pwc-archive/papers-with-abstracts huggingface dataset:
Evolutionary Multi-Objective Optimization Algorithm Framework with Three Solution Sets
Authors: Hisao Ishibuchi, Lie Meng Pang, Ke Shang
Date: 2020-12-14 00:00:00

It is assumed in the evolutionary multi-objective optimization (EMO) community that a final solution is selected by a decision maker from a non-dominated solution set obtained by an EMO algorithm. The number of solutions to be presented to the decision maker can be totally different. In some cases, the decision maker may want to examine only a few representative solutions from which a final solution is selected. In other cases, a large number of non-dominated solutions may be needed to visualize the Pareto front. In this paper, we suggest the use of a general EMO framework with three solution sets to handle various situations with respect to the required number of solutions. The three solution sets are the main population of an EMO algorithm, an external archive to store promising solutions, and a final solution set which is presented to the decision maker. The final solution set is selected from the archive. Thus the population size and the archive size can be arbitrarily specified as long as the archive size is not smaller than the required number of solutions. The final population is not necessarily to be a good solution set since it is not presented to the decision maker. Through computational experiments, we show the advantages of this framework over the standard final population and final archive frameworks. We also discuss how to select a final solution set and how to explain the reason for the selection, which is the first attempt towards an explainable EMO framework.
Low-Order Model of Biological Neural Networks
Authors: Huachuan Wang, James Ting-Ho Lo
Date: 2020-12-12 00:00:00
Tasks: model

A biologically plausible low-order model (LOM) of biological neural networks is a recurrent hierarchical network of dendritic nodes/trees, spiking/nonspiking neurons, unsupervised/ supervised covariance/accumulative learning mechanisms, feedback connections, and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect and recognize multiple/hierarchical corrupted, distorted, and occluded temporal and spatial patterns.
Distributed Model Predictive Control for Heterogeneous V ehicle Platoons Under Unidirectional Topologies
Authors: Y ang Zheng, Shengbo Eben Li, Keqiang Li, Francesco Borrelli
Date: 2017-03-03 00:00:00
Tasks: Model Predictive Control

This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a p r i o r i unknown desired set point. The vehicles (or nodes) in a platoon are dynamically decoupled but constrained by spatial geometry. Each node is assigned a local open-loop optimal control problem only relying on the information of neighboring nodes, in which the cost function is designed by penalizing on the errors between the predicted and assumed trajectories. Together with this penalization, an equalitybased terminal constraint is proposed to ensure stability, which enforces the terminal states of each node in the predictive horizon equal to the average of its neighboring states. By using the sum of local cost functions as a Lyapunov candidate, it is proved that asymptotic stability of such a DMPC can be achieved through an explicit sufficient condition on the weights of the cost functions. Simulations with passenger cars demonstrate the effectiveness of the proposed DMPC.
Throwing Darts in the Dark? Detecting Bots with Limited Data using Neural Data Augmentation
Authors: Steve T.K. Jan, Qingying Hao, Tianrui Hu, Jiameng Pu, Sonal Oswal, Gang Wang, Bimal Viswanath
Date: 2020-05-17 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation, Generative Adversarial Network

Abstract—Machine learning has been widely applied to building security applications. However, many machine learning models require the continuous supply of representative labeled data for training, which limits the models’ usefulness in practice. In this paper, we use bot detection as an example to explore the use of data synthesis to address this problem. We collected the
network traffic from 3 online services in three different months within a year (23 million network requests). We develop a streambased feature encoding scheme to support machine learning models for detecting advanced bots. The key novelty is that
our model detects bots with extremely limited labeled data. We propose a data synthesis method to synthesize unseen (or future)
bot behavior distributions. The synthesis method is distributionaware, using two different generators in a Generative Adversarial Network to synthesize data for the clustered regions and the outlier regions in the feature space. We evaluate this idea and show our method can train a model that outperforms existing methods with only 1% of the labeled data. We show that data synthesis also improves the model’s sustainability over time and speeds up the retraining. Finally, we compare data synthesis and adversarial retraining and show they can work complementary with each other to improve the model generalizability
Object-aware Multimodal Named Entity Recognition in Social Media Posts with Adversarial Learning
Authors: Changmeng Zheng, Zhiwei Wu, Tao Wang, Cai Yi, Qing Li
Date: 2020-08-03 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named Entity Recognition (NER) in social media posts is challenging since texts are usually short and contexts are lacking. Most recent works show that visual information can boost the NER performance since images can provide complementary contextual information for texts. However, the image-level features ignore the mapping relations between fine-grained visual objects and textual entities, which results in error detection in entities with different types. To better exploit visual and textual information in NER, we propose an adversarial gated bilinear attention neural network (AGBAN). The model jointly extracts entity-related features from both visual objects and texts, and leverages an adversarial training to map two different representations into a shared representation. As a result, domain information contained in an image can be transferred and applied for extracting named entities in the text associated with the image. Experimental results on Tweets dataset demonstrate that our model outperforms the state-of-the-art methods. Moreover, we systematically evaluate the effectiveness of the proposed gated bilinear attention network in capturing the interactions of mutimodal features visual objects and textual words. Our results indicate that the adversarial training can effectively exploit commonalities across heterogeneous data sources, which leads to improved performance in NER when compared to models purely exploiting text data or combining the image-level visual features.
Image Inpainting with Onion Convolutions
Authors: Shant Navasardyan, Marianna Ohanyan
Date: 2020-12-04 00:00:00
Tasks: Image Inpainting

Recently deep learning methods have achieved a great success in image inpainting problem. However, reconstructing continuities of complex structures with non-stationary textures remains a challenging task for computer vision. In this paper, a novel approach to image inpainting problem is presented, which adapts exemplar-based methods for deep convolutional neural networks. The concept of onion convolution is introduced with the purpose of preserving feature continuities and semantic coherence. Similar to recent approaches, our onion convolution is able to capture long-range spatial correlations. In general, the implementation of modules with such ability in low-level features leads to impractically high latency and complexity. To address this limitations, the onion convolution suggests an efficient implementation. As qualitative and quantitative comparisons show, our method with onion convolutions outperforms state-of-the-art methods by producing more realistic, visually plausible and semantically coherent results.
Reconstructing unseen modalities and pathology with an efficient Recurrent Inference Machine
Authors: Dimitrios Karkalousos, Kai Lønning, Hanneke E. Hulst, Serge O. Dumoulin, Jan-Jakob Sonke, Frans M. Vos, Matthan W. A. Caan
Date: 2020-12-14 00:00:00
Tasks: compressed sensing, Image Reconstruction, MRI Reconstruction

Objective: To allow efficient learning using the Recurrent Inference Machine (RIM) for image reconstruction whereas not being strictly dependent on the training data distribution so that unseen modalities and pathologies are still accurately recovered. Methods: Theoretically, the RIM learns to solve the inverse problem of accelerated-MRI reconstruction whereas being robust to variable imaging conditions. The efficiency and generalization capabilities with different training datasets were studied, as well as recurrent network units with decreasing complexity: the Gated Recurrent Unit (GRU), the Minimal Gated Unit (MGU), and the Independently Recurrent Neural Network (IndRNN), to reduce inference times. Validation was performed against Compressed Sensing (CS) and further assessed based on data unseen during training. A pathology study was conducted by reconstructing simulated white matter lesions and prospectively undersampled data of a Multiple Sclerosis patient. Results: Training on a single modality of 3T $T_1$-weighted brain data appeared sufficient to also reconstruct 7T $T_{2}^*$-weighted brain and 3T $T_2$-weighted knee data. The IndRNN is an efficient recurrent unit, reducing inference time by 68\% compared to CS, whereas maintaining performance. The RIM was able to reconstruct lesions unseen during training more accurately than CS when trained on $T_2$-weighted knee data. Training on $T_1$-weighted brain data and on combined data slightly enhanced the signal compared to CS. Conclusion: The RIM is efficient when decreasing its complexity, which reduces the inference time, whereas still being able to reconstruct data and pathology that was unseen during training.
A Low-Loss 1-4 GHz Optically-Controlled Silicon Plasma Switch
Authors: Alden Fisher, Zach Vander Missen, Abbas Semnani, Dimitrios Peroulis
Date: 2020-12-14 00:00:00

This paper presents a low-loss optically-controlled inline RF switch suitable for L- and S-band applications. Under 1.5 W laser power, the switch exhibits a measured ON-state insertion loss of less than 0.33 dB and return loss better than 20 dB across the band. The measured OFF-state isolation ranges from 27 dB at 1 GHz to 17 dB at 4 GHz. The switch comprises a single silicon chiplet excited by a 915-nm laser fiber which creates electron-hole pairs, thereby exciting the ON-state silicon plasma. An optical fiber is guided through the bottom of the RF substrate to illuminate the chiplet, which bridges a 1.075-mm microstrip line gap. To the best of our knowledge, this is the lowest-loss silicon plasma switch demonstrated today.
A simple microstructural explanation of the concavity of price impact
Authors: Sergey Nadtochiy
Date: 2020-01-07 00:00:00

This article provides a simple explanation of the asymptotic concavity of the price impact of a meta-order via the microstructural properties of the market. This explanation is made more precise by a model in which the local relationship between the order flow and the fundamental price (i.e. the local price impact) is linear, with a constant slope, which makes the model dynamically consistent. Nevertheless, the expected impact on midprice from a large sequence of co-directional trades is nonlinear and asymptotically concave. The main practical conclusion of the proposed explanation is that, throughout a meta-order, the volumes at the best bid and ask prices change (on average) in favor of the executor. This conclusion, in turn, relies on two more concrete predictions, one of which can be tested, at least for large-tick stocks, using publicly available market data.
The economics of stop-and-go epidemic control
Authors: Claudius Gros, Daniel Gros
Date: 2020-12-14 00:00:00

We analyse 'stop-and-go' containment policies that produce infection cycles as periods of tight lockdowns are followed by periods of falling infection rates. The subsequent relaxation of containment measures allows cases to increase again until another lockdown is imposed and the cycle repeats. The policies followed by several European countries during the Covid-19 pandemic seem to fit this pattern. We show that 'stop-and-go' should lead to lower medical costs than keeping infections at the midpoint between the highs and lows produced by 'stop-and-go'. Increasing the upper and reducing the lower limits of a stop-and-go policy by the same amount would lower the average medical load. But increasing the upper and lowering the lower limit while keeping the geometric average constant would have the opposite effect. We also show that with economic costs proportional to containment, any path that brings infections back to the original level (technically a closed cycle) has the same overall economic cost.
Numerical study of solar cells based on ZnSnN2 structure
Authors: A.Laidouci
Date: 2020-11-15 00:00:00

Based on several semiconductor research, we have studied one of the new semiconductors, due to its exciting physical proprieties that in turn solve some problems in the photovoltaic’s industry. We have divided this work into two parts. The first part, is to study the optical proprieties of ZnSnN2, as calculation of the absorption coefficient, bandgap, reflection coefficient, and transmission coefficient. As for the second part, a study of some parameters that affect the efficiency of p-CuCrO2 (CCO)/ n-ZnSnN2 (ZTN) solar cell using SCAPS-1D software, and our results are compared with the results of another software (wxAMPS), where results showed great compatibility with the presence of weak uncertainty. We have studied the influence of many parameters such as the thickness of the absorber layer (n-ZnSnN2), the thickness of the buffer layer (p-CuCrO2), the temperature, the series resistance (Rs), the shunt resistance (Rsh) and the defect density on the performance of ZnSnN2 solar cells which included in the real device. The photovoltaic parameters have been calculated using AM1.5G solar irradiance at the intensity of one sun, a temperature of 300 K, and considering the flat band condition at the interface. We have been achieving a high-efficiency of η ≈ 22% without defects. Given the characteristics of this new semiconductor ZnSnN2, which composed of earth-abundant, non-toxic and inexpensive element, as well as a high absorption coefficient, it can be considered as an alternative for PV and optoelectronic applications.
Simulation and Optimization of Cds/ZnSnN 2 Structure for Solar Cell Applications with SCAPS-1D Software
Authors: A.Laidouci
Date: 2020-08-15 00:00:00

In this paper, we are interested in simulating and modeling of Cds/ZnSnN2 structure for a solar cell using SCAPS-1D. The ZnSnN2 is considered as one of the promising absorber materials for photovoltaic application due to the high optical efficiency and the low cost. In the present work, we have investigated the effects of the thickness of the buffer and the absorber layers, the temperature on electrical parameters (Voc) the open-circuit voltage and (Jsc) the short-circuit current density, (FF) fill factor and (η) efficiency of the solar cell. The results show a remarkable improved of the efficiency a η = 26.49% under the AM1.5G spectrum, one sun and deformation of 0.51% between the Cds and the ZnSnN2. The achieved results show that the ZnSnN2 is a very promising material for thin film photovoltaics and offers a number of interesting advantages compared to (CIGS) and (CZTS) due to its high efficiency, earth-abundant, non-toxic and inexpensive element.
Temperature Effect on ZnGeN2/GaN Multiwell Quantum Solar Cells
Authors: A.Laidouci
Date: 2018-12-05 00:00:00

In this paper, we are interested in simulations and modeling of ZnGeN2/GaN quantum wells solar cell, like all other solar cells as we know always are sensitive to the temperature, many effects like number of quantum wells (NQWs), temperature (T) on photovoltaic properties such as current density-voltage I(V), power density-voltage P(V) and the external quantum efficiency (EQE) are investigated. The most parameter affected by the change in temperature is the open circuit voltage (V oc ). We found in a temperature of 280 K gives better results of open circuit voltage (V oc ), maximum power output (P m ), and conversion efficiency (η) their values are 2.87 V, 21.12 mW/cm 2 , and 21.22% respectively.
The structure of multiplex networks predicts play in economic games and real-world cooperation
Authors: Curtis Atkisson, Monique Borgerhoff Mulder
Date: 2020-12-14 00:00:00
Tasks: Sociology

Explaining why humans cooperate in anonymous contexts is a major goal of human behavioral ecology, cultural evolution, and related fields. What predicts cooperation in anonymous contexts is inconsistent across populations, levels of analysis, and games. For instance, market integration is a key predictor across ethnolinguistic groups but has inconsistent predictive power at the individual level. We adapt an idea from 19th-century sociology: people in societies with greater overlap in ties across domains among community members (Durkheim's "mechanical" solidarity) will cooperate more with their network partners and less in anonymous contexts than people in societies with less overlap ("organic" solidarity). This hypothesis, which can be tested at the individual and community level, assumes that these two types of societies differ in the importance of keeping existing relationships as opposed to recruiting new partners. Using multiplex networks, we test this idea by comparing cooperative tendencies in both anonymous experimental games and real-life communal labor tasks across 9 Makushi villages in Guyana that vary in the degree of within-village overlap. Average overlap in a village predicts both real-world cooperative and anonymous interactions in the predicted direction; individual overlap also has effects in the expected direction. These results reveal a consistent patterning of cooperative tendencies at both individual and local levels and contribute to the debate over the emergence of norms for cooperation among humans. Multiplex overlap can help us understand inconsistencies in previous studies of cooperation in anonymous contexts and is an unexplored dimension with explanatory power at multiple levels of analysis.
Welfare Analysis via Marginal Treatment Effects
Authors: Yuya Sasaki, Takuya Ura
Date: 2020-12-14 00:00:00

Consider a causal structure with endogeneity (i.e., unobserved confoundedness) in empirical data, where an instrumental variable is available. In this setting, we show that the mean social welfare function can be identified and represented via the marginal treatment effect (MTE, Bjorklund and Moffitt, 1987) as the operator kernel. This representation result can be applied to a variety of statistical decision rules for treatment choice, including plug-in rules, Bayes rules, and empirical welfare maximization (EWM) rules as in Hirano and Porter (2020, Section 2.3). Focusing on the application to the EWM framework of Kitagawa and Tetenov (2018), we provide convergence rates of the worst case average welfare loss (regret) in the spirit of Manski (2004).
NAPA: Neural Art Human Pose Amplifier
Authors: Qingfu Wan, Oliver Lu
Date: 2020-12-15 00:00:00
Tasks: Pose Estimation, regression, Style Transfer

This is the project report for CSCI-GA.2271-001. We target human pose estimation in artistic images. For this goal, we design an end-to-end system that uses neural style transfer for pose regression. We collect a 277-style set for arbitrary style transfer and build an artistic 281-image test set. We directly run pose regression on the test set and show promising results. For pose regression, we propose a 2d-induced bone map from which pose is lifted. To help such a lifting, we additionally annotate the pseudo 3d labels of the full in-the-wild MPII dataset. Further, we append another style transfer as self supervision to improve 2d. We perform extensive ablation studies to analyze the introduced features. We also compare end-to-end with per-style training and allude to the tradeoff between style transfer and pose regression. Lastly, we generalize our model to the real-world human dataset and show its potentiality as a generic pose model. We explain the theoretical foundation in Appendix. We release code at https://github.com/strawberryfg/NAPA-NST-HPE, data, and video.
Classification of Smoking and Calling using Deep Learning
Authors: Miaowei Wang, Alexander William Mohacey, Hongyu Wang, James Apfel
Date: 2020-12-15 00:00:00
Tasks: Classification, Deep Learning, General Classification

Since 2014, very deep convolutional neural networks have been proposed and become the must-have weapon for champions in all kinds of competition. In this report, a pipeline is introduced to perform the classification of smoking and calling by modifying the pretrained inception V3. Brightness enhancing based on deep learning is implemented to improve the classification of this classification task along with other useful training tricks. Based on the quality and quantity results, it can be concluded that this pipeline with small biased samples is practical and useful with high accuracy.
Improving model calibration with accuracy versus uncertainty optimization
Authors: Ranganath Krishnan, Omesh Tickoo
Date: 2020-12-14 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: image-classification, Image Classification, model, Variational Inference

Obtaining reliable and accurate quantification of uncertainty estimates from deep neural networks is important in safety-critical applications. A well-calibrated model should be accurate when it is certain about its prediction and indicate high uncertainty when it is likely to be inaccurate. Uncertainty calibration is a challenging problem as there is no ground truth available for uncertainty estimates. We propose an optimization method that leverages the relationship between accuracy and uncertainty as an anchor for uncertainty calibration. We introduce a differentiable accuracy versus uncertainty calibration (AvUC) loss function that allows a model to learn to provide well-calibrated uncertainties, in addition to improved accuracy. We also demonstrate the same methodology can be extended to post-hoc uncertainty calibration on pretrained models. We illustrate our approach with mean-field stochastic variational inference and compare with state-of-the-art methods. Extensive experiments demonstrate our approach yields better model calibration than existing methods on large-scale image classification tasks under distributional shift.