Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Urban Mobility: When Mobility Systems Meet Smart Data

Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050. The economic contribution of the transport sector represents 5% of the GDP in Europe and costs an average of US $\$ $ 482.05 billion in the United States. These figures indicate the rapid rise of industrial cities and the urgent need to move from traditional cities to smart cities. This article provides a survey of different approaches and technologies such as intelligent transportation systems (ITS) that leverage communication technologies to help maintain road users safe while driving, as well as support autonomous mobility through the optimization of control systems. The role of ITS is strengthened when combined with accurate artificial intelligence models that are built to optimize urban planning, analyze crowd behavior and predict traffic conditions. AI-driven ITS is becoming possible thanks to the existence of a large volume of mobility data generated by billions of users through their use of new technologies and online social media. The optimization of urban planning enhances vehicle routing capabilities and solves traffic congestion problems, as discussed in this paper. From an ecological perspective, we discuss the measures and incentives provided to foster the use of mobility systems. We also underline the role of the political will in promoting open data in the transport sector, considered as an essential ingredient for developing technological solutions necessary for cities to become healthier and more sustainable.
ChewBaccaNN: A Flexible 223 TOPS/W BNN Accelerator

Binary Neural Networks enable smart IoT devices, as they significantly reduce the required memory footprint and computational complexity while retaining a high network performance and flexibility. This paper presents ChewBaccaNN, a 0.7 mm2 sized binary convolutional neural network (CNN) accelerator designed in GlobalFoundries 22 nm technology. By exploiting efficient data re-use, data buffering, latch-based memories, and voltage scaling, a throughput of 241 GOPS is achieved while consuming just 1.1 mW at 0.4V/154MHz during inference of binary CNNs with up to 7×7 kernels, leading to a peak core energy efficiency of 223 TOPS/W. ChewBaccaNN's flexibility allows to run a much wider range of binary CNNs than other accelerators, drastically improving the accuracy-energy tradeoff beyond what can be captured by the TOPS/W metric. In fact, it can perform CIFAR-10 inference at 86.8% accuracy with merely 1.3 J, thus exceeding the accuracy while at the same time lowering the energy cost by 2.8× compared to even the most efficient and much larger analog processing-in-memory devices, while keeping the flexibility of running larger CNNs for higher accuracy when needed. It also runs a binary ResNet-18 trained on the 1000-class ILSVRC dataset and improves the energy efficiency by 4.4× over accelerators of similar flexibility. Furthermore, it can perform inference on a binarized ResNet-18 trained with 8-bases Group-Net to achieve a 67.5% Top-1 accuracy with only 3.0mJ/frame—at an accuracy drop of merely 1.8% from the fullprecision ResNet-18.
Small‐Scale Fragmentation Effects on Local Genetic Diversity in Two Phyllostomid Bats with Different Dispersal Abilities in Panama

Habitat fragmentation is one of the greatest threats to biodiversity. Despite their importance for conservation, the genetic consequences of small‐scale habitat fragmentation for bat populations are largely unknown. In this study, we linked genetic with ecological and demographic data to assess the effects of habitat fragmentation on two species of phyllostomid bats (Uroderma bilobatum and Carollia perspicillata) that differ in their dispersal abilities and demographic response to fragmentation. We hypothesized that population differentiation and the effect of habitat fragmentation on levels of genetic diversity will be a function of the species' mobility. We sequenced mtDNA from 232 bats caught on 11 islands in Gatún Lake, Panamá, isolated from the mainland for ca 90 yr, and in adjacent, continuous forest on the mainland. Populations of both species showed significant genetic differentiation (FST). Consistent with our prediction, population subdivision was lower in the highly mobile U. bilobatum (FST= 0.01) compared to the less vagile C. perspicillata (FST= 0.06), and only the latter species showed a pattern indicative of isolation by distance and, in addition, an effect of fragmentation. Genetic erosion as a result of fragmentation was also only detectable in the less mobile species, C. perspicillata, where haplotype diversity was lower in island compared to mainland populations. Our results suggest that some Neotropical bat species are prone to loss of genetic variation in response to anthropogenic small‐scale habitat fragmentation. In this context, our findings point toward mobility as a good predictor of a species' vulnerability to fragmentation and altered population genetic structure.
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.
Emerging Polynomial Growth Trends in COVID-19 Pandemic Data and Their Reconciliation with Compartment Based Models

We study the reported data from the COVID-19 pandemic outbreak in January - May 2020 in 119 countries. We observe that the time series of active cases in individual countries (the difference of the total number of confirmed infections and the sum of the total number of reported deaths and recovered cases) display a strong agreement with polynomial growth and at a later epidemic stage also with a combined polynomial growth with exponential decay. Our results are also formulated in terms of compartment type mathematical models of epidemics. Within these models the universal scaling characterizing the observed regime in an advanced epidemic stage can be interpreted as an algebraic decay of the relative reproduction number $R_0$ as $T_M/t$, where $T_M$ is a constant and $t$ is the duration of the epidemic outbreak. We show how our findings can be applied to improve predictions of the reported pandemic data and estimate some epidemic parameters. Note that although the model shows a good agreement with the reported data we do not make any claims about the real size of the pandemics as the relation of the observed reported data to the total number of infected in the population is still unknown.
Towards Automatic building of Human-Machine Conversational System to support Maintenance Processes

Companies are dealing with many cognitive changes with the introduction of the Industry 4.0 paradigm. In this constantly changing environment, knowledge management is a key factor. Dialog systems, being able to hold a conversation with humans, could support the knowledge management in business environment. Although, these systems are currently hand-coded and need the intervention of a human being in writing all the possible questions and answers, and then planning the interactions. This process, besides being time-consuming, is not scalable. Conversely, a dialog system, also referred to as chatbot, can be built from scratch by simply extracting rules from technical documentation. So, the goal of this research is designing a methodology for automatic building of human-machine conversational system, able to interact in an industrial environment. An initial taxonomy, containing entities expected to be found in maintenance manuals, is used to identify the relevant sentences of a manual provided by the company BOBST SA and applying text mining techniques, it is automatically expanded. The final result is a taxonomy network representing the entities and their relation, that will be used in future works for managing the interactions of a maintenance chatbot.
Structural origin of plasticity in strained high-entropy alloy

High-entropy alloys (HEAs) are solid solutions of multiple elements with equal atomic ratios which present an innovative pathway for de novo alloy engineering. While there exist extensive studies to ascertain the important structural aspects governing their mechanical behaviors, elucidating the underlying deformation mechanisms still remains a challenge. Using atomistic simulations, we probe the particle rearrangements in a yielding, model HEA system to understand the structural origin of its plasticity. We find the plastic deformation is initiated by irreversible topological fluctuations which tend to spatially localize in regions termed as soft spots which consist of particles actively participating in slow vibrational motions, an observation strikingly reminiscent of nonlinear glassy rheology. Due to the varying local elastic moduli resulting from the loss of compositional periodicity, these plastic responses exhibit significant spatial heterogeneity and are found to be inversely correlated with the distribution of local electronegativity. Further mechanical loading promotes the cooperativity among these local plastic events and triggers the formation of dislocation loops. As in strained crystalline solids, different dislocation loops can further merge together and propagate as the main carrier of large-scale plastic deformation. However, the energy barriers located at the spatial regions with higher local electronegativity severely hinders the motion of dislocations. By delineating the transient mechanical response in terms of atomic configuration, our computational findings shed new light on understanding the nature of plasticity of single-phase HEA.
Power Cyber-Physical System Risk Area Prediction Using Dependent Markov Chain and Improved Grey Wolf Optimization

Existing power cyber-physical system (CPS) risk prediction results are inaccurate as they fail to reflect the actual physical characteristics of the components and the specific operational status. A new method based on dependent Markov chain for power CPS risk area prediction is proposed in this paper. The load and constraints of the non-uniform power CPS coupling network are first characterized, and can be utilized as a node state judgment standard. Considering the component node isomerism and interdependence between the coupled networks, a power CPS risk regional prediction model based on dependent Markov chain is then constructed. A cross-adaptive gray wolf optimization algorithm improved by adaptive position adjustment strategy and cross-optimal solution strategy is subsequently developed to optimize the prediction model. Simulation results using the IEEE 39-BA 110 test system verify the effectiveness and superiority of the proposed method.
Space–time singularities and cosmic censorship conjecture: A Review with some thoughts

The singularity theorems of Hawking and Penrose tell us that singularities are common place in general relativity. Singularities not only occur at the beginning of the Universe at the Big Bang, but also in complete gravitational collapses that result in the formation of black holes. If singularities - except the one at the Big Bang - ever become "naked", i.e., not shrouded by black hole horizons, then it is expected that problems would arise and render general relativity indeterministic. For this reason, Penrose proposed the cosmic censorship conjecture, which states that singularities should never be naked. Various counterexamples to the conjecture have since been discovered, but it is still not clear under which kind of physical processes one can expect violation of the conjecture. In this short review, I briefly examine some progresses in spacetime singularities and cosmic censorship conjecture. In particular, I shall discuss why we should still care about the conjecture, and whether we should be worried about some of the counterexamples. This is not meant to be a comprehensive review, but rather to give an introduction to the subject, which has recently seen an increase of interest.
Topology and axions in QCD

QCD axions are at the crossroads of QCD topology and Dark Matter searches. We present here the current status of topological studies on the lattice, and their implication on axion physics. We outline the specific challenges posed by lattice topology, the different proposals for handling them, the observable effects of topology on the QCD spectrum and its interrelation with chiral and axial symmetries. We review the transition to the Quark Gluon Plasma, the fate of topology at the transition, and the approach to the high temperature limit. We discuss the extrapolations needed to reach the regime of cosmological relevance, and the resulting constraints on the QCD axion.
Recognition of 26 Degrees of Freedom of Hands Using Model-based approach and Depth-Color Images

In this study, we present an model-based approach to recognize full 26 degrees of freedom of a human hand. Input data include RGB-D images acquired from a Kinect camera and a 3D model of the hand constructed from its anatomy and graphical matrices. A cost function is then defined so that its minimum value is achieved when the model and observation images are matched. To solve the optimization problem in 26 dimensional space, the particle swarm optimization algorimth with improvements are used. In addition, parallel computation in graphical processing units (GPU) is utilized to handle computationally expensive tasks. Simulation and experimental results show that the system can recognize 26 degrees of freedom of hands with the processing time of 0.8 seconds per frame. The algorithm is robust to noise and the hardware requirement is simple with a single camera.
Dynamic shrinkage in time‐varying parameter stochastic volatility in mean models

Summary Successful forecasting models strike a balance between parsimony and flexibility. This is often achieved by employing suitable shrinkage priors that penalize model complexity but also reward model fit. In this article, we modify the stochastic volatility in mean (SVM) model by introducing state‐of‐the‐art shrinkage techniques that allow for time variation in the degree of shrinkage. Using a real‐time inflation forecast exercise, we show that employing more flexible prior distributions on several key parameters sometimes improves forecast performance for the United States, the United Kingdom, and the euro area (EA). Comparing in‐sample results reveals that our proposed model yields qualitatively similar insights to the original version of the model.
CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations

This paper describes the winning contribution to SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection (Subtask 2) handed in by team UG Student Intern. We present an ensemble model that makes predictions based on context-free and context-dependent word representations. The key findings are that (1) context-free word representations are a powerful and robust baseline, (2) a sentence classification objective can be used to obtain useful context-dependent word representations, and (3) combining those representations increases performance on some datasets while decreasing performance on others.
A Category Theory Approach to Interoperability

In this article, we propose a Category Theory approach to (syntactic) interoperability between linguistic tools. The resulting category consists of textual documents, including any linguistic annotations, NLP tools that analyze texts and add additional linguistic information, and format converters. Format converters are necessary to make the tools both able to read and to produce different output formats, which is the key to interoperability. The idea behind this document is the parallelism between the concepts of composition and associativity in Category Theory with the NLP pipelines. We show how pipelines of linguistic tools can be modeled into the conceptual framework of Category Theory and we successfully apply this method to two real-life examples.
Fair and Efficient Allocations under Subadditive Valuations

We study the problem of allocating a set of indivisible goods among agents with subadditive valuations in a fair and efficient manner. Envy-Freeness up to any good (EFX) is the most compelling notion of fairness in the context of indivisible goods. Although the existence of EFX is not known beyond the simple case of two agents with subadditive valuations, some good approximations of EFX are known to exist, namely 1/2-EFX allocation and EFX allocations with bounded charity. Nash welfare (the geometric mean of agents' valuations) is one of the most commonly used measures of efficiency. In case of additive valuations, an allocation that maximizes Nash welfare also satisfies fairness properties like Envy-Free up to one good (EF1). Although there is substantial work on approximating Nash welfare when agents have additive valuations, very little is known when agents have subadditive valuations. In this paper, we design a polynomial-time algorithm that outputs an allocation that satisfies either of the two approximations of EFX as well as achieves an O(n) approximation to the Nash welfare. Our result also improves the current best-known approximation of O(n log n) and O(m) to Nash welfare when agents have submodular and subadditive valuations, respectively. Furthermore, our technique also gives an O(n) approximation to a family of welfare measures, p-mean of valuations for p in (-\infty, 1], thereby also matching asymptotically the current best approximation ratio for special cases like p = -\infty while also retaining the remarkable fairness properties.
A Review on Impact of Bloom Filter on Named Data Networking: The Future Internet Architecture

Today is the era of smart devices. Through the smart devices, people remain connected with systems across the globe even in mobile state. Hence, the current Internet is facing scalability issue. Therefore, leaving IP based Internet behind due to scalability, the world is moving to the Future Internet Architecture, called Named Data Networking (NDN). Currently, the number of nodes connected to the Internet is in billions. And, the number of requests sent is in millions per second. NDN handles such huge numbers by modifying the IP architecture to meet the current requirements. NDN is scalable, produces less traffic and congestion, provides high level security, saves bandwidth, efficiently utilizes multiple network interfaces and have many more functionalities. Similarly, Bloom Filter is the only good choice to deploy in various modules of NDN to handle the huge number of packets. Bloom Filter is a simple probabilistic data structure for the membership query. This article presents a detailed discussion on the role of Bloom Filter in implementing NDN. The article includes a precise discussion on Bloom Filter and the main components of the NDN architecture, namely, packet, content store, forward information base and pending interest table are also discussed briefly.
An Improved Topic Masking Technique for Authorship Analysis

Authorship verification (AV) is an important sub-area of digital text forensics and has been researched for more than two decades. The fundamental question addressed by AV is whether two documents were written by the same person. A serious problem that has received little attention in the literature so far is the question if AV methods actually focus on the writing style during classification, or whether they are unintentionally distorted by the topic of the documents. To counteract this problem, we propose an effective technique called POSNoise, which aims to mask topic-related content in documents. In this way, AV methods are forced to focus on those text units that are more related to the author's writing style. Based on a comprehensive evaluation with eight existing AV methods applied to eight corpora, we demonstrate that POSNoise is able to outperform a well-known topic masking approach in 51 out of 64 cases with up to 12.5% improvement in terms of accuracy. Furthermore, we show that for corpora preprocessed with POSNoise, the AV methods examined often achieve higher accuracies (improvement of up to 20.6%) compared to the original corpora.