language, and the class of queries and transformations that the language supports in polytime. We then provide a knowledge compilation map, which analyzes a large number of existing target compilation languages according to their succinctness and their polytime transformations and queries. We argue that such analysis is necessary for placing new compilation approaches within the context of existing ones. We also go beyond classical, flat target compilation languages based on CNF and DNF, and consider a richer, nested class based on directed acyclic graphs (such as OBDDs), which we show to include a relatively large number of target compilation languages.
MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients
Authors: Mohammad Shorfuzzaman, M. ShamimHossain
Date: 2020-10-17 00:00:00
Proceeding: Pattern Recognition 2020 10
Tasks: Contrastive Learning, COVID-19 Diagnosis, Meta-Learning, Pneumonia Detection

Various AI functionalities such as pattern recognition and prediction can effectively be used to diagnose (recognize) and predict coronavirus disease 2019 (COVID-19) infections and propose timely response (remedial action) to minimize the spread and impact of the virus. Motivated by this, an AI system based on deep meta learning has been proposed in this research to accelerate analysis of chest X-ray (CXR) images in automatic detection of COVID-19 cases. We present a synergistic approach to integrate contrastive learning with a fine-tuned pre-trained ConvNet encoder to capture unbiased feature representations and leverage a Siamese network for final classification of COVID-19 cases. We validate the effectiveness of our proposed model using two publicly available datasets comprising images from normal, COVID-19 and other pneumonia infected categories. Our model achieves 95.6% accuracy and AUC of 0.97 in diagnosing COVID-19 from CXR images even with a limited number of training samples.
Multi-Armed Bandits in Metric Spaces
Authors: Robert Kleinberg, Aleksandrs Slivkins, Eli Upfal
Date: 2008-09-29 00:00:00
Tasks: Multi-Armed Bandits

In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a sequence of trials so as to maximize the total payoff of the chosen strategies. While the performance of bandit algorithms with a small finite strategy set is quite well understood, bandit problems with large strategy sets are still a topic of very active investigation, motivated by practical applications such as online auctions and web advertisement. The goal of such research is to identify broad and natural classes of strategy sets and payoff functions which enable the design of efficient solutions. In this work we study a very general setting for the multi-armed bandit problem in which the strategies form a metric space, and the payoff function satisfies a Lipschitz condition with respect to the metric. We refer to this problem as the "Lipschitz MAB problem". We present a complete solution for the multi-armed problem in this setting. That is, for every metric space (L,X) we define an isometry invariant which bounds from below the performance of Lipschitz MAB algorithms for X, and we present an algorithm which comes arbitrarily close to meeting this bound. Furthermore, our technique gives even better results for benign payoff functions.
Efficient Two-Stage Detection of Human-Object Interactions with a Novel Unary-Pairwise Transformer
Authors: Frederic Z. Zhang, Dylan Campbell, Stephen Gould
Date: 2021-12-03 00:00:00
Proceeding: CVPR 2022 1
Tasks: GPU, Human-Object Interaction Detection

Recent developments in transformer models for visual data have led to significant improvements in recognition and detection tasks. In particular, using learnable queries in place of region proposals has given rise to a new class of one-stage detection models, spearheaded by the Detection Transformer (DETR). Variations on this one-stage approach have since dominated human-object interaction (HOI) detection. However, the success of such one-stage HOI detectors can largely be attributed to the representation power of transformers. We discovered that when equipped with the same transformer, their two-stage counterparts can be more performant and memory-efficient, while taking a fraction of the time to train. In this work, we propose the Unary-Pairwise Transformer, a two-stage detector that exploits unary and pairwise representations for HOIs. We observe that the unary and pairwise parts of our transformer network specialise, with the former preferentially increasing the scores of positive examples and the latter decreasing the scores of negative examples. We evaluate our method on the HICO-DET and V-COCO datasets, and significantly outperform state-of-the-art approaches. At inference time, our model with ResNet50 approaches real-time performance on a single GPU.
Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images
Authors: Sang-gil Lee, Eunji Kim, Jae Seok Bae, Jung Hoon Kim, Sungroh Yoon
Date: 2021-12-02 00:00:00
Tasks: Automatic Liver And Tumor Segmentation, Computed Tomography (CT), Lesion Detection, Liver Segmentation, Medical Image Registration, Medical Object Detection, Object Detection

The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosis. Despite the recent success of deep-learning-based approaches in detecting FLLs, current methods are not sufficiently robust for assessing misaligned multiphase data. By introducing an attention-guided multiphase alignment in feature space, this study presents a fully automated, end-to-end learning framework for detecting FLLs from multiphase computed tomography (CT) images. Our method is robust to misaligned multiphase images owing to its complete learning-based approach, which reduces the sensitivity of the model's performance to the quality of registration and enables a standalone deployment of the model in clinical practice. Evaluation on a large-scale dataset with 280 patients confirmed that our method outperformed previous state-of-the-art methods and significantly reduced the performance degradation for detecting FLLs using misaligned multiphase CT images. The robustness of the proposed method can enhance the clinical adoption of the deep-learning-based computer-aided detection system.
TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs
Authors: yuke wang, Boyuan Feng, Zheng Wang, Guyue Huang, Yufei Ding
Date: 2021-12-03 00:00:00
Tasks: GPU, Translation

Recently, graph neural networks (GNNs), as the backbone of graph-based machine learning, demonstrate great success in various domains (e.g., e-commerce). However, the performance of GNNs is usually unsatisfactory due to the highly sparse and irregular graph-based operations. To this end, we propose TC-GNN, the first GNN acceleration framework based on GPU Tensor Core Units (TCUs). The core idea is to reconcile the "Sparse" GNN computation with the high-performance "Dense" TCUs. Specifically, we conduct an in-depth analysis of the sparse operations in mainstream GNN computing frameworks. We introduce a novel sparse graph translation technique to facilitate TCU processing of the sparse GNN workload. We implement an effective CUDA core and TCU collaboration design to fully utilize GPU resources. We integrate TC-GNN with the PyTorch framework for high programmability. Rigorous experiments show an average of 1.70X speedup over the state-of-the-art DGL framework across various models and datasets.