pwc-archive/papers-with-abstracts huggingface dataset:
Data Management Challenges for Internet-scale 3D Search Engines
Authors: James Williams, Shane Scott, Sean Wedig, Timur Hindanov, Christoph Roedig
Date: 2022-09-08 00:00:00
Tasks: Information Retrieval, Management, Retrieval

This paper describes the most significant data-related challenges involved in building internet-scale 3D search engines. The discussion centers on the most pressing data management issues in this domain, including model acquisition, support for multiple file formats, asset versioning, data integrity errors, the data lifecycle, intellectual property, and the legality of web crawling. The paper also discusses numerous issues that fall under the rubric of trustworthy computing, including privacy, security, inappropriate content, and copying/remixing of assets. The goal of the paper is to provide an overview of these general issues, illustrated by empirical data drawn from the internet's largest operational search engine. While numerous works have been published on 3D information retrieval, this paper is the first to discuss the real-world challenges that arise in building practical search engines at scale.
Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential Recommendation
Authors: Ruihong Qiu, Zi Huang, Hongzhi Yin
Date: 2022-09-08 00:00:00
Tasks: Sequential Recommendation

Overfitting has long been considered a common issue to large neural network models in sequential recommendation. In our study, an interesting phenomenon is observed that overfitting is temporary. When the model scale is increased, the trend of the performance firstly ascends, then descends (i.e., overfitting) and finally ascends again, which is named as double ascent in this paper. We therefore raise an assumption that a considerably larger model will generalise better with a higher performance. In an extreme case to infinite-width, performance is expected to reach the limit of this specific structure. Unfortunately, it is impractical to directly build a huge model due to the limit of resources. In this paper, we propose the Overparameterised Recommender (OverRec), which utilises a recurrent neural tangent kernel (RNTK) as a similarity measurement for user sequences to successfully bypass the restriction of hardware for huge models. We further prove that the RNTK for the tied input-output embeddings in recommendation is the same as the RNTK for general untied input-output embeddings, which makes RNTK theoretically suitable for recommendation. Since the RNTK is analytically derived, OverRec does not require any training, avoiding physically building the huge model. Extensive experiments are conducted on four datasets, which verifies the state-of-the-art performance of OverRec.
Data-Driven Deep Supervision for Medical Image Segmentation
Authors: Suraj Mishra, Yizhe Zhang, Danny Z. Chen, X. Sharon Hu
Date: 2022-06-14 00:00:00
Proceeding: IEEE Transactions on Medical Imaging 2022 6
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Medical image segmentation plays a vital role in disease diagnosis and analysis. However, data-dependent difficulties such as low image contrast, noisy background, and complicated objects of interest render the segmentation problem challenging. These difficulties diminish dense prediction and make it tough for known approaches to explore data-specific attributes for robust feature extraction. In this paper, we study medical image segmentation by focusing on robust data-specific feature extraction to achieve improved dense prediction. We propose a new deep convolutional neural network (CNN), which exploits specific attributes of input datasets to utilize deep supervision for enhanced feature extraction. In particular, we strategically locate and deploy auxiliary supervision,
by matching the object perceptive field (OPF) (which we define and compute) with the layer-wise effective receptive fields (LERF) of the network. This helps the model pay close attention to some distinct input data dependent features, which the network might otherwise ‘ignore’ during training.
Further, to achieve better target localization and refined dense prediction, we propose the densely decoded networks (DDN), by selectively introducing additional network connections (the ‘crutch’ connections). Using five public datasets (two retinal vessel, melanoma, optic disc/cup, and spleen segmentation) and two in-house datasets (lymph node and fungus segmentation), we verify the effectiveness of our proposed approach in 2D and 3D segmentation
Towards Locality Similarity Preserving to 3D Human Pose Estimation
Authors: Shihao Zhou, Mengxi Jiang, Qicong Wang, Yunqi Lei
Date: 2020-12-04 00:00:00
Proceeding: Asian Conference on Computer Vision Workshops 2020 12
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Pose Estimation

Estimating 3D human pose from an annotated or detected 2D pose in a single RGB image is a challenging problem. A successful way to address this problem is the example-based approach. The existing example-based approaches often calculate a global pose error to search a single match 3D pose from the source library. This way fails to capture the local deformations of human pose and highly dependent on a large training set. To alleviate these issues, we propose a simple example-based approach with locality similarity preserving to estimate 3D human pose. Specifically, first of all, we split an annotated or detected 2D pose into 2D body parts with kinematic priors. Then, to recover the 3D pose from these 2D body parts, we recombine a 3D pose by using 3D body parts that are split from the 3D pose candidates. Note that joints in the combined 3D parts are refined by a weighted searching strategy during the inference. Moreover, to increase the search speed, we propose a candidate selecting mechanism to narrow the original source data. We evaluate our approach on three well-design benchmarks, including Human3.6M, HumanEva-I, and MPII. The extensive experimental results show the effectiveness of our approach. Specifically, our approach achieves better performance than compared approaches while using fewer training samples
No means ‘No’; a non-im-proper modeling approach, with embedded speculative context
Authors: Priya Tiwary, Akshayraj M, Amit Gautam
Date: 2022-08-30 00:00:00
Proceeding: Bioinformatics 2022 8
Tasks: Natural Language Inference, Negation, Negation and Speculation Cue Detection, Negation and Speculation Scope resolution, Negation Scope Resolution, Sentence, Sentence Embeddings, Speculation Scope Resolution

Motivation: The medical data are complex in nature as terms that appear in records usually appear in different contexts. Through this paper, we investigate various bio model’s embeddings(BioBERT, BioE- LECTRA, PubMedBERT) on their understanding of "negation and speculation context" wherein we found that these models were unable to differentiate "negated context" vs "non-negated context". To measure the understanding of models, we used cosine similarity scores of negated sentence embeddings vs non- negated sentence embeddings pairs. For improving these models, we introduce a generic super tuning approach to enhance the embeddings on "negation and speculation context" by utilizing a synthesized dataset.
Results: After super-tuning the models we can see that the model’s embeddings are now understanding negative and speculative contexts much better. Furthermore, we fine-tuned the super tuned models on various tasks and we found that the model has outperformed the previous models and achieved state- of-the-art (SOTA) on negation, speculation cue, and scope detection tasks on BioScope abstracts and Sherlock dataset. We also confirmed that our approach had a very minimal trade-off in the performance of the model in other tasks like Natural Language Inference after super-tuning.
The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes
Authors: György Szarvas, Veronika Vincze, Richárd Farkas, János Csirik
Date: 2008-06-01 00:00:00
Proceeding: Association for Computational Linguistics 2008 6
Tasks: Negation, Sentence

This article reports on a corpus annotation project that has produced a freely available resource for research on handling negation and
uncertainty in biomedical texts (we call this corpus the BioScope corpus). The corpus consists of three parts, namely medical free texts,biological full papers and biological scientific abstracts. The dataset contains annotations at the token level for negative and speculative keywords and at the sentence level for their linguistic scope. The annotation process was carried out by two independent linguist annotators and a chief annotator – also responsible for setting up the annotation guidelines – who resolved cases where the annotators disagreed. We will report our statistics on corpus size, ambiguity levels and the consistency of annotations.
Beyond Random Split for Assessing Statistical Model Performance
Authors: Carlos Catania, Jorge Guerra, Juan Manuel Romero, Gabriel Caffaratti, Martin Marchetta
Date: 2022-09-04 00:00:00
Tasks: model

Even though a train/test split of the dataset randomly performed is a common practice, could not always be the best approach for estimating performance generalization under some scenarios. The fact is that the usual machine learning methodology can sometimes overestimate the generalization error when a dataset is not representative or when rare and elusive examples are a fundamental aspect of the detection problem. In the present work, we analyze strategies based on the predictors' variability to split in training and testing sets. Such strategies aim at guaranteeing the inclusion of rare or unusual examples with a minimal loss of the population's representativeness and provide a more accurate estimation about the generalization error when the dataset is not representative. Two baseline classifiers based on decision trees were used for testing the four splitting strategies considered. Both classifiers were applied on CTU19 a low-representative dataset for a network security detection problem. Preliminary results showed the importance of applying the three alternative strategies to the Monte Carlo splitting strategy in order to get a more accurate error estimation on different but feasible scenarios.
Optimal Communication and Control Strategies for a Multi-Agent System in the Presence of an Adversary
Authors: Dhruva Kartik, Sagar Sudhakara, Rahul Jain, Ashutosh Nayyar
Date: 2022-09-08 00:00:00

We consider a multi-agent system in which a decentralized team of agents controls a stochastic system in the presence of an adversary. Instead of committing to a fixed information sharing protocol, the agents can strategically decide at each time whether to share their private information with each other or not. The agents incur a cost whenever they communicate with each other and the adversary may eavesdrop on their communication. Thus, the agents in the team must effectively coordinate with each other while being robust to the adversary's malicious actions. We model this interaction between the team and the adversary as a stochastic zero-sum game where the team aims to minimize a cost while the adversary aims to maximize it. Under some assumptions on the adversary's capabilities, we characterize a min-max control and communication strategy for the team. We supplement this characterization with several structural results that can make the computation of the min-max strategy more tractable.
Assessing Active Distribution Network Flexibility: On the Effects of Nonlinearities and Nonconvexities
Authors: Andrey Churkin, Pierluigi Mancarella, Eduardo A. Martinez Cesena
Date: 2022-09-08 00:00:00

A widespread approach to characterise the aggregated flexibility of active distribution networks (ADNs) is to estimate the boundary of the feasible network operating areas using convex polygons in the P-Q space. However, such approximations can be inaccurate under realistic conditions where, for example, the nonlinear nature of the network is captured, and the behaviour of flexible units is constrained. This letter demonstrates, using a small ADN example with four flexible units and considering only nonlinearities from the network, that reaching the full P-Q flexibility areas would require perfect coordination of units and high ramping rates. Without these requirements, the P-Q areas become nonconvex. Thus, if the effects of nonlinearities and nonconvexities are ignored, existing approaches in the literature can result in overestimation of ADN flexibility and give rise to impractical solutions, hampering coordination between transmission and distribution system operators.
Optimization-based framework for low-voltage grid reinforcement assessment under various levels of flexibility and coordination
Authors: Soner Candas, Beneharo Reveron Baecker, Anurag Mohapatra, Thomas Hamacher
Date: 2022-09-08 00:00:00

The rapid electrification of residential heating and mobility sectors is expected to drive the existing distribution grid assets beyond their planned operating conditions. This change will also reveal new potentials through sector coupling, flexibilities, and the local exchange of decentralized generation. This paper thus presents an optimization framework for multi-modal energy systems at the low voltage (LV) distribution grid level. In this, we focus on the reinforcement requirements of the grid and the techno-economic assessment of flexibility components and coordination between agents. By employing a multi-level approach, computational complexity is reduced, and various levels of coordination and flexibilities are implemented. We conclude the work with a case study for a representative rural grid in Germany, in which we observe high economic potential in the flexible operation of buildings, majorly thanks to better integration of photovoltaics. Across all paradigms barring a best-case benchmark, grid reinforcement based on a mix of passive and active measures was necessary. A synergy effect is observed between flexibilities and coordination, as their combination reduces the peaks to the extent of completely avoiding grid reinforcement. The presented framework can be applied with a wide range of grid and component types to outline the broad landscape of future LV distribution grids.
H2 Performance Analysis and Synthesis for Discrete-Time Linear Systems with Dynamics Determined by an i.i.d. Process
Authors: Yohei Hosoe, Takashi Okamoto, Tomomichi Hagiwara
Date: 2022-09-08 00:00:00

This paper is concerned with H2 control of discrete-time linear systems with dynamics determined by an independent and identically distributed (i.i.d.) process. A definition of H2 norm is first discussed for the class of systems. Then, a linear matrix inequality (LMI) condition is derived for the associated performance analysis, which is tractable in the sense of numerical computation. The results about analysis are also extended toward state-feedback controller synthesis.
Constructive Equivariant Observer Design for Inertial Velocity-Aided Attitude
Authors: Pieter van Goor, Tarek Hamel, Robert Mahony
Date: 2022-09-08 00:00:00

Inertial Velocity-Aided Attitude (VAA) is an important problem in the control of Remotely Piloted Aerial Systems (RPAS), and involves estimating the velocity and attitude of a vehicle using gyroscope, accelerometer, and inertial-frame velocity (e.g. GPS velocity) measurements. Existing solutions tend to be complex and provide limited stability guarantees, relying on either high gain designs or assuming constant acceleration of the vehicle. This paper proposes a novel observer for inertial VAA that exploits Lie group symmetries of the system dynamics, and shows that the observer is synchronous with the system trajectories. This is achieved by adding a virtual state of only three dimensions, in contrast to the larger virtual states typically used in the literature. The error dynamics of the observer are shown to be almost globally asymptotically and locally exponentially stable. Finally, the observer is verified in simulation, where it is shown that the estimation error converges to zero even with an extremely poor initial condition.
Tube-Based Zonotopic Data-Driven Predictive Control
Authors: Alessio Russo, Alexandre Proutiere
Date: 2022-09-07 00:00:00
Tasks: Computational Efficiency

We present a novel tube-based data-driven predictive control method for linear systems affected by a bounded addictive disturbance. Our method leverages recent results in the reachability analysis of unknown linear systems to formulate and solve a robust tube-based predictive control problem. More precisely, our approach consists in deriving, from the collected data, a zonotope that includes the true state error set. We show how to guarantee the stability of the resulting error zonotope, which can be exploited to increase the computational efficiency of existing zonotopic data-driven MPC formulations. Results on a double-integrator affected by strong adversarial noise demonstrate the effectiveness of the proposed control approach.
Energy Optimization of Wind Turbines via a Neural Control Policy Based on Reinforcement Learning Markov Chain Monte Carlo Algorithm
Authors: Vahid Tavakol Aghaei, Arda Ağababaoğlu, Biram Bawo, Peiman Naseradinmousavi, Sinan Yildirim, Serhat Yeşilyurt, Ahmet Onat
Date: 2022-09-07 00:00:00
Tasks: Point Tracking, Reinforcement Learning (RL)

This study focuses on the numerical analysis and optimal control of vertical-axis wind turbines (VAWT) using Bayesian reinforcement learning (RL). We specifically address small-scale wind turbines, which are well-suited to local and compact production of electrical energy on a small scale, such as urban and rural infrastructure installations. Existing literature concentrates on large scale wind turbines which run in unobstructed, mostly constant wind profiles. However urban installations generally must cope with rapidly changing wind patterns. To bridge this gap, we formulate and implement an RL strategy using the Markov chain Monte Carlo (MCMC) algorithm to optimize the long-term energy output of a wind turbine. Our MCMC-based RL algorithm is a model-free and gradient-free algorithm, in which the designer does not have to know the precise dynamics of the plant and its uncertainties. Our method addresses the uncertainties by using a multiplicative reward structure, in contrast with additive reward used in conventional RL approaches. We have shown numerically that the method specifically overcomes the shortcomings typically associated with conventional solutions, including, but not limited to, component aging, modeling errors, and inaccuracies in the estimation of wind speed patterns. Our results show that the proposed method is especially successful in capturing power from wind transients; by modulating the generator load and hence the rotor torque load, so that the rotor tip speed quickly reaches the optimum value for the anticipated wind speed. This ratio of rotor tip speed to wind speed is known to be critical in wind power applications. The wind to load energy efficiency of the proposed method was shown to be superior to two other methods; the classical maximum power point tracking method and a generator controlled by deep deterministic policy gradient (DDPG) method.
An efficient approach for nonconvex semidefinite optimization via customized alternating direction method of multipliers
Authors: Chuangchuang Sun
Date: 2022-09-07 00:00:00
Tasks: Community Detection, Form, Image Segmentation, Semantic Segmentation

We investigate a class of general combinatorial graph problems, including MAX-CUT and community detection, reformulated as quadratic objectives over nonconvex constraints and solved via the alternating direction method of multipliers (ADMM). We propose two reformulations: one using vector variables and a binary constraint, and the other further reformulating the Burer-Monteiro form for simpler subproblems. Despite the nonconvex constraint, we prove the ADMM iterates converge to a stationary point in both formulations, under mild assumptions. Additionally, recent work suggests that in this latter form, when the matrix factors are wide enough, local optimum with high probability is also the global optimum. To demonstrate the scalability of our algorithm, we include results for MAX-CUT, community detection, and image segmentation benchmark and simulated examples.
Macroscopic Dynamics of Neural Networks with Heterogeneous Spiking Thresholds
Authors: Richard Gast, Sara A. Solla, Ann Kennedy
Date: 2022-09-07 00:00:00

Mean-field theory links the physiological properties of individual neurons to the emergent dynamics of neural population activity. These models provide an essential tool for studying brain function at different scales; however, for their application to neural populations on large scale, they need to account for differences between distinct neuron types. The Izhikevich single neuron model can account for a broad range of different neuron types and spiking patterns, thus rendering it an optimal candidate for a mean-field theoretic treatment of brain dynamics in heterogeneous networks. Here, we derive the mean-field equations for networks of all-to-all coupled Izhikevich neurons with heterogeneous spiking thresholds. Using methods from bifurcation theory, we examine the conditions under which the mean-field theory accurately predicts the dynamics of the Izhikevich neuron network. To this end, we focus on three important features of the Izhikevich model that are subject here to simplifying assumptions: (i) spike-frequency adaptation, (ii) the spike reset conditions, and (iii) the distribution of single-cell spike thresholds across neurons. Our results indicate that, while the mean-field model is not an exact model of the Izhikevich network dynamics, it faithfully captures its different dynamic regimes and phase transitions. We thus present a mean-field model that can represent different neuron types and spiking dynamics. The model is comprised of biophysical state variables and parameters, incorporates realistic spike resetting conditions, and accounts for heterogeneity in neural spiking thresholds. These features allow for a broad applicability of the model as well as for a direct comparison to experimental data.
Mid-Infrared Photothermal-Fluorescence in Situ Hybridization for Functional Analysis and Genetic Identification of Single Cells
Authors: Yeran Bai, Zhongyue Guo, Fátima C. Pereira, Michael Wagner, Ji-Xin Cheng
Date: 2022-09-06 00:00:00

Simultaneous identification and metabolic analysis of microbes with single-cell resolution and high throughput is necessary to answer the question of "who eats what, when, and where" in complex microbial communities. Here, we present a mid-infrared photothermal-fluorescence in situ hybridization (MIP-FISH) platform that enables direct bridging of genotype and phenotype. Through multiple improvements of MIP imaging, the sensitive detection of isotopically-labelled compounds incorporated into proteins of individual bacterial cells became possible, while simultaneous detection of FISH labelling with rRNA-targeted probes enabled the identification of the analyzed cells. In proof-of-concept experiments, we showed that the clear spectral red shift in the protein amide I region due to incorporation of $^{13}$C atoms originating from $^{13}$C-labelled-glucose can be exploited by MIP-FISH to discriminate and identify $^{13}$C-labelled bacterial cells within a complex human gut microbiome sample. The presented methods open new opportunities for single-cell structure-function analyses for microbiology.