Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring the impact of automated evaluation tools on alternative text quality: a web translation study

The number of Internet users has increased tenfold since the beginning of the century up to present, especially thanks to the improvements experienced in web accessibility and the growing number of languages which online content is available in. While translation professionals are making a considerable contribution to that digital information richness, little evidence exists regarding their involvement in the achievement of a more accessible web for all. In this paper, we present the main results of the first empirical study on web accessibility conceived around a translation task. The experiment sought to particularly investigate the quality of image text alternatives produced by French translators with the help of two evaluation tools: aDesigner and Acrolinx. The assessment of their alt text proposals, carried out by seven screen reader users, suggests that using both tools helps translators to create more appropriate text alternatives than when trying to do so with only one tool or without any automated support. A more in-depth analysis of the data gathered shows that Acrolinx offers better guidance than aDesigner for translators to render images accessible.
Prognostic significance of activated CD8(+) T cell infiltrations within esophageal carcinomas.

The purpose of this study was to explore whether there is a linkage between the infiltration of CD8(+) T cells and the risk of death from esophageal cancer. Cases of 70 consecutive patients in whom esophageal squamous cell carcinomas ( n = 33) or adenocarcinomas (n = 37) were R0-resected between 1993 and 1999 were reviewed. The presence of activated CD8(+) T cells was evaluated by quantitative real-time PCR and immunohistochemistry and compared to clinical and pathological stages. The primary end point analyzed was overall survival, and a multivariate analysis was performed to distinguish any factors conferring an improved survivorship. Intratumoral (i.t.) CD8(+) T cells accumulating within the epithelial complexes were detected in 11 of all (16%) cases: in 9 of 25 (36%) patients with Union Internationale Contrele Cancer stage I or II versus 2 of 45 (4%) patients with Union Internationale Contrele Cancer stage III or IV (P = 0.001). Intratumoral CD8(+) T cell infiltrations showed proliferative activity and also IFN-gamma secretion. The presence of i.t. CD8(+) T cell infiltration more than peritumoral infiltration was associated with a good prognosis in both squamous cell and adenocarcinomas. Multivariate analysis showed that i.t. CD8(+) T cell infiltration was an independent prognostic factor (hazard ratio, 0.5; P = 0.0004) indicating favorable outcome. In conclusion, the presence of CD8(+) T cell infiltration in esophageal carcinomas is a favorable prognostic factor that should have diagnostic and therapeutic implications.
Asset pricing with general transaction costs: Theory and numerics

We study risk‐sharing equilibria with general convex costs on the agents' trading rates. For an infinite‐horizon model with linear state dynamics and exogenous volatilities, we prove that the equilibrium returns mean‐revert around their frictionless counterparts—the deviation has Ornstein‐Uhlenbeck dynamics for quadratic costs whereas it follows a doubly‐reflected Brownian motion if costs are proportional. More general models with arbitrary state dynamics and endogenous volatilities lead to multidimensional systems of nonlinear, fully‐coupled forward‐backward SDEs. These fall outside the scope of known well‐posedness results, but can be solved numerically using the simulation‐based deep‐learning approach of Han, Jentzen, and E (2018). In a calibration to time series of prices and trading volume, realistic liquidity premia are accompanied by a moderate increase in volatility. The effects of different cost specifications are rather similar, justifying the use of quadratic costs as a proxy for other less tractable specifications.
Improving web clustering through a new modeling for web documents

The constant and rapid growth of the Web complexity and the Web size generates new challenges regarding the approaches in efficient processing of Web searched results. Due to the dynamic Web content and the huge amount of information returned by search engines, it is necessary to find new methods and ways for better organizing and modelling the information spread on the Web. In this paper, we propose the structuring of the Web content as a hierarchical environment, taking into account the site content and structure, the HTML document structure and the term importance. Furthermore, we propose an effective partitional clustering algorithm for a Web site. The preliminary results prove the effectiveness of the new Web content representation and the accuracy of the Web clustering algorithm.
Combined Hybrid ARQ and Link Adaptation for Coded Cooperation in Block-Fading Channels

Cooperative hybrid ARQ (HARQ) protocols have been widely studied in recent years and they were shown to provide higher efficiency than cooperative protocols. This paper proposes a joint design within which the HARQ protocol collaborates with the link adaptation functionality and the cooperative transmission scheme in order to adapt the behavior of the HARQ protocol to the QoS requirements of the supported services. As performance metrics of the HARQ protocol, we use the expected number of retransmissions and the expected spectral efficiency. The paper also studies the influence of the protocol parameters upon the performance provided.
Knives are picked before slices are cut: recognition through activity sequence analysis

In this paper, we introduce a model to classify cooking activities using their visual and temporal coherence information. We fuse multiple feature descriptors for fine-grained activity recognition as we would need every single detail to catch even subtle differences between classes with low inter-class variance. Considering the observation that daily activities such as cooking are likely to be performed in sequential patterns of activities, we also model temporal coherence of activities. By combining both aspects, we show that we can improve the overall accuracy of cooking recognition tasks.
Similarity Grouping-Guided Neural Network Modeling for Maritime Time Series Prediction

Reliable and accurate prediction of time series plays a crucial role in the maritime industry, such as economic investment, transportation planning, port planning, design, and so on. The dynamic growth of maritime time series has the predominantly complex, and nonlinear and non-stationary properties. To guarantee high-quality prediction performance, we propose to first adopt the empirical mode decomposition (EMD) and ensemble EMD (EEMD) methods to decompose the original time series into high- and low-frequency components. The low-frequency components can be easily predicted directly through traditional neural network (NN) methods. It is more difficult to predict high-frequency components due to their properties of weak mathematical regularity. To take advantage of the inherent self-similarities within high-frequency components, these components will be divided into several continuous small (overlapping) segments. The grouped segments with high similarities are then selected to form more proper training datasets for traditional NN methods. This regrouping strategy can assist in enhancing the prediction accuracy of high-frequency components. The final prediction result is obtained by integrating the predicted high- and low-frequency components. Our proposed three-step prediction frameworks benefit from the time series decomposition and similar segments grouping. The experiments on both port cargo throughput and vessel traffic flow have illustrated its superior performance in terms of prediction accuracy and robustness.
Type II critical behavior of gravitating magnetic monopoles

I study type II critical collapse in the spherically symmetric gravitating magnetic monopole system. This is an Einstein-Yang-Mills-Higgs system with two matter fields: a field parametrizing the scalar field gauged under SU(2) and a field parametrizing the gauge field. This system offers interesting differences compared to what is commonly found for type II collapse in other systems. For example, instead of the critical solution sitting between collapse and complete dispersal of the matter fields, on the non-black hole side of the critical solution, the matter fields settle down to a static and stable configuration. More interesting, however, is that I find strong evidence for the existence of two critical solutions, each with their own set of scaling and echoing exponents, which I determine numerically.
Formal Synthesis of Stochastic Systems via Control Barrier Certificates

This article focuses on synthesizing control policies for discrete-time stochastic control systems together with a lower bound on the probability that the systems satisfy the complex temporal properties. The desired properties of the system are expressed as linear temporal logic specifications over finite traces. In particular, our approach decomposes the given specification into simpler reachability tasks based on its automata representation. We, then, propose the use of so-called control barrier certificate to solve those simpler reachability tasks along with computing the corresponding controllers and probability bounds. Finally, we combine those controllers to obtain a hybrid control policy solving the considered problem. Under some assumptions, we also provide two systematic approaches for uncountable and finite input sets to search for control barrier certificates. We demonstrate the effectiveness of the proposed approach on a room temperature control and lane keeping of a vehicle modeled as a four-dimensional single-track kinematic model. We compare our results with the discretization-based methods in the literature.
Differential Recurrent Neural Network and its Application for Human Activity Recognition

The Long Short-Term Memory (LSTM) recurrent neural network is capable of processing complex sequential information since it utilizes special gating schemes for learning representations from long input sequences. It has the potential to model any sequential time-series data, where the current hidden state has to be considered in the context of the past hidden states. This property makes LSTM an ideal choice to learn the complex dynamics present in long sequences. Unfortunately, the conventional LSTMs do not consider the impact of spatio-temporal dynamics corresponding to the given salient motion patterns, when they gate the information that ought to be memorized through time. To address this problem, we propose a differential gating scheme for the LSTM neural network, which emphasizes on the change in information gain caused by the salient motions between the successive video frames. This change in information gain is quantified by Derivative of States (DoS), and thus the proposed LSTM model is termed as differential Recurrent Neural Network (dRNN). In addition, the original work used the hidden state at the last time-step to model the entire video sequence. Based on the energy profiling of DoS, we further propose to employ the State Energy Profile (SEP) to search for salient dRNN states and construct more informative representations. The effectiveness of the proposed model was demonstrated by automatically recognizing human actions from the real-world 2D and 3D single-person action datasets. We point out that LSTM is a special form of dRNN. As a result, we have introduced a new family of LSTMs. Our study is one of the first works towards demonstrating the potential of learning complex time-series representations via high-order derivatives of states.
Valproate Inhibits Methamphetamine Induced Hyperactivity via Glycogen Synthase Kinase 3β Signaling in the Nucleus Accumbens Core

Valproate (VPA) has recently been shown to influence the behavioral effects of psycho-stimulants. Although glycogen synthase kinase 3β (GSK3β) signaling in the nucleus accumbens (NAc) plays a key role in mediating dopamine (DA)-dependent behaviors, there is less direct evidence that how VPA acts on the GSK3β signaling in the functionally distinct sub-regions of the NAc, the NAc core (NAcC) and the NAc shell (NAcSh), during psycho-stimulant-induced hyperactivity. In the present study, we applied locomotion test after acute methamphetamine (MA) (2 mg/kg) injection to identify the locomotor activity of rats received repeated VPA (300 mg/kg) pretreatment. We next measured phosphor-GSK3β at serine 9 and total GSK3β levels in NAcC and NAcSh respectively to determine the relationship between the effect of VPA on MA-induced hyperlocomotor and changes in GSK3β activity. We further investigated whether microinjection of VPA (300 μg/0.5 μl/side, once daily for 7 consecutive days) into NAcC or NAcSh could affect hyperactivity induced by MA. Our data indicated that repeated VPA treatment attenuated MA-induced hyperlocomotor, and the effect was associated with decreased levels of phosphorylated GSK3β at Ser 9 in the NAcC. Moreover, repeated bilateral intra-NAcC, but not intra-NAcSh VPA treatment, significantly attenuated MA-induced hyperactivity. Our results suggested that GSK3β activity in NAcC contributes to the inhibitory effects of VPA on MA-induced hyperactivity.
On Restricted Ternary Words and Insets

We investigate combinatorial properties of a kind of insets we defined in an earlier paper, interpreting them now in terms of restricted ternary words. This allows us to give new combinatorial interpretations of a number of known integer sequences, namely the coefficients of Chebyshev polynomials of both kinds, Fibonacci numbers, Delannoy numbers, asymmetric Delannoy numbers, Sulanke numbers, coordinating sequences for some cubic lattices, crystal ball sequences for some cubic lattices, and others. We also obtain several new properties of said insets. In particular, we derive three generating functions when two of three variables are constant. At the end, we state 40 combinatorial configurations counted by our words.
Hierarchical Importance Weighted Autoencoders

Importance weighted variational inference (Burda et al., 2015) uses multiple i.i. d. samples to have a tighter variational lower bound. We believe a joint proposal has the potential of reducing the number of redundant samples, and introduce a hierarchical structure to induce correlation. The hope is that the proposals would coordinate to make up for the error made by one another to reduce the variance of the importance estimator. Theoretically, we analyze the condition under which convergence of the estimator variance can be connected to convergence of the lower bound. Empirically, we confirm that maximization of the lower bound does implicitly minimize variance. Further analysis shows that this is a result of negative correlation induced by the proposed hierarchical meta sampling scheme, and performance of inference also improves when the number of samples increases.
Disentangling Content and Style via Unsupervised Geometry Distillation

It is challenging to disentangle an object into two orthogonal spaces of content and style since each can influence the visual observation differently and unpredictably. It is rare for one to have access to a large number of data to help separate the influences. In this paper, we present a novel framework to learn this disentangled representation in a completely unsupervised manner. We address this problem in a two-branch Autoencoder framework. For the structural content branch, we project the latent factor into a soft structured point tensor and constrain it with losses derived from prior knowledge. This constraint encourages the branch to distill geometry information. Another branch learns the complementary style information. The two branches form an effective framework that can disentangle object's content-style representation without any human annotation. We evaluate our approach on four image datasets, on which we demonstrate the superior disentanglement and visual analogy quality both in synthesized and real-world data. We are able to generate photo-realistic images with 256*256 resolution that are clearly disentangled in content and style.
Joint demosaicing and denoising by overfitting of bursts of raw images

Demosaicking and denoising are the first steps of any camera image processing pipeline and are key for obtaining high quality RGB images. A promising current research trend aims at solving these two problems jointly using convolutional neural networks. Due to the unavailability of ground truth data these networks cannot be currently trained using real RAW images. Instead, they resort to simulated data. In this paper we present a method to learn demosacking directly from mosaicked images, without requiring ground truth RGB data. We apply this to learn joint demosaicking and denoising only from RAW images, thus enabling the use of real data. In addition we show that for this application overfitting a network to a specific burst improves the quality of restoration for both demosaicking and denoising.
What is the added value of negative links in online social networks?

We investigate the "negative link" feature of social networks that allows users to tag other users as foes or as distrusted in addition to the usual friend and trusted links. To answer the question whether negative links have an added value for an online social network, we investigate the machine learning problem of predicting the negative links of such a network using only the positive links as a basis, with the idea that if this problem can be solved with high accuracy, then the "negative link" feature is redundant. In doing so, we also present a general methodology for assessing the added value of any new link type in online social networks. Our evaluation is performed on two social networks that allow negative links: The technology news website Slashdot and the product review site Epinions. In experiments with these two datasets, we come to the conclusion that a combination of centrality-based and proximity-based link prediction functions can be used to predict the negative edges in the networks we analyse. We explain this result by an application of the models of preferential attachment and balance theory to our learning problem, and show that the "negative link" feature has a small but measurable added value for these social networks.
Fast Skill Learning for Variable Compliance Robotic Assembly

The robotic assembly represents a group of benchmark problems for reinforcement learning and variable compliance control that features sophisticated contact manipulation. One of the key challenges in applying reinforcement learning to physical robot is the sample complexity, the requirement of large amounts of experience for learning. We mitigate this sample complexity problem by incorporating an iteratively refitted model into the learning process through model-guided exploration. Yet, fitting a local model of the physical environment is of major difficulties. In this work, a Kalman filter is used to combine the adaptive linear dynamics with a coarse prior model from analytical description, and proves to give more accurate predictions than the existing method. Experimental results show that the proposed model fitting strategy can be incorporated into a model predictive controller to generate good exploration behaviors for learning acceleration, while preserving the benefits of model-free reinforcement learning for uncertain environments. In addition to the sample complexity, the inevitable robot overloaded during operation also tends to limit the learning efficiency. To address this problem, we present a method to restrict the largest possible potential energy in the compliance control system and therefore keep the contact force within the legitimate range.
A fluid model for many-server queues with time-varying arrivals and phase-type service distribution

We introduce and analyze a deterministic ﬂuid model that serves as an approximation for the G t /PH/s t + GI many-server queueing model, which has a general time-varying arrival process (the G t ), a phase-type (PH) service distribution (the PH ), a time-dependent number of servers (the s t ) and allows abandonment from queue according to a general abandonment distribution (the + GI ). We provide an eﬃcient algorithm, using matrix-analytic methods, to compute all standard transient (time-dependent) performance measures, such as the ﬂuid content in queue, potential waiting time, abandonment and service-completion rate
Exponential Synchronization of Impulsive Complex Networks with Nonidentical Coupling Delays and Switching Topology

The exponential synchronization problem is considered for impulsive complex networks with nonidentical coupling delays and switching topologies. The complex networks under investigation include two cases: one is that all sub networks are self-synchronizing, the other is that there are some sub networks are nonsychronizing. By means of the average dwell time technology of the switched systems, the delay-dependent sufficient conditions and switching signals are proposed for exponential synchronization of entire networks with respect to above two cases. Finally, two simulations are given to show the effectiveness of the developed results.
Quantum Probes for Ohmic Environments at Thermal Equilibrium

It is often the case that the environment of a quantum system may be described as a bath of oscillators with an ohmic density of states. In turn, the precise characterization of these classes of environments is a crucial tool to engineer decoherence or to tailor quantum information protocols. Recently, the use of quantum probes in characterizing ohmic environments at zero-temperature has been discussed, showing that a single qubit provides precise estimation of the cutoff frequency. On the other hand, thermal noise often spoil quantum probing schemes, and for this reason we here extend the analysis to a complex system at thermal equilibrium. In particular, we discuss the interplay between thermal fluctuations and time evolution in determining the precision attainable by quantum probes. Our results show that the presence of thermal fluctuations degrades the precision for low values of the cutoff frequency, i.e., values of the order ωc≲T (in natural units). For larger values of ωc, decoherence is mostly due to the structure of environment, rather than thermal fluctuations, such that quantum probing by a single qubit is still an effective estimation procedure.
Specialized MLP Classifiers to Support the Isolation of Patients Suspected of Pulmonary Tuberculosis

Tuberculosis is an infectious disease widely present in developing countries, which is largely motivated by the difficulty of a rapid and efficient diagnosis. In order to reduce the number of patients suspected of having TB unnecessarily isolated in hospitals, thus optimize the use of health resources, we propose a systematic procedure for developing a decision support system based on specialized MLP network committee. The system based on 3 MLP models, which response to input data clusters inferred by the k-means technique, exhibits a better classification performance than a single network in terms of the number of false positives, achieving a sensitivity of 83.3% and specificity of 94.3%.