Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeted Sentiment Analysis: A Data-Driven Categorization

Targeted sentiment analysis (TSA), also known as aspect based sentiment analysis (ABSA), aims at detecting fine-grained sentiment polarity towards targets in a given opinion document. Due to the lack of labeled datasets and effective technology, TSA had been intractable for many years. The newly released datasets and the rapid development of deep learning technologies are key enablers for the recent significant progress made in this area. However, the TSA tasks have been defined in various ways with different understandings towards basic concepts like `target' and `aspect'. In this paper, we categorize the different tasks and highlight the differences in the available datasets and their specific tasks. We then further discuss the challenges related to data collection and data annotation which are overlooked in many previous studies.
The Unsuspected Dynamics of the Regulative Pillar: The Case of Faute Inexcusable in France

Institutional work translates actors’ capacity to bring about change in an institutionalised practice through its creation, continuation or destruction. This research examines political institutional work in order to understand actors’ activity in the regulative pillar of the institution. With its focus on the cognitive and normative institutional pillars, the existing literature has underestimated the regulative pillar’s contribution to institutional change, ignoring part of its role in institutional change. We propose to examine the status conferred on this regulative dimension through a qualitative study of an institutionalised practice, the legal concept of Faute Inexcusable in France from 1898 to 2012. We use secondary data including legal data, contextualised with other sources, and interviews with two key actors in the field. We show the existence of a genuine “regulative dynamic”; beneath its apparent stability, the regulative pillar is in fact the setting for institutional struggles that lead institutionalised practices to evolve. We highlight sequences of institutional work in which various forms of political institutional work interact. This research advances understanding of institutional dynamics in the regulative pillar, casting light on actors and mechanisms that have so far gone unreported. The article thus contributes to a rehabilitation of the regulative pillar’s role in neo-institutional theory.
Differential Voting in Case Based Spam Filtering

Case-based reasoning (CBR) has been shown to be of considerable utility in a spam-filtering task. In the course of this study, we propose that the non-random skewed distribution of the cases in a case base is crucial, especially in the context of a classification task like spam filtering. In this paper, we propose approaches to improve the performance of a CBR spam filter by making use of the non-random nature of the case base. We associate each case in the case base with a voting power, which is essentially a function that incorporates the knowledge of the local neighborhood of the case. We show that the performance of the spam filter can be considerably improved by making use of such techniques that incorporate the voting powers.
On-Demand Routing Technology Based on Task-Relay in Partially Connected Ad Hoc Networks

In partially connected Ad Hoc networks (PCANETs), the whole structure is partitioned into several unconnected regions. To address the communication problem between these regions, an on-demand routing technology based on task-relay (OR-TR) is proposed. Single or multiple task-relay nodes are adopted to act as the special communication medium in PCANETs. Simulations have been conducted to study the effects of task-relay nodes' moving speed and communication distance on the transmission delay.
Grand Challenge of 106-Point Facial Landmark Localization

Facial landmark localization is a very crucial step in numerous face related applications, such as face recognition, facial pose estimation, face image synthesis, etc. However, previous competitions on facial landmark localization (i.e., the 300-W, 300-VW and Menpo challenges) aim to predict 68-point landmarks, which are incompetent to depict the structure of facial components. In order to overcome this problem, we construct a challenging dataset, named JD-landmark. Each image is manually annotated with 106-point landmarks. This dataset covers large variations on pose and expression, which brings a lot of difficulties to predict accurate landmarks. We hold a 106-point facial landmark localization competition1 on this dataset in conjunction with IEEE International Conference on Multimedia and Expo (ICME) 2019. The purpose of this competition is to discover effective and robust facial landmark localization approaches.
A Catalog of Known Galactic K-M Stars of Class I Candidate Red Supergiants in Gaia DR2

We investigate individual distances and luminosities of a sample of 889 nearby candidate red supergiants (RSGs) with reliable parallaxes (ϖ/σϖ > 4 and RUWE < 2.7) from Gaia Data Release 2 (DR2). The sample was extracted from the historical compilation of spectroscopically derived spectral types by Skiff, and consists of K-M stars that are listed with class I at least once. The sample includes well-known RSGs from Humphreys, Elias et al., Jura & Kleinmann, and Levesque et al. Infrared and optical measurements from the Two Micron All Sky Survey, Catalog of Infrared Observations (CIO), Midcourse Space Experiment, Wide-field Infrared Survey Explorer, MIPSGAL, Galactic Legacy Infrared Midplane Extraordinaire (GLIMPSE), and The Naval Observatory Merged Astrometric Dataset catalogs allow us to estimate the stellar bolometric magnitudes. We analyze the stars in the luminosity versus effective temperature plane and confirm that 43 sources are highly probably RSGs with M bol < −7.1 mag. Of the stars in the sample, 43% have masses >7 M⊙. Another ≈30% of the sample consists of giant stars.
Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function

At present, lesion segmentation is still performed manually (or semi-automatically) by medical experts. To facilitate this process, we contribute a fully-automatic lesion segmentation pipeline. This work proposes a method as a part of the LiTS (Liver Tumor Segmentation Challenge) competition for ISBI 17 and MICCAI 17 comparing methods for automatics egmentation of liver lesions in CT scans. By utilizing cascaded, densely connected 2D U-Nets and a Tversky-coefficient based loss function, our framework achieves very good shape extractions with high detection sensitivity, with competitive scores at time of publication. In addition, adjusting hyperparameters in our Tversky-loss allows to tune the network towards higher sensitivity or robustness.
Experimental Investigation on Compressive Toughness of the PVA-Steel Hybrid Fiber Reinforced Cementitious Composites

In order to achieve the whole process control of crack formation of engineered cementitious composites (ECC) under loading and improve the mechanical properties of ECC, PVA-steel hybrid fiber reinforced cementitious composites (HyFRCC) was proposed by mixing steel fibers with traditional ECC. And the key parameters to affect the HyFRCC performance are PVA fiber content, steel fiber content and aspect ratio of steel fiber. In order to investigate the mechanical properties of PVA-steel HyFRCC such as the compressive toughness, 15 PVA-steel HyFRCC specimens with different volume fraction of steel fiber and PVA fiber, together with 1 non-fibrous cementitious composites specimen and 3 ECC specimens with PVA fiber only for comparison were designed and uniaxial compression tests were conducted. Furthermore, the effect of fiber content, fiber type and fiber geometrical characteristic on the compressive toughness of HyFRCC were analyzed. The results show that PVA fiber content, steel fiber content and aspect ratio of steel fiber have little impact on the uniaxial compressive strength of the PVA-steel HyFRCC, while the peak strain, compressive toughness and the post-peak ductility of PVA-steel HyFRCC are improved significantly due to fiber hybridization. The formation of large scale cracks of ECC specimens after post-peak loading is restrained effectively with the addition of steel fiber. The mixture of PVA and steel fiber has a remarkable effect on the improvement of ECC compression toughness and crack control. It indicates that the energy dissipation and damage control capacity of the structural component with PVA-steel HyFRCC under seismic loading will be enhanced.
Achievable MSE lower bounds in non-Bayesian Biased estimation

In this paper, a new structured approach for obtaining uniformly best biased (UBB) estimators, in the mean-square-error (MSE) sense, is established. We show that if a UBB estimator exists, then it is uniquely given by the locally best biased (LBB) estimator. A necessary and sufficient condition for the existence of a UBB estimator is derived, and it is shown that if there exists an optimal bias, such that this condition is satisfied, then it is unique, and the UBB estimator is directly obtained from the LBB estimator. The UBB estimator is derived in a non-linear Gaussian estimation problem. In comparison to the maximum-likelihood estimator, we show that the UBB estimator exhibits superior estimation performance in the MSE sense.
Evidence of charge density wave transverse pinning by x-ray microdiffraction

Incommensurate charge density waves (CDW) have the extraordinary ability to display non-Ohmic behavior when submitted to an external field. The mechanism leading to this non trivial dynamics is still not well understood, although recent experimental studies tend to prove that it is due to solitonic transport. Solitons could come from the relaxation of the strained CDW within an elastic-to-plastic transition. However, the nucleation process and the transport of these charged topological objects have never been observed at the local scale until now. In this letter, we use in-situ scanning x-ray micro-diffraction with micrometer resolution of a NbSe$_3$ sample designed to have sliding and non-sliding areas. Direct imaging of the charge density wave deformation is obtained using an analytical approach based on the phase gradient to disentangle the transverse from the longitudinal components over a large surface of a hundred microns size. We show that the CDW dissociates itself from the host lattice in the sliding regime and displays a large transverse deformation, ten times larger than the longitudinal one and strongly dependent on the amplitude and the direction of the applied currents. This deformation continuously extends across the macroscopic sample dimensions, over a distance 10 000 times greater than the CDW wavelength despite the presence of strong defects while remaining strongly pinned by the lateral surfaces. This 2D quantitative study highlights the prominent role of shear effect that should play a significant role in the nucleation of solitons.
The sum of edge lengths in random linear arrangements

Spatial networks are networks where nodes are located in a space equipped with a metric. Typically, the space is two-dimensional and until recently and traditionally, the metric that was usually considered was the Euclidean distance. In spatial networks, the cost of a link depends on the edge length, i.e. the distance between the nodes that define the edge. Hypothesizing that there is pressure to reduce the length of the edges of a network requires a null model, e.g. a random layout of the vertices of the network. Here we investigate the properties of the distribution of the sum of edge lengths in random linear arrangement of vertices, that has many applications in different fields. A random linear arrangement consists of an ordering of the elements of the nodes of a network being all possible orderings equally likely. The distance between two vertices is one plus the number of intermediate vertices in the ordering. Compact formulae for the 1st and 2nd moments about zero as well as the variance of the sum of edge lengths are obtained for arbitrary graphs and trees. We also analyze the evolution of that variance in Erdős–Rényi graphs and its scaling in uniformly random trees. Various developments and applications for future research are suggested.
Effective Subdivision Algorithm for Isolating Zeros of Real Systems of Equations, with Complexity Analysis

We describe a new algorithm Miranda for isolating the simple zeros of a function \boldsymbolf :\mathbbR ^n\to\mathbbR ^n within a box B_0\subseteq\mathbbR ^n. The function \boldsymbolf and its partial derivatives must have interval forms, but need not be polynomial. Our subdivision-based algorithm is "effective'' in the sense that our algorithmic description also specifies the numerical precision that is sufficient to certify an implementation with any standard BigFloat number type. The main predicate is the Moore-Kioustelidis (MK) test, based on Miranda's Theorem (1940). Although the MK test is well-known, this paper appears to be the first synthesis of this test into a complete root isolation algorithm. We provide a complexity analysis of our algorithm based on intrinsic geometric parameters of the system. Our algorithm and complexity analysis are developed using 3 levels of description (Abstract, Interval, Effective). This methodology provides a systematic pathway for achieving effective subdivision algorithms in general.
A Method for Detecting Structural Breaks and an Application to the Turkish Stock Market

We suggest a procedure for model update, based on detection of structural breaks at unknown change-points. The procedure makes use of the SupF test introduced by Andrews (1993). We apply this procedure for modelling the common stock index returns in the Istanbul Stock Exchange for the 11 year period of 1989 - 1999. The underlying model consists simply of a mean plus noise, with occasional jumps in the level of mean at unknown time instances. The problem is the detection of this jump and the corresponding model update. We find critical values for the SupF test statistic by using the Bootstrap method. A trading rule that uses the forecasts from the suggested procedure is observed to outperform the buy-and-hold strategy.
Optimal-Time Dictionary-Compressed Indexes

We describe the first self-indexes able to count and locate pattern occurrences in optimal time within a space bounded by the size of the most popular dictionary compressors. To achieve this result, we combine several recent findings, including string attractors—new combinatorial objects encompassing most known compressibility measures for highly repetitive texts—and grammars based on locally consistent parsing. More in detail, letγ be the size of the smallest attractor for a text T of length n. The measureγ is an (asymptotic) lower bound to the size of dictionary compressors based on Lempel–Ziv, context-free grammars, and many others. The smallest known text representations in terms of attractors use space O(γ log (n/γ)), and our lightest indexes work within the same asymptotic space. Let ε > 0 be a suitably small constant fixed at construction time, m be the pattern length, and occ be the number of its text occurrences. Our index counts pattern occurrences in O(m+log 2+ε n) time and locates them in O(m+(occ+1)log ε n) time. These times already outperform those of most dictionary-compressed indexes, while obtaining the least asymptotic space for any index searching within O((m+occ),polylog, n) time. Further, by increasing the space to O(γ log (n/γ)log ε n), we reduce the locating time to the optimal O(m+occ), and within O(γ log (n/γ)log n) space we can also count in optimal O(m) time. No dictionary-compressed index had obtained this time before. All our indexes can be constructed in O(n) space and O(nlog n) expected time. As a by-product of independent interest, we show how to build, in O(n) expected time and without knowing the sizeγ of the smallest attractor (which is NP-hard to find), a run-length context-free grammar of size O(γ log (n/γ)) generating (only) T. As a result, our indexes can be built without knowingγ.
Inflammatory cytokines IL-1β and TNF-α regulate p75NTR expression in CNS neurons and astrocytes by distinct cell-type-specific signalling mechanisms

The p75NTR (where NTR is neurotrophin receptor) can mediate many distinct cellular functions, including cell survival and apoptosis, axonal growth and cell proliferation, depending on the cellular context. This multifunctional receptor is widely expressed in the CNS (central nervous system) during development, but its expression is restricted in the adult brain. However, p75NTR is induced by a variety of pathophysiological insults, including seizures, lesions and degenerative disease. We have demonstrated previously that p75NTR is induced by seizures in neurons, where it induces apoptosis, and in astrocytes, where it may regulate proliferation. In the present study, we have investigated whether the inflammatory cytokines IL (interleukin)-1β and TNF-α (tumour necrosis factor-α), that are commonly elevated in these pathological conditions, mediate the regulation of p75NTR in neurons and astrocytes. We have further analysed the signal transduction pathways by which these cytokines induce p75NTR expression in the different cell types, specifically investigating the roles of the NF-κB (nuclear factor κB) and p38 MAPK (mitogen-activated protein kinase) pathways. We have demonstrated that both cytokines regulate p75NTR expression; however, the mechanisms governing this regulation are cytokine- and cell-type specific. The distinct mechanisms of cytokine-mediated p75NTR regulation that we demonstrate in the present study may facilitate therapeutic intervention in regulation of this receptor in a cell-selective manner.
A multidimensional analog to the Burrows-Wheeler transform

We show how to perform multidimensional pattern matching over an $n$-dimensional grid of text spanning a total of $s$ characters with nength, an analog to the Burrows-Wheeler transform. Nength exploits a Fourier duality between two kinds of grid products to map a search problem that naively takes $\mathcal{O}(s^2)$ arithmetic operations to an equivalent problem that takes $\mathcal{O}(s \log s)$ arithmetic operations.
Intra-Frame Object Tracking by Deblatting

Objects moving at high speed along complex trajectories often appear in videos, especially videos of sports. Such objects elapse non-negligible distance during exposure time of a single frame and therefore their position in the frame is not well defined. They appear as semi-transparent streaks due to the motion blur and cannot be reliably tracked by standard trackers. We propose a novel approach called Tracking by Deblatting based on the observation that motion blur is directly related to the intra-frame trajectory of an object. Blur is estimated by solving two intertwined inverse problems, blind deblurring and image matting, which we call deblatting. The trajectory is then estimated by fitting a piecewise quadratic curve, which models physically justifiable trajectories. As a result, tracked objects are precisely localized with higher temporal resolution than by conventional trackers. The proposed TbD tracker was evaluated on a newly created dataset of videos with ground truth obtained by a high-speed camera using a novel Trajectory-IoU metric that generalizes the traditional Intersection over Union and measures the accuracy of the intra-frame trajectory. The proposed method outperforms baseline both in recall and trajectory accuracy.
Non-Gaussianity in the weak lensing correlation function likelihood – implications for cosmological parameter biases

We study the significance of non-Gaussianity in the likelihood of weak lensing shear two-point correlation functions, detecting significantly non-zero skewness and kurtosis in 1D marginal distributions of shear two-point correlation functions in simulated weak lensing data. We examine the implications in the context of future surveys, in particular LSST, with derivations of how the non-Gaussianity scales with survey area. We show that there is no significant bias in 1D posteriors of Ωm and σ8 due to the non-Gaussian likelihood distributions of shear correlations functions using the mock data (100 deg2). We also present a systematic approach to constructing approximate multivariate likelihoods with 1D parametric functions by assuming independence or more flexible non-parametric multivariate methods after decorrelating the data points using principal component analysis (PCA). While the use of PCA does not modify the non-Gaussianity of the multivariate likelihood, we find empirically that the 1D marginal sampling distributions of the PCA components exhibit less skewness and kurtosis than the original shear correlation functions. Modelling the likelihood with marginal parametric functions based on the assumption of independence between PCA components thus gives a lower limit for the biases. We further demonstrate that the difference in cosmological parameter constraints between the multivariate Gaussian likelihood model and more complex non-Gaussian likelihood models would be even smaller for an LSST-like survey. In addition, the PCA approach automatically serves as a data compression method, enabling the retention of the majority of the cosmological information while reducing the dimensionality of the data vector by a factor of ∼5.
Botulinum Toxin Modulates Posterior Parietal Cortex Activation in Post-stroke Spasticity of the Upper Limb

Post-stroke spasticity (PSS) is effectively treated with intramuscular botulinum toxin type A (BoNT-A), although the clinical improvement is likely mediated by changes at the central nervous system level. Using functional magnetic resonance imaging (fMRI) of the brain, this study aims to confirm and locate BoNT-A-related changes during motor imagery with the impaired hand in severe PSS. Temporary alterations in primary and secondary sensorimotor representation of the impaired upper limb were expected. Thirty chronic stroke patients with upper limb PSS undergoing comprehensive treatment including physiotherapy and indicated for BoNT treatment were investigated. A change in PSS of the upper limb was assessed with the modified Ashworth scale (MAS). fMRI and clinical assessments were performed before (W0) and 4 weeks (W4) and 11 weeks (W11) after BoNT-A application. fMRI data were acquired using 1.5-Tesla scanners during imagery of finger-thumb opposition sequences with the impaired hand. At the group level, we separately modeled (1) average activation at each time point with the MAS score and age at W0 as covariates; and (2) within-subject effect of BoNT-A and the effect of time since W0 as independent variables. Comprehensive treatment of PSS with BoNT-A significantly decreased PSS of the upper limb with a maximal effect at W4. Task-related fMRI prior to treatment (W0) showed extensive activation of bilateral frontoparietal sensorimotor cortical areas, bilateral cerebellum, and contralesional basal ganglia and thalamus. After BoNT-A application (W4), the activation extent decreased globally, mostly in the bilateral parietal cortices and cerebellum, but returned close to baseline at W11. The intra-subject contrast revealed a significant BoNT-A effect, manifesting as a transient decrease in the activation of the ipsilesional intraparietal sulcus and superior parietal lobule. We demonstrate that BoNT-A treatment of PSS of the upper limb is associated with transient changes in the ipsilesional posterior parietal cortex, possibly resulting from temporarily altered sensorimotor upper limb representations.