Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolving Towards Mobility-as-a-Service: Drivers and Consequences of Car Subscription Services

ABSTRACT:This study investigates the emergence of car subscription services as a new development in the area of Mobility-as-a-Service (MaaS). Our aim is to explore the drivers of the emergence of car subscription services, analyse its impact on the incumbent manufacturers, and investigate the impact of COVID-19. We adopt a qualitative approach based on in-depth, semi-structured interviews with managers of three car subscription service providers: a leading car manufacturer and two car subscription start-ups. We found three key behavioural and technological trends that drive the emergence and growth of car subscription services and highlighted the various opportunities and risks for the incumbent manufacturers.
Learning to select useful landmarks

To navigate effectively, an autonomous agent must be able to quickly and accurately determine its current location. Given an initial estimate of its position (perhaps based on dead-reckoning) and an image taken of a known environment, our agent first attempts to locate a set of landmarks (real-world objects at known locations), then uses their angular separation to obtain an improved estimate of its current position. Unfortunately, some landmarks may not be visible, or worse, may be confused with other landmarks, resulting in both time wasted in searching for the undetected landmarks, and in further errors in the agent's estimate of its position. To address these problems, we propose a method that uses previous experiences to learn a selection function that, given the set of landmarks that might be visible, returns the subset that can be used to reliably provide an accurate registration of the agent's position. We use statistical techniques to prove that the learned selection function is, with high probability, effectively at a local optimum in the space of such functions. This paper also presents empirical evidence, using real-world data, that demonstrate the effectiveness of our approach.
Stream Reasoning in Temporal Datalog

In recent years, there has been an increasing interest in extending traditional stream processing engines with logical, rule-based, reasoning capabilities. This poses significant theoretical and practical challenges since rules can derive new information and propagate it both towards past and future time points; as a result, streamed query answers can depend on data that has not yet been received, as well as on data that arrived far in the past. Stream reasoning algorithms, however, must be able to stream out query answers as soon as possible, and can only keep a limited number of previous input facts in memory. In this paper, we propose novel reasoning problems to deal with these challenges, and study their computational properties on Datalog extended with a temporal sort and the successor function (a core rule-based language for stream reasoning applications).
Perceptron Trees: A Case Study In Hybrid Concept Representations

The paper presents a case study in examining the bias of two particular formalisms: decision trees and linear threshold units. The immediate result is a new hybrid representation, called a perceptron tree, and an associated learning algorithm called the perceptron tree error correction procedure. The longer term result is a model for exploring issues related to understanding representational bias and constructing other useful hybrid representations.
Improving Cross-Domain Recommendation through Probabilistic Cluster-Level Latent Factor Model

Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the sparsity problem appearing in single rating domains. However, previous models only assume that multiple domains share a latent common rating pattern based on the user-item co-clustering. To capture diversities among different domains, we propose a novel Probabilistic Cluster-level Latent Factor (PCLF) model to improve the cross-domain recommendation performance. Experiments on several real world datasets demonstrate that our proposed model outperforms the state-of-the-art methods for the cross-domain recommendation task.
Presentation Trainer: what experts and computers can tell about your nonverbal communication

The ability to present effectively is essential for professionals; therefore, oral communication courses have become part of the curricula for higher education studies. However, speaking in public is still a challenge for many graduates. To tackle this problem, driven by the recent advances in computer vision techniques and prosody analysis, multimodal tools have been designed to support the development of public speaking skills. One of these tools is the Presentation Trainer, a research prototype able to provide learners with real-time feedback on a set of nonverbal communication aspects. Despite initial positive evaluations, the application still lacks grounding in a valid assessment model for nonverbal communication aspects in the context of presentations. To come up with such a model, we conducted semi-structured inter- views with experts in the public speaking domain. Furthermore, the objective of these interviews was also to have a formative evaluation of the Presentation Trainer, analysing how it suits with common practices for teaching and learning public speaking skills. The results of this study iden- tify 131 nonverbal communication practices that affect the quality of a presentation and summa- rize experts’ points of view regarding multimodal public speaker instructors.
EWLS: A New Local Search for Minimum Vertex Cover

A number of algorithms have been proposed for the Minimum Vertex Cover problem. However, they are far from satisfactory, especially on hard instances. In this paper, we introduce Edge Weighting Local Search (EWLS), a new local search algorithm for the Minimum Vertex Cover problem. EWLS is based on the idea of extending a partial vertex cover into a vertex cover. A key point of EWLS is to find a vertex set that provides a tight upper bound on the size of the minimum vertex cover. To this purpose, EWLS employs an iterated local search procedure, using an edge weighting scheme which updates edge weights when stuck in local optima. Moreover, some sophisticated search strategies have been taken to improve the quality of local optima. Experimental results on the broadly used DIMACS benchmark show that EWLS is competitive with the current best heuristic algorithms, and outperforms them on hard instances. Furthermore, on a suite of difficult benchmarks, EWLS delivers the best results and sets a new record on the largest instance.
Etude multi-échelle de l'érosion de contact au sein des ouvrages hydrauliques en terre

L'erosion de contact est un type d'erosion interne qui se developpe a l'interface entre deux couches de materiaux de granulometries differentes. Les particules d'un sol fin (sable, limon, argile…) sont detachees par l'ecoulement et entrainees a travers les pores du sol grossier au contact (gravier…). Bien que l'on suspecte sa presence dans de nombreux ouvrages, ce processus d'erosion a ete peu etudie jusqu'a present. Aussi ce travail de these s'est attache a mieux le comprendre pour parvenir a le modeliser. A l'echelle du pore, l'ecoulement a l'interface entre deux milieux poreux a ete caracterise, grâce a un dispositif experimental developpe au Cemagref d'Aix-en-Provence qui combine fluorescence induite par laser, methode PIV et milieu iso-indice. L'importance de la variabilite des sollicitations hydrauliques a ainsi ete soulignee. A l'echelle de l'echantillon, des essais sur sols reels et sols reconstitues ont ete menes au LTHE, afin d'identifier les phenomenes en jeu. Ont egalement ete obtenues sur ce dispositif des mesures du taux d'erosion en fonction de l'intensite de l'ecoulement, pour differents types de sols fins et de sols grossiers. Une modelisation stochastique de ces essais a ensuite ete proposee. Enfin, des essais a grande echelle ont ete conduits au laboratoire de la Compagnie Nationale du Rhone pour etudier l'eventuelle influence d'effets d'echelle ainsi que les consequences de cette erosion de contact sur le comportement global et l'integrite d'un ouvrage. Il a ainsi ete mis en evidence la possibilite qu'une erosion de conduit soit initiee par erosion de contact. Cette these a ete realisee dans le cadre du projet ERINOH (ERosion INterne dans les Ouvrages Hydrauliques), en convention CIFRE avec le Centre d'Ingenierie Hydraulique d'EDF.
A Robust Bayesian Truth Serum for Small Populations ( Technical Report )

Peer prediction methods allow the truthful elicitation of private signals (e.g., experiences, or opinions) in regard to a true world state when this ground truth is unobservable. The original peer prediction method is incentive compatible for any finite number of agents n ≥ 2 but critically relies on a common prior, shared by all agents and the center. The Bayesian Truth Serum (BTS) relaxes this assumption. While it still assumes that the agents share a common prior, this prior need not be known by the center. However, BTS is proven to be incentive compatible only for a large enough number of agents, and this number depends on the prior and is thus unknown to the mechanism. In this paper, we present a robust BTS for the elicitation of binary information which is incentive compatible for any n ≥ 3, taking advantage of a particularity of the quadratic scoring rule. Our mechanism is the first peer prediction method that does not rely on knowledge of the common prior to provide strict incentive compatibility for any n ≥ 3. Moreover, and in contrast to the original BTS, our mechanism is numerically robust and ex post individually rational.
Phenomenological model of nuclear relaxation in the normal state of YBa2Cu3O7.

A phenomenological model of a system of antiferromagnetically correlated spins is shown to give a good quantitative description of NMR, nuclear-quadrupole-resonance, and Knight-shift measurements on yttrium, planar copper, and planar oxygen sites in YBa{sub 2}Cu{sub 3}O{sub 7}. The antiferromagnetic correlation length is estimated to be {similar to}2.5 lattice constants at {ital T}=100 K. The temperature dependence of the correlation length ceases at {ital T}{sub {ital x}}{congruent}100 K. The enhancement of the observed relaxation rates over what is expected for weakly interacting electrons is calculated and shown to be large. Extension of the calculation to other cuprate superconductors is discussed.
Towards Clause-Learning State Space Search : Learning to Recognize Dead-Ends ( Extended Abstract )

The ability to learn from conflicts is a key algorithm ingredient in constraint satisfaction (e. g. [6, 24, 20, 22, 8, 2]). For state space search, like goal reachability in classical planning which we consider here, progress in this direction has been elusive, and almost entirely limited to length-bounded reachability, where reachability testing reduces to a constraint satisfaction problem, yet requires iterating over different length bounds until some termination criterion applies [5, 19, 16, 28]. But do we actually need a length bound to be able to do conflict analysis and nogood learning in state space search? Arguably, the canonical form of a “conflict” in state space search is a dead-end state, from which no solution (of any length) exists. Such conflicts are not as ubiquitous as in constraint satisfaction (including length-bounded reachability), yet they do occur, e. g. , in oversubscription planning [26], in planning with limited resources [11], in singleagent puzzles [15, 4], and in explicit-state model checking of safety properties [7] where a dead-end is any state from which the error property cannot be reached. We introduce a method that learns sound and generalizable knowledge from deadend states during state space search classical planning. To our knowledge, this is the first of its kind. Prodigy [21] comes closest with its learning of sound action-pruning rules in backward search. Inspired by Prodigy, Bhatnagar and Mostow [3] considered forwardsearch conflict-based learning, yet their techniques are not sound (do not guarantee that pruned states actually are dead-ends). Kolobov et al’s SixthSense technique [17] is sound, yet is placed in probabilistic planning and incorporates classical planning as a sub-procedure. Value function refinement using Bellman updates [18, 25, 1] will eventually learn that a state is a dead-end, yet does not generalize that knowledge. The key to our technique are critical-path heuristics h [10, 9], relative to a setC of atomic conjunctions. These heuristics incorporate an approximation allowing to break up conjunctive subgoals into the elements of C. We don’t give a full definition here, but the central equation should be suitable to get an idea:
A human pancreatic ribonuclease variant kills cancer cells by apoptosis and reduces the expression of P-glycoprotein in MDR cell lines

En aquesta tesi s'han estudiat les propietats antitumorals d'una variant de la ribonucleasa pancreatica humana anomenada PE5 que incorpora un senyal de localitzacio nuclear. Aquest estudi mostra que PE5 indueix l'apoptosi de les cel·lules tractades i que aquesta mort es independent de l'activitat de p53. A mes, l'efecte citotoxic no es veu afectat per un fenotip de resistencia a multiples drogues. Les dades tambe mostren que l'activitat citotoxica de PE5 es selectiva per a cel·lules tumorals in vitro i que la capacitat citotoxica de les dues ribonucleases es semblant. S'ha estudiat l'efecte d'aquestes dues ribonucleases sobre el cicle cel·lular, l'activacio de diferents caspases i l'expressio de proteines relacionades amb l'apoptosi i el cicle cel·lular. Els resultats indiquen que PE5 i l'onconasa maten les cel·lules a traves de mecanismes diferents. A mes, PE5 pero no l'onconasa, redueix l'acumulacio de glicoproteina-P en dues linies cel·lulars resistents a multiples drogues.
A Prototype Intelligent Assistant to Help Dysphagia Patients Eat Safely At Home

For millions of people with swallowing disorders, preventing potentially deadly aspiration pneumonia requires following prescribed safe eating strategies. But adherence is poor, and caregivers’ ability to encourage adherence is limited by the onerous and socially aversive need to monitoring another’s eating. We have developed an early prototype for an intelligent assistant that monitors adherence and provides feedback to the patient, and tested monitoring precision with healthy subjects for one strategy called a “chin tuck. ” Results indicate that adaptations of current generation machine vision and personal assistant technologies could effectively monitor chin tuck adherence, and suggest the feasibility of a more general assistant that encourages adherence to a wide range of safe eating strategies.
Classification of Resting State fMRI Datasets Using Dynamic Network Clusters

Resting state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating intrinsic and spontaneous brain activity. The application of univariate and multivariate methods such as multi voxel pattern analysis has been instrumental in localizing neural correlates to various cognitive states and psychiatric disease. However, many existing methods of rsfMRI analysis are insufficient for investigating the true mechanism of brain activity since they make implicit assumptions that are agnostic of the temporal and spatial dynamics of brain activity. The proposed method aims to create a superior feature space for representing brain activity using k-means and to create interpretable generalizations on these features for studying group differences using support vector machine classifiers. ​
The Utility of Text: The Case of Amicus Briefs and the Supreme Court (Supplementary material)

= ∑ j∈J σ ψj,1c s − ξ(∆1 − θ1) if 0 < ∆d < 1 where σ′ is the first order derivative of the vote probability sigmoid function. Optimality drives all topics (with positive weight) to have equal marginal values.2 The marginal value highlights the tradeoffs an amicus faces, in four components: (i) the cost of deviating from the merits, i.e., a large difference between ∆d and θd, is ξ; (ii) a justice whose σ′ is large, i.e., whose vote is uncertain, receives greater attention, in particular (iii) on issues she cares about, i.e., ψj,d is large, with (iv) c controlling the strength of correspondence to issues justices care about.
Using Matched Samples to Estimate the Effects of Exercise on Mental Health via Twitter

Recent work has demonstrated the value of social media monitoring for health surveillance (e.g., tracking influenza or depression rates). It is an open question whether such data can be used to make causal inferences (e.g., determining which activities lead to increased depression rates). Even in traditional, restricted domains, estimating causal effects from observational data is highly susceptible to confounding bias. In this work, we estimate the effect of exercise on mental health from Twitter, relying on statistical matching methods to reduce confounding bias. We train a text classifier to estimate the volume of a user's tweets expressing anxiety, depression, or anger, then compare two groups: those who exercise regularly (identified by their use of physical activity trackers like Nike+), and a matched control group. We find that those who exercise regularly have significantly fewer tweets expressing depression or anxiety; there is no significant difference in rates of tweets expressing anger. We additionally perform a sensitivity analysis to investigate how the many experimental design choices in such a study impact the final conclusions, including the quality of the classifier and the construction of the control group.
A Cascaded Inception of Inception Network with Attention Modulated Feature Fusion for Human Pose Estimation Submission ID : 2065

Accurate keypoint localization of human pose needs diversified features: the high level for contextual dependencies and low level for detailed refinement of joints. However, the importance of the two factors varies from case to case, but how to efficiently use the features is still an open problem. Existing methods have limitations in preserving low level features, adaptively adjusting the importances of different levels of features, and modeling the human perception process. This paper presents three novel techniques step by step to efficiently utilize different levels of features for human pose estimation. Firstly, an inception of inception (IOI) block is designed to emphasize the low level features. Secondly, an attention mechanism is proposed to adjust the importances of individual levels according to the context. Thirdly, a cascaded network is proposed to sequentially localize the joints to enforce message passing from joints of stand-alone parts like head and torso to remote joints like wrist or ankle. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on both MPII and LSP bench-
Generating natural language description of human behavior from video images

In visual surveillance applications, it is becoming popular to perceive video images and to interpret them using natural language concepts. We propose an approach to generating a natural language description of human behavior appearing in real video images. First, a head region of a human, on behalf of the whole body, is extracted from each frame. Using a model based method, three dimensional pose and position of the head are estimated. Next, the trajectory of these parameters is divided into segments of monotonous motions. For each segment, we evaluate conceptual features such as degree of change of pose and position and that of relative distance to some objects in the surroundings, and so on. By calculating the product of these feature values, a most suitable verb is selected and other syntactic elements are supplied. Finally natural language text is generated using a technique of machine translation.
Draining the Water Hole: Mitigating Social Engineering Attacks

Cyber adversaries have increasingly leveraged social engineering attacks to breach large organizations and threaten the well-being of today's online users. One clever technique, the ``watering hole'' attack, compromises a legitimate website to execute drive-by download attacks by redirecting users to another malicious domain. We introduce a game-theoretic model that captures the salient aspects for an organization protecting itself from a watering hole attack by altering the environment information in web traffic so as to deceive the attackers. Our main contributions are (1) a novel Social Engineering Deception (SED) game model that features a continuous action set for the attacker, (2) an in-depth analysis of the SED model to identify computationally feasible real-world cases, and (3) an iterative algorithm which solves for the optimal protection policy using (i) a characterization of websites that may be compromised, (ii) an LP-relaxation with optimality condition, and (iii) the column generation method. A Chrome extension is being built to field our algorithms in the real world.
Greater Intermanual Transfer in the Elderly Suggests Age-Related Bilateral Motor Cortex Activation Is Compensatory

Hemispheric lateralization of movement control diminishes with age; whether this is compensatory or maladaptive is debated. The authors hypothesized that if compensatory, bilateral activation would lead to greater intermanual transfer in older subjects learning tasks that activate the cortex unilaterally in young adults. They studied 10 young and 14 older subjects, learning a unimanual visuomotor task comprising a feedforward phase, where there is unilateral cortical activation in young adults, and a feedback phase, which activates the cortex bilaterally in both age groups. Increased intermanual transfer was demonstrated in older subjects during feedforward learning, with no difference between groups during feedback learning. This finding is consistent with bilateral cortical activation being compensatory to maintain performance despite declining computational efficiency in neural networks.
Post-correlation RFI detection

Radio astronomy is entering a new era with new and future radio observatories such as the Low-Frequency Array and the Square Kilometer Array. We describe in detail an automated flagging pipeline and evaluate its performance. With only a fraction of the computational cost of correlation and its use of the previously introduced SumThreshold method, it is found to be both fast and unrivalled in its high accuracy. The LOFAR radio environment is analysed with the help of this pipeline. The high time and spectral resolution of LOFAR have resulted in an observatory where only a few percent of the data is lost due to RFI.
Linear Discriminant Analysis

THE PROBLEM to be considered here is that of identifying, or of classifying, an observed individual as being a member of one of two "populations. " This problem arises in some form in most sciences. A recent example is the problem, associated with certain international ten sions, of classifying salmon caught in the North Pacific fishery as having arisen from the Asiatic or American salmon populations. The populations are to be considered as giv ing rise to observable individuals each of which may be (partially ) characterized by a set of k measurements. The measurements of individuals from either population are distributed as if they were independent observations on a multivariate distribution of probability. These distributions are assumed to be multivariate normal, with known parameters, for each population.