pwc-archive/papers-with-abstracts huggingface dataset:
CloTH-VTON+: Clothing Three-dimensional reconstruction for Hybrid image-based Virtual Try-ON
Authors: Matiur Rahman Minar, Thai Thanh Tuan, Heejune Ahn
Date: 2021-02-16 00:00:00
Tasks: Pose Estimation, Virtual Try-on

Image-based virtual try-on (VTON) systems based on deep learning have attracted research and commercialinterests. Although they show their strengths in blending the person and try-on clothing image andsynthesizing the dis-occluded regions, their results for complex-posed persons are often unsatisfactorydue to the limitations in their geometry deformation and texture-preserving capacity. To address thesechallenges, we propose CloTH-VTON+ for seamlessly integrating the image-based deep learning methodsand the strength of the 3D model in shape deformation. Specifically, a fully automatic pipeline is developedfor 3D clothing model reconstruction and deformation using a reference human model: first, the try-onclothing is matched to the target clothing regions in the simple shaped reference human model, and thenthe 3D clothing model is reconstructed. The reconstructed 3D clothing model can generate a very naturalpose and shape transfer, retaining the textures of clothes. A clothing refinement network further refines thealignment, eliminating the misalignment due to the errors in human pose estimation and 3D deformation.The deformed clothing images are combined utilizing conditional generative networks to in-paint the dis-occluded areas and blend them all. Experiments on an existing benchmark dataset demonstrate that CloTH-VTON+ generates higher quality results in comparison to the state-of-the-art VTON systems and CloTH-VTON. CloTH-VTON+ can be incorporated into extended applications such as multi-pose guided and Video VTON.
A Dirichlet Multinomial Mixture Model-based Approach for Short Text Clustering
Authors: Jianhua Yin, Jianyong Wang
Date: 2014-08-01 00:00:00
Tasks: Clustering, Short Text Clustering, Text Clustering

Short text clustering has become an increasingly important task with the popularity of social media like Twitter,Google+, and Facebook. It is a challenging problem due to its sparse, high-dimensional, and large-volume characteristics. In this paper, we proposed a collapsed Gibbs Sampling algorithm for the Dirichlet Multinomial Mixture model for short text clustering (abbr. to GSDMM). We found that GSDMM can infer the number of clusters automatically with a good balance between the completeness and homogeneity of the clustering results, and is fast to converge. GSDMM can also cope with the sparse and high-dimensional problem of short texts, and can obtain the representative words of each cluster. Our extensive experimental study shows that GSDMM can achieve significantly better performance than three other clustering models.
DeepBlur: A Simple and Effective Method for Natural Image Obfuscation
Authors: Tao Li, Min Soo Choi
Date: 2021-03-31 00:00:00
Tasks: Deep Learning, Face Recognition

There is a growing privacy concern due to the popularity of social media and surveillance systems, along with advances in face recognition software. However, established image obfuscation techniques are either vulnerable to re-identification attacks by human or deep learning models, insufficient in preserving image fidelity, or too computationally intensive to be practical. To tackle these issues, we present DeepBlur, a simple yet effective method for image obfuscation by blurring in the latent space of an unconditionally pre-trained generative model that is able to synthesize photo-realistic facial images. We compare it with existing methods by efficiency and image quality, and evaluate against both state-of-the-art deep learning models and industrial products (e.g., Face++, Microsoft face service). Experiments show that our method produces high quality outputs and is the strongest defense for most test cases.
DeepEverest: Accelerating Declarative Top-K Queries for Deep Neural Network Interpretation
Authors: Dong He, Maureen Daum, Walter Cai, Magdalena Balazinska
Date: 2021-04-06 00:00:00
Tasks: Network Interpretation

We design, implement, and evaluate DeepEverest, a system for the efficient execution of interpretation by example queries over the activation values of a deep neural network. DeepEverest consists of an efficient indexing technique and a query execution algorithm with various optimizations. We prove that the proposed query execution algorithm is instance optimal. Experiments with our prototype show that DeepEverest, using less than 20% of the storage of full materialization, significantly accelerates individual queries by up to 63x and consistently outperforms other methods on multi-query workloads that simulate DNN interpretation processes.
Indirect Identification of Horizontal Gene Transfer
Authors: David Schaller, Manuel Lafond, Peter F. Stadler, Nicolas Wieseke, Marc Hellmuth
Date: 2020-12-16 00:00:00

Several implicit methods to infer Horizontal Gene Transfer (HGT) focus on pairs of genes that have diverged only after the divergence of the two species in which the genes reside. This situation defines the edge set of a graph, the later-divergence-time (LDT) graph, whose vertices correspond to genes colored by their species. We investigate these graphs in the setting of relaxed scenarios, i.e., evolutionary scenarios that encompass all commonly used variants of duplication-transfer-loss scenarios in the literature. We characterize LDT graphs as a subclass of properly vertex-colored cographs, and provide a polynomial-time recognition algorithm as well as an algorithm to construct a relaxed scenario that explains a given LDT. An edge in an LDT graph implies that the two corresponding genes are separated by at least one HGT event. The converse is not true, however. We show that the complete xenology relation is described by an rs-Fitch graph, i.e., a complete multipartite graph satisfying constraints on the vertex coloring. This class of vertex-colored graphs is also recognizable in polynomial time. We finally address the question "how much information about all HGT events is contained in LDT graphs" with the help of simulations of evolutionary scenarios with a wide range of duplication, loss, and HGT events. In particular, we show that a simple greedy graph editing scheme can be used to efficiently detect HGT events that are implicitly contained in LDT graphs.
Extraction of a computer-certified ODE solver
Authors: Grigory Devadze, Lars Flessing, Stefan Streif
Date: 2021-04-06 00:00:00

Reliably determining system trajectories is essential in many analysis and control design approaches. To this end, an initial value problem has to be usually solved via numerical algorithms which rely on a certain software realization. Because software realizations can be error-prone, proof assistants may be used to verify the underlying mathematical concepts and corresponding algorithms. In this work we present a computer-certified formalization of the solution of the initial value problem of ordinary differential equations. The concepts are performed in the framework of constructive analysis and the proofs are written in the \texttt{Minlog} proof system. We show the extraction of a program, which solves an ODE numerically and provide some possible optimization regarding the efficiency. Finally, we provide numerical experiments to demonstrate how programs of a certain high level of abstraction can be obtained efficiently. The presented concepts may also be viewed as a part of preliminary work for the development of formalized nonlinear control theory, hence offering the possibility of computer-assisted controller design and program extraction for the controller implementation.
An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-Level Structural Information
Authors: Zejun Li, Zhongyu Wei, Zhihao Fan, Haijun Shan, Xuanjing Huang
Date: 2021-03-21 00:00:00
Tasks: Representation Learning, Semantic Similarity, Semantic Textual Similarity, Sentence

In this paper, we focus on the problem of unsupervised image-sentence matching. Existing research explores to utilize document-level structural information to sample positive and negative instances for model training. Although the approach achieves positive results, it introduces a sampling bias and fails to distinguish instances with high semantic similarity. To alleviate the bias, we propose a new sampling strategy to select additional intra-document image-sentence pairs as positive or negative samples. Furthermore, to recognize the complex pattern in intra-document samples, we propose a Transformer based model to capture fine-grained features and implicitly construct a graph for each document, where concepts in a document are introduced to bridge the representation learning of images and sentences in the context of a document. Experimental results show the effectiveness of our approach to alleviate the bias and learn well-aligned multimodal representations.
An LSTM-Aided Hybrid Random Access Scheme for 6G Machine Type Communication Networks
Authors: Wenchao Zhai, Huimei Han, Lei Liu, Jun Zhao
Date: 2020-12-25 00:00:00

In this paper, an LSTM-aided hybrid random access scheme (LSTMH-RA) is proposed to support diverse quality of service (QoS) requirements in 6G machine-type communication (MTC) networks, where massive MTC (mMTC) devices and ultra-reliable low latency communications (URLLC) devices coexist. In the proposed LSTMH-RA scheme, mMTC devices access the network via a timing advance (TA)-aided four-step procedure to meet massive access requirement, while the access procedure of the URLLC devices is completed in two steps coupled with the mMTC devices' access procedure to reduce latency. Furthermore, we propose an attention-based LSTM prediction model to predict the number of active URLLC devices, thereby determining the parameters of the multi-user detection algorithm to guarantee the latency and reliability access requirements of URLLC devices. We analyze the successful access probability of the LSTMH-RA scheme. Numerical results show that, compared with the benchmark schemes, the proposed LSTMH-RA scheme can significantly improve the successful access probability, and thus satisfy the diverse QoS requirements of URLLC and mMTC devices.
Adaptive Variants of Optimal Feedback Policies
Authors: Brett T. Lopez, Jean-Jacques E. Slotine
Date: 2021-04-06 00:00:00
Tasks: Transfer Learning

The stable combination of optimal feedback policies with online learning is studied in a new control-theoretic framework for uncertain nonlinear systems. The framework can be systematically used in transfer learning and sim-to-real applications, where an optimal policy learned for a nominal system needs to remain effective in the presence of significant variations in parameters. Given unknown parameters within a bounded range, the resulting adaptive control laws guarantee convergence of the closed-loop system to the state of zero cost. Online adjustment of the learning rate is used as a key stability mechanism, and preserves certainty equivalence when designing optimal policies without assuming uncertainty to be within the control range. The approach is illustrated on the familiar mountain car problem, where it yields near-optimal performance despite the presence of parametric model uncertainty.
Particle MPC for Uncertain and Learning-Based Control
Authors: Robert Dyro, James Harrison, Apoorva Sharma, Marco Pavone
Date: 2021-04-06 00:00:00
Tasks: Model-based Reinforcement Learning, Model Predictive Control, State Estimation

As robotic systems move from highly structured environments to open worlds, incorporating uncertainty from dynamics learning or state estimation into the control pipeline is essential for robust performance. In this paper we present a nonlinear particle model predictive control (PMPC) approach to control under uncertainty, which directly incorporates any particle-based uncertainty representation, such as those common in robotics. Our approach builds on scenario methods for MPC, but in contrast to existing approaches, which either constrain all or only the first timestep to share actions across scenarios, we investigate the impact of a \textit{partial consensus horizon}. Implementing this optimization for nonlinear dynamics by leveraging sequential convex optimization, our approach yields an efficient framework that can be tuned to the particular information gain dynamics of a system to mitigate both over-conservatism and over-optimism. We investigate our approach for two robotic systems across three problem settings: time-varying, partially observed dynamics; sensing uncertainty; and model-based reinforcement learning, and show that our approach improves performance over baselines in all settings.
An Initial Investigation for Detecting Partially Spoofed Audio
Authors: Lin Zhang, Xin Wang, Erica Cooper, Junichi Yamagishi, Jose Patino, Nicholas Evans
Date: 2021-04-06 00:00:00
Tasks: Voice Anti-spoofing

All existing databases of spoofed speech contain attack data that is spoofed in its entirety. In practice, it is entirely plausible that successful attacks can be mounted with utterances that are only partially spoofed. By definition, partially-spoofed utterances contain a mix of both spoofed and bona fide segments, which will likely degrade the performance of countermeasures trained with entirely spoofed utterances. This hypothesis raises the obvious question: 'Can we detect partially-spoofed audio?' This paper introduces a new database of partially-spoofed data, named PartialSpoof, to help address this question. This new database enables us to investigate and compare the performance of countermeasures on both utterance- and segmental- level labels. Experimental results using the utterance-level labels reveal that the reliability of countermeasures trained to detect fully-spoofed data is found to degrade substantially when tested with partially-spoofed data, whereas training on partially-spoofed data performs reliably in the case of both fully- and partially-spoofed utterances. Additional experiments using segmental-level labels show that spotting injected spoofed segments included in an utterance is a much more challenging task even if the latest countermeasure models are used.
Weakly-supervised Audio-visual Sound Source Detection and Separation
Authors: Tanzila Rahman, Leonid Sigal
Date: 2021-03-25 00:00:00
Tasks: Audio Source Separation, Denoising, Object, Segmentation, Semantic Segmentation, Visually Guided Sound Source Separation, Weakly-Supervised Object Segmentation

Learning how to localize and separate individual object sounds in the audio channel of the video is a difficult task. Current state-of-the-art methods predict audio masks from artificially mixed spectrograms, known as Mix-and-Separate framework. We propose an audio-visual co-segmentation, where the network learns both what individual objects look and sound like, from videos labeled with only object labels. Unlike other recent visually-guided audio source separation frameworks, our architecture can be learned in an end-to-end manner and requires no additional supervision or bounding box proposals. Specifically, we introduce weakly-supervised object segmentation in the context of sound separation. We also formulate spectrogram mask prediction using a set of learned mask bases, which combine using coefficients conditioned on the output of object segmentation , a design that facilitates separation. Extensive experiments on the MUSIC dataset show that our proposed approach outperforms state-of-the-art methods on visually guided sound source separation and sound denoising.
Locate the Source of Resonance-Involved Forced Oscillation in Power Systems Based on Mode Shape Analysis
Authors: Shutang You
Date: 2020-11-24 00:00:00

This paper proposed a new method to locate the source of forced oscillation that involves resonance with natural oscillation modes. The new method is based on comparing the oscillation mode shape of the forced oscillation with that of the natural oscillation that the forced oscillation resonating with. The location that has the largest angle difference between the forced oscillation mode and the natural oscillation mode usually indicates the location of the driving force in forced oscillations. Some examples in actual U.S. EI system verified this approach.
Time-Robust Control for STL Specifications
Authors: Alena Rodionova, Lars Lindemann, Manfred Morari, George J. Pappas
Date: 2021-04-06 00:00:00

We present a robust control framework for time-critical systems in which satisfying real-time constraints robustly is of utmost importance for the safety of the system. Signal Temporal Logic (STL) provides a formal means to express a large variety of real-time constraints over signals and is suited for planning and control purposes as it allows us to reason about the time robustness of such constraints. The time robustness of STL particularly quantifies the extent to which timing uncertainties can be tolerated without violating real-time specifications. In this paper, we first pose a control problem in which we aim to find an optimal input sequence to a control system that maximizes the time robustness of an STL constraint. We then propose a Mixed Integer Linear Program (MILP) encoding and provide correctness guarantees along with a complexity analysis of the encoding. We also show in two case studies that maximizing STL time robustness allows to account for timing uncertainties of the underlying control system.
Dendritic trafficking: synaptic scaling and structural plasticity
Authors: Saeed Aljaberi, Timothy O'Leary, Fulvio Forni
Date: 2020-11-24 00:00:00

Neuronal circuits internally regulate electrical signaling via a host of homeostatic mechanisms. Two prominent mechanisms, synaptic scaling and structural plasticity, are believed to maintain average activity within an operating range by modifying the strength and spatial extent of network connectivity using negative feedback. However, both mechanisms operate on relatively slow timescales and thus face fundamental limits due to delays. We show that these mechanisms fulfill complementary roles in maintaining stability in a large network. In particular, even relatively, slow growth dynamics improves performance significantly beyond synaptic scaling alone.
A Unified Passivity-Based Framework for Control of Modular Islanded AC Microgrids
Authors: Felix Strehle, Pulkit Nahata, Albertus Johannes Malan, Sören Hohmann, Giancarlo Ferrari-Trecate
Date: 2021-04-06 00:00:00

Voltage and frequency control in an islanded AC microgrid (ImGs) amount to stabilizing an a priori unknown ImG equilibrium induced by loads and changes in topology. This paper puts forth a unified control framework which, while guaranteeing such stability, allows for modular ImGs interconnecting multiple subsystems, that is, dynamic RLC lines, nonlinear constant impedance, current, power (ZIP) and exponential (EXP) loads, and inverter-based distributed generation units (DGUs) controlled with different types of primary controllers. The underlying idea of the framework is based one equilibrium-independent passivity (EIP) of the ImG subsystems, which enables stability certificates of ImG equilibria without their explicit knowledge. In order to render DGUs EIP, we propose a decentralized controller synthesis algorithm based on port-Hamiltonian systems (PHSs). We also show that EIP, being the key to stability, provides a general framework which can embrace other solutions available in the literature. Furthermore, we provide a novel argument based on LaSalle's theorem for proving asymptotic voltage and frequency stability. Finally, we analyze the impact of actuator saturation on the stability results by exploiting the inherent EIP properties of the PHS DGU model. Theoretical findings are backed up by realistic simulations based on the CIGRE benchmark for medium voltage networks.
Safety-Critical Control of Stochastic Systems using Stochastic Control Barrier Functions
Authors: Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu
Date: 2021-04-06 00:00:00

Control barrier functions have been widely used for synthesizing safety-critical controls, often via solving quadratic programs. However, the existence of Gaussian-type noise may lead to unsafe actions and result in severe consequences. In this paper, we study systems modeled by stochastic differential equations (SDEs) driven by Brownian motions. We propose a notion of stochastic control barrier functions (SCBFs)and show that SCBFs can significantly reduce the control efforts, especially in the presence of noise, compared to stochastic reciprocal control barrier functions (SRCBFs), and offer a less conservative estimation of safety probability, compared to stochastic zeroing control barrier functions (SZCBFs). Based on this less conservative probabilistic estimation for the proposed notion of SCBFs, we further extend the results to handle high relative degree safety constraints using high-order SCBFs. We demonstrate that the proposed SCBFs achieve good trade-offs of performance and control efforts, both through theoretical analysis and numerical simulations.
Temporal-Logic-Based Intermittent, Optimal, and Safe Continuous-Time Learning for Trajectory Tracking
Authors: Aris Kanellopoulos, Filippos Fotiadis, Chuangchuang Sun, Zhe Xu, Kyriakos G. Vamvoudakis, Ufuk Topcu, Warren E. Dixon
Date: 2021-04-06 00:00:00
Tasks: Reinforcement Learning (RL), Safe Reinforcement Learning

In this paper, we develop safe reinforcement-learning-based controllers for systems tasked with accomplishing complex missions that can be expressed as linear temporal logic specifications, similar to those required by search-and-rescue missions. We decompose the original mission into a sequence of tracking sub-problems under safety constraints. We impose the safety conditions by utilizing barrier functions to map the constrained optimal tracking problem in the physical space to an unconstrained one in the transformed space. Furthermore, we develop policies that intermittently update the control signal to solve the tracking sub-problems with reduced burden in the communication and computation resources. Subsequently, an actor-critic algorithm is utilized to solve the underlying Hamilton-Jacobi-Bellman equations. Finally, we support our proposed framework with stability proofs and showcase its efficacy via simulation results.
Bias Correction in Deterministic Policy Gradient Using Robust MPC
Authors: Arash Bahari Kordabad, Hossein Nejatbakhsh Esfahani, Sebastien Gros
Date: 2021-04-06 00:00:00
Tasks: Model Predictive Control

In this paper, we discuss the deterministic policy gradient using the Actor-Critic methods based on the linear compatible advantage function approximator, where the input spaces are continuous. When the policy is restricted by hard constraints, the exploration may not be Centred or Isotropic (non-CI). As a result, the policy gradient estimation can be biased. We focus on constrained policies based on Model Predictive Control (MPC) schemes and to address the bias issue, we propose an approximate Robust MPC approach accounting for the exploration. The RMPC-based policy ensures that a Centered and Isotropic (CI) exploration is approximately feasible. A posterior projection is used to ensure its exact feasibility, we formally prove that this approach does not bias the gradient estimation.
Robust Learning Model Predictive Control for Periodically Correlated Building Control
Authors: Jicheng Shi, Yingzhao Lian, Colin N. Jones
Date: 2020-11-27 00:00:00
Tasks: Model Predictive Control

Accounting for more than 40% of global energy consumption, residential and commercial buildings will be key players in any future green energy systems. To fully exploit their potential while ensuring occupant comfort, a robust control scheme is required to handle various uncertainties, such as external weather and occupant behaviour. However, prominent patterns, especially periodicity, are widely seen in most sources of uncertainty. This paper incorporates this correlated structure into the learning model predictive control framework, in order to learn a global optimal robust control scheme for building operations.