pwc-archive/papers-with-abstracts huggingface dataset:
Robust and efficient post-processing for video object detection
Authors: Alberto Sabater, Luis Montesano, Ana C. Murillo
Date: 2020-09-23 00:00:00
Tasks: Autonomous Driving, Object, object-detection, Object Detection, Object Recognition, Video Object Detection

Object recognition in video is an important task for plenty of applications, including autonomous driving perception, surveillance tasks, wearable devices or IoT networks. Object recognition using video data is more challenging than using still images due to blur, occlusions or rare object poses. Specific video detectors with high computational cost or standard image detectors together with a fast post-processing algorithm achieve the current state-of-the-art. This work introduces a novel post-processing pipeline that overcomes some of the limitations of previous post-processing methods by introducing a learning-based similarity evaluation between detections across frames. Our method improves the results of state-of-the-art specific video detectors, specially regarding fast moving objects, and presents low resource requirements. And applied to efficient still image detectors, such as YOLO, provides comparable results to much more computationally intensive detectors.
Artificial Intelligence Control in 4D Cylindrical Space for Industrial Robotic Applications
Authors: Andrea de Giorgio, Lihui Wang
Date: 2020-09-23 00:00:00
Tasks: Industrial Robots, Motion Planning, Trajectory Planning

This article argues that an efficient artificial intelligence control algorithm needs the built-in symmetries of an industrial robot manipulator to be further characterized and exploited. The product of this enhancement is a four-dimensional (4D) discrete cylindrical grid space that can directly replace complex robot models. A* is chosen for its wide use among such algorithms to study the advantages and disadvantages of steering the robot manipulator within the 4D cylindrical discrete grid. The study shows that this approach makes it possible to control a robot without any specific knowledge of the robot kinematic and dynamic models at planning and execution time. In fact, the robot joint positions for each grid cell are pre-calculated and stored as knowledge, then quickly retrieved by the pathfinding algorithm when needed. The 4D cylindrical discrete space has both the advantages of the configuration space and the three-dimensional Cartesian workspace of the robot. Since path optimization is the core of any search algorithms, including A*, the 4D cylindrical grid provides for a search space that can embed further knowledge in form of cell properties, including the presence of obstacles and volumetric occupancy of the entire industrial robot body for obstacle avoidance applications. The main trade-off is between a limited capacity for pre-computed grid knowledge and the path search speed. This innovative approach encourages the use of search algorithms for industrial robotic applications, opens up to the study of other robot symmetries present in different robot models and lays a foundation for the application of dynamic obstacle avoidance algorithms.
Explainable Inference on Sequential Data via Memory-Tracking
Authors: Biagio La Rosa, Roberto Capobianco, Daniele Nardi
Date: 2020-07-11 00:00:00
Tasks: Cloze Test, Common Sense Reasoning, Prediction, Sentence

In this paper we present a novel mechanism to
get explanations that allow to better understand
network predictions when dealing with sequential
data. Specifically, we adopt memory-based networks — Differential Neural Computers — to exploit their capability of storing data in memory and
reusing it for inference. By tracking both the memory access at prediction time, and the information
stored by the network at each step of the input
sequence, we can retrieve the most relevant input
steps associated to each prediction. We validate
our approach (1) on a modified T-maze, which is a
non-Markovian discrete control task evaluating an
algorithm’s ability to correlate events far apart in
history, and (2) on the Story Cloze Test, which is
a commonsense reasoning framework for evaluating story understanding that requires a system to
choose the correct ending to a four-sentence story.
Our results show that we are able to explain agent’s
decisions in (1) and to reconstruct the most relevant
sentences used by the network to select the story
ending in (2). Additionally, we show not only that
by removing those sentences the network prediction changes, but also that the same are sufficient to
reproduce the inference.
EigenRec: Generalizing PureSVD for Effective and Efficient Top-N Recommendations
Authors: Athanasios N. Nikolakopoulos, Vassilis Kalantzis, Efstratios Gallopoulos, John D. Garofalakis
Date: 2015-11-19 00:00:00

We introduce EigenRec; a versatile and efficient Latent-Factor framework for Top-N Recommendations that includes the well-known PureSVD algorithm as a special case. EigenRec builds a low dimensional model of an inter-item proximity matrix that combines a similarity component, with a scaling operator, designed to control the influence of the prior item popularity on the final model. Seeing PureSVD within our framework provides intuition about its inner workings, exposes its inherent limitations, and also, paves the path towards painlessly improving its recommendation performance. A comprehensive set of experiments on the MovieLens and the Yahoo datasets based on widely applied performance metrics, indicate that EigenRec outperforms several state-of-the-art algorithms, in terms of Standard and Long-Tail recommendation accuracy, exhibiting low susceptibility to sparsity, even in its most extreme manifestations -- the Cold-Start problems. At the same time EigenRec has an attractive computational profile and it can apply readily in large-scale recommendation settings.
Hybrid European MV-LV Network Models for Smart Distribution Network Modelling
Authors: Matthew Deakin, David Greenwood, Sara Walker, Phil Taylor
Date: 2020-09-29 00:00:00

A pair of European-style, integrated MV-LV circuits are presented, created by combining generic MV and real LV networks. The two models have 86,000 and 113,000 nodes, and are made readily available for download in the OpenDSS file format. Primary substation tap change controls and MV-LV feeders are represented as three-phase unbalanced distribution network models, capturing the coupling of voltages at the MV level. The assumptions made in constructing the models are outlined, including a preconditioning step that reduces the number of nodes by more than five times without affecting the solution. Two flexibility-based case studies are presented, with TSO-DSO and peer-peer-based smart controls considered. The demonstration of the heterogeneous nature of these systems is corroborated by the analysis of measured LV voltage data. The models are intended to aid the development of algorithms for maximising the benefits of smart devices within the context of whole energy systems.
A Parallel Trajectory Swapping Wang - Landau Study Of The HP Protein Model
Authors: Luke Kristopher Davis
Date: 2016-07-12 00:00:00
Tasks: Protein Folding

The HP model of protein folding, where the chain exists in a free medium, is investigated using a parallel Monte Carlo scheme based upon Wang-Landau sampling. Expanding on the work of Wust and Landau by introducing a lesser known replica -exchange scheme between individual Wang- Landau samplers, the problem of dynamical trapping (spiking in the density of states) was avoided and an enhancement in the efficiency of traversing configuration space was obtained. Highlighting dynamical trapping as an issue for lattice polymer simulations for increasing lengths is explicitly done here for the first time. The 1/t scheme is also integrated within this sophisticated Monte Carlo methodology. A trial move set was developed which includes pull, bond re-bridging, pivot, kink-flip and a newly invented and implemented fragment random walk move which allowed rapid exploration of high and low temperature configurations. A native state search was conducted leading to the attainment of the native states of the benchmark sequences of 2D50 (-21), 2D60 (-36)and 2D64 (-42), whilst attaining minimum energies close to the native state for 2D85(-52 NATIVE= -53), 2D100a (-47 NATIVE= -48) and 2D100b (-49 NATIVE=-50). Thermodynamic observables such as the specific heat, internal energy, entropy and free energy were computed for 2D benchmark sequences at varying temperatures and folding and unfolding behaviour was investigated. Lattice polymers with monomeric hydrophobic structure were also studied in the same manner with the recording of minimum energy values and thermodynamic behaviour. The native results for the benchmark sequences and lattice polymers were compared with varying computational methods.
Combining Weighted Total Variation and Deep Image Prior for natural and medical image restoration via ADMM
Authors: Pasquale Cascarano, Andrea Sebastiani, Maria Colomba Comes, Giorgia Franchini, Federica Porta
Date: 2020-09-23 00:00:00
Tasks: Image Restoration, SSIM

In the last decades, unsupervised deep learning based methods have caught researchers attention, since in many real applications, such as medical imaging, collecting a great amount of training examples is not always feasible. Moreover, the construction of a good training set is time consuming and hard because the selected data have to be enough representative for the task. In this paper, we focus on the Deep Image Prior (DIP) framework and we propose to combine it with a space-variant Total Variation regularizer with an automatic estimation of the local regularization parameters. Differently from other existing approaches, we solve the arising minimization problem via the flexible Alternating Direction Method of Multipliers (ADMM). Furthermore, we provide a specific implementation also for the standard isotropic Total Variation. The promising performances of the proposed approach, in terms of PSNR and SSIM values, are addressed through several experiments on simulated as well as real natural and medical corrupted images.
Can quantum physics help solve the hard problem of consciousness? A hypothesis based on entangled spins and photons
Authors: Christoph Simon
Date: 2018-09-08 00:00:00
Tasks: Philosophy, Unity

The hard problem of consciousness is the question how subjective experience arises from brain matter. I suggest exploring the possibility that quantum physics could be part of the answer. The simultaneous unity and complexity of subjective experience is difficult to understand from a classical physics perspective. In contrast, quantum entanglement is naturally both complex and unified. Moreover the concept of matter is much more subtle in quantum physics compared to classical physics, and quantum computing shows that quantum effects can be useful for information processing. Building on recent progress in quantum technology and neuroscience, I propose a concrete hypothesis as a basis for further investigation, namely that subjective experience is related to the dynamics of a complex entangled state of spins, which is continuously generated and updated through the exchange of photons. Spins in condensed matter systems at room or body temperature can have coherence times in the relevant range for subjective experience (milliseconds to seconds). Photons are well suited for distributing entanglement over macroscopic distances. Neurons emit photons, reactive oxygen species in the mitochondria being likely sources. Opsins, light-sensitive proteins that are plausible single-photon detectors, exist in the brain and are evolutionarily conserved, suggesting that they serve a function. We have recently shown by detailed numerical modeling that axons can plausibly act as photonic waveguides. The oxygen molecule, which has non-zero electronic spin and emits photons, might serve as an interface between photons and spins. The achievable photon rates seem to be more than sufficient to support the bandwidth of subjective experience. The proposed hypothesis raises many interesting experimental and theoretical questions in neuroscience, quantum physics, evolutionary biology, psychophysics, and philosophy.
Structural diverseness of neurons between brain areas and between cases
Authors: Ryuta Mizutani, Rino Saiga, Yoshiro Yamamoto, Masayuki Uesugi, Akihisa Takeuchi, Kentaro Uesugi, Yasuko Terada, Yoshio Suzuki, Vincent De Andrade, Francesco De Carlo, Susumu Takekoshi, Chie Inomoto, Naoya Nakamura, Youta Torii, Itaru Kushima, Shuji Iritani, Norio Ozaki, Kenichi Oshima, Masanari Itokawa, Makoto Arai
Date: 2020-07-01 00:00:00

The cerebral cortex is composed of multiple cortical areas that exert a wide variety of brain functions. Although human brain neurons are genetically and areally mosaic, the three-dimensional structural differences between neurons in different brain areas or between the neurons of different individuals have not been delineated. Here, we report a nanometer-scale geometric analysis of brain tissues of the superior temporal gyrus of 4 schizophrenia and 4 control cases by using synchrotron radiation nanotomography. The results of the analysis and a comparison with results for the anterior cingulate cortex indicated that 1) neuron structures are dissimilar between brain areas and that 2) the dissimilarity varies from case to case. The structural diverseness was mainly observed in terms of the neurite curvature that inversely correlates with the diameters of the neurites and spines. The analysis also revealed the geometric differences between the neurons of the schizophrenia and control cases, suggesting that neuron structure is associated with brain function. The area dependency of the neuron structure and its diverseness between individuals should represent the individuality of brain functions.
Stochasticity and heterogeneity in the transmission dynamics of SARS-CoV-2
Authors: Benjamin M. Althouse, Edward A. Wenger, Joel C. Miller, Samuel V. Scarpino, Antoine Allard, Laurent Hébert-Dufresne, Hao Hu
Date: 2020-05-27 00:00:00

SARS-CoV-2 causing COVID-19 disease has moved rapidly around the globe, infecting millions and killing hundreds of thousands. The basic reproduction number, which has been widely used and misused to characterize the transmissibility of the virus, hides the fact that transmission is stochastic, is dominated by a small number of individuals, and is driven by super-spreading events (SSEs). The distinct transmission features, such as high stochasticity under low prevalence, and the central role played by SSEs on transmission dynamics, should not be overlooked. Many explosive SSEs have occurred in indoor settings stoking the pandemic and shaping its spread, such as long-term care facilities, prisons, meat-packing plants, fish factories, cruise ships, family gatherings, parties and night clubs. These SSEs demonstrate the urgent need to understand routes of transmission, while posing an opportunity that outbreak can be effectively contained with targeted interventions to eliminate SSEs. Here, we describe the potential types of SSEs, how they influence transmission, and give recommendations for control of SARS-CoV-2.
An optimal home energy management system for modulating heat pumps and photovoltaic systems
Authors: Lissy Langer, Thomas Volling
Date: 2020-09-04 00:00:00
Tasks: energy management, Management

Efficient residential sector coupling plays a key role in supporting the energy transition. In this study, we analyze the structural properties associated with the optimal control of a home energy management system and the effects of common technological configurations and objectives. We conduct this study by modeling a representative building with a modulating air-sourced heat pump, a photovoltaic (PV) system, a battery, and thermal storage systems for floor heating and hot-water supply. In addition, we allow grid feed-in by assuming fixed feed-in tariffs and consider user comfort. In our numerical analysis, we find that the battery, naturally, is the essential building block for improving self-sufficiency. However, in order to use the PV surplus efficiently grid feed-in is necessary. The commonly considered objective of maximizing self-consumption is not economically viable under the given tariff structure; however, close-to-optimal performance and significant reduction in solution times can be achieved by maximizing self-sufficiency. Based on optimal control and considering seasonal effects, the dominant order of PV distribution and the target states of charge of the storage systems can be derived. Using a rolling horizon approach, the solution time can be reduced to less than 1 min (achieving a time resolution of 1 h per year). By evaluating the value of information, we find that the common value of 24 h for the prediction and control horizons results in unintended but avoidable end-of-horizon effects. Our input data and mixed-integer linear model developed using the Julia JuMP programming language are available in an open-source manner.
Effective containment explains sub-exponential growth in confirmed cases of recent COVID-19 outbreak in Mainland China
Authors: Benjamin F. Maier, Dirk Brockmann
Date: 2020-02-18 00:00:00

The recent outbreak of COVID-19 in Mainland China is characterized by a distinctive algebraic, sub-exponential increase of confirmed cases during the early phase of the epidemic, contrasting an initial exponential growth expected for an unconstrained outbreak with sufficiently large reproduction rate. Although case counts vary significantly between affected provinces in Mainland China, the scaling law $t^{\mu}$ is surprisingly universal, with a range of exponents $\mu=2.1\pm0.3$. The universality of this behavior indicates that despite social, regional, demographical, geographical, and socio-economical heterogeneities of affected Chinese provinces, this outbreak is dominated by fundamental mechanisms that are not captured by standard epidemiological models. We show that the observed scaling law is a direct consequence of containment policies that effectively deplete the susceptible population. To this end we introduce a parsimonious model that captures both, quarantine of symptomatic infected individuals as well as population wide isolation in response to mitigation policies or behavioral changes. For a wide range of parameters, the model reproduces the observed scaling law in confirmed cases and explains the observed exponents. Quantitative fits to empirical data permit the identification of peak times in the number of asymptomatic or oligo-symptomatic, unidentified infected individuals, as well as estimates of local variations in the basic reproduction number. The model implies that the observed scaling law in confirmed cases is a direct signature of effective contaiment strategies and/or systematic behavioral changes that affect a substantial fraction of the susceptible population. These insights may aid the implementation of containment strategies in potential export induced COVID-19 secondary outbreaks elsewhere or similar future outbreaks of other emergent infectious diseases.
Deep Completion Autoencoders for Radio Map Estimation
Authors: Yves Teganya, Daniel Romero
Date: 2020-05-11 00:00:00
Tasks: Management

Radio maps provide metrics such as power spectral density for every location in a geographic area and find numerous applications such as UAV communications, interference control, spectrum management, resource allocation, and network planning to name a few. Radio maps are constructed from measurements collected by spectrum sensors distributed across space. Since radio maps are complicated functions of the spatial coordinates due to the nature of electromagnetic wave propagation, model-free approaches are strongly motivated. Nevertheless, all existing schemes for radio occupancy map estimation rely on interpolation algorithms unable to learn from experience. In contrast, this paper proposes a novel approach in which the spatial structure of propagation phenomena such as shadowing is learned beforehand from a data set with measurements in other environments. Relative to existing schemes, a significantly smaller number of measurements is therefore required to estimate a map with a prescribed accuracy. As an additional novelty, this is also the first work to estimate radio occupancy maps using deep neural networks. Specifically, a fully convolutional deep completion autoencoder architecture is developed to effectively exploit the manifold structure of this class of maps.
Speaker dependent articulatory-to-acoustic mapping using real-time MRI of the vocal tract
Authors: Tamás Gábor Csapó
Date: 2020-08-03 00:00:00

Articulatory-to-acoustic (forward) mapping is a technique to predict speech using various articulatory acquisition techniques (e.g. ultrasound tongue imaging, lip video). Real-time MRI (rtMRI) of the vocal tract has not been used before for this purpose. The advantage of MRI is that it has a high `relative' spatial resolution: it can capture not only lingual, labial and jaw motion, but also the velum and the pharyngeal region, which is typically not possible with other techniques. In the current paper, we train various DNNs (fully connected, convolutional and recurrent neural networks) for articulatory-to-speech conversion, using rtMRI as input, in a speaker-specific way. We use two male and two female speakers of the USC-TIMIT articulatory database, each of them uttering 460 sentences. We evaluate the results with objective (Normalized MSE and MCD) and subjective measures (perceptual test) and show that CNN-LSTM networks are preferred which take multiple images as input, and achieve MCD scores between 2.8-4.5 dB. In the experiments, we find that the predictions of speaker `m1' are significantly weaker than other speakers. We show that this is caused by the fact that 74% of the recordings of speaker `m1' are out of sync.
Robust MPC for Linear Systems with Parametric and Additive Uncertainty: A Novel Constraint Tightening Approach
Authors: Monimoy Bujarbaruah, Ugo Rosolia, Yvonne R Stürz, Xiaojing Zhang, Francesco Borrelli
Date: 2020-07-02 00:00:00

We propose a novel approach to design a robust Model Predictive Controller (MPC) for constrained uncertain linear systems. The uncertain system is modeled as linear parameter varying with additive disturbance. Set bounds for the system matrices and the additive uncertainty are assumed to be known. We formulate a novel optimization-based constraint tightening strategy around a predicted nominal trajectory which utilizes these bounds. With an appropriately designed terminal cost function and constraint set, we prove robust satisfaction of the imposed constraints by the resulting MPC in closed-loop with the uncertain system, and Input to State Stability of the origin. We highlight the efficacy of our proposed approach via a numerical example.
Bayesian Nonparametric Inference of Switching Linear Dynamical Systems
Authors: Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
Date: 2010-03-19 00:00:00

Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hierarchical Dirichlet process prior to learn an unknown number of persistent, smooth dynamical modes. We additionally employ automatic relevance determination to infer a sparse set of dynamic dependencies allowing us to learn SLDS with varying state dimension or switching VAR processes with varying autoregressive order. We develop a sampling algorithm that combines a truncated approximation to the Dirichlet process with efficient joint sampling of the mode and state sequences. The utility and flexibility of our model are demonstrated on synthetic data, sequences of dancing honey bees, the IBOVESPA stock index, and a maneuvering target tracking application.