Miracle-dz/UniFAR-FTUs huggingface dataset:
High-dimensional gaussian filtering for computational photography

Over the last decade, digital imaging has become ubiquitous. The advent of cheap digital cameras, and the inclusion of cameras in almost all mobile devices, has made photography one of the basic ways in which people record and communicate experiences. The ubiquity of cameras has imposed new constraints on their physical form. Camera modules are expected to be thin, light, and cheap. These restrictions make the production of high-quality images challenging. We turn to increasingly sophisticated algorithmic tools to transform the raw data captured by a camera into a photograph. This dissertation focuses on one such family of algorithmic tools: those expressible as a Gauss transform. One popular technique in this family is the bilateral filter, which smooths the fine detail in an image without crossing strong edges. It can be used to isolate and control the sharpness, tone, and contrast of a photograph at various scales. Its relatives, the joint-bilateral filter and the joint-bilateral upsample, allow for the fusion of data from multiple images. Another popular technique in the same family is non-local means, which denoises an image by replacing each pixel with the average color of all other pixels in the image with a similar local neighborhood. A naive implementation of these algorithms is prohibitively slow. This dissertation unifies these algorithms under a common framework, describes a variety of applications of the transform in photographic image processing, and presents two new data structures to accelerate the computation of such transforms: the permutohedral lattice, and the Gaussian kd-tree. The permutohedral lattice implements a Gauss transform as a resampling using the lattice A∗, which tessellates space with uniform simplices. For n d-dimensional
Vicomtech at LivingNER2022

This paper describes the participation of the Vicomtech NLP team in the LivingNER 2022 shared task about detecting and normalising mentions of living beings in clinical texts written in Spanish. We participate in each of the 3 LivingNER tasks, combining multiple approaches and strategies. For task 1 (NER) we use a Transformer-based model to perform sequence labelling. For task 2 (Normalisation) we use Semantic Text Search approaches to relate entity mentions to their taxonomy codes. For task 3 we try two different strategies: a trained multi-label classifier and a zero-shot semantic similarity approach. The results for task 1 and task 2 are high, both in our experiments and in the official evaluation results. For task 1 our system obtains an overall of 95.1% of F1-score. For task 2 our system achieves 93.04% F1-score. Task 3 was the most challenging and the one with the least available training data; the scores obtained by all the participating systems have been extremely low. According to the official results, our systems score more than 10 points above the average of the other participating systems for task 1 and 2.
New advances in molecular and neural mechanisms of sleep regulation

1 National Institute of Biological Sciences (NIBS), Beijing 102206, China 2 Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, Beijing 102206, China Sleep exists ubiquitously among invertebrate and vertebrate animals. While sleep survives millions of years of evolution, it is unclear exactly what essential functions that sleep provides to animals. Sleep and wakefulness are mainly regulated by circadian and homeostatic mechanisms [1, 2]. However, the molecular and neural mechanisms of sleep regulation, especially in mammals, remain unclear. This special issue consisted of four review articles cover some of the new exciting advances in sleep research. In recent years, there has been a rapid expansion of our knowledge on the neural pathways that control wakefulness, rapid eye movement (REM) sleep and non-REM (NREMS) sleep [3–5]. The paraventricular nucleus of the hypothalamus (PVH) is an important hub for regulating neuroendocrine and autonomic functions, complex behaviors and negative emotions after stress [6]. Jiang et al. provide a focused and thorough review of emerging anatomical and neuroscience research supporting a critical role of PVH in the promotion and maintenance of wakefulness [7]. The core molecular pathways and biochemical mechanisms that govern mammalian sleep regulation remain to be elucidated. Funato and Yanagisawa wrote a nice review of forward and reverse mouse genetics studies in search for mammalian sleep regulatory genes [8]. Notably, their previous studies of orexin knockout mice uncover the mechanistic link between orexin deficiency and human sleep disorder–narcolepsy [9]. A tour-de-force forward genetic screen of randomly mutagenized mice has identified Sik3 and Nalcn as key regulators of NREM and REM sleep, respectively [10]. Moreover, the advent of new genome-editing tools, such as CRISPR/Cas9, has greatly expedited identification of new sleep regulatory genes by reverse mouse genetics [11–13]. Although most human adults sleep on average 8 hours per day, there are natural short sleepers (NSS) who need only 4 to 6.5-h sleep per day without any obvious negative health consequences. Zheng and Zhang reviewed human genetic studies that identified the causative mutations in a series of NSS families in the last decade [14]. Understanding the genetic basis of NSS provides an opportunity to study not only the genetic mechanism of human sleep, but also relationship between sleep and physiological function [14]. Apart from genetic factors, environmental factors also exert major influences on sleep quantity and quality. Public health measures to prevent the spread of COVID-19, have caused
Using influence to understand complex systems

This thesis is concerned with understanding the behavior of complex systems, particularly in the common case where instrumentation data is noisy or incomplete. We begin with an empirical study of logs from production systems, which characterizes the content of those logs and the challenges associated with analyzing them automatically, and present an algorithm for identifying surprising messages in such logs. The principal contribution is a method, called influence, that identifies relationships among components—even when the underlying mechanism of interaction is unknown—by looking for correlated surprise. Two components are said to share an influence if they tend to exhibit surprising behavior that is correlated in time. We represent the behavior of components as surprise (deviation from typical or expected behavior) over time and use signal-processing techniques to find correlations. The method makes few assumptions about the underlying systems or the data they generate, so it is applicable to a variety of unmodified production systems, including supercomputers, clusters, and autonomous vehicles. We then extend the idea of influence by presenting a query language and online implementation, which allow the method to scale to systems with hundreds of thousands of signals. In collaboration with system administrators, we applied these tools to real systems and discovered correlated problems, failure cascades, skewed clocks, and performance bugs. According to the administrators, it also generated information useful for diagnosing and fixing these issues.
A Global Solution to a Two-dimensional Riemann Problem Involving Shocks as Free Boundaries

We present a global solution to a Riemann problem for the pressure gradient system of equations.The Riemann problem has initially two shock waves and two contact discontinuities. The angle between the two shock waves is set initially to be close to 180 degrees. The solution has a shock wave that is usually regarded as a free boundary in the self-similar variable plane. Our main contribution in methodology is handling the tangential oblique derivative boundary values.
URJC-Team at PoliticEs 2022: Political Ideology Prediction using Linear Classifiers

Different works have demonstrated the relationship between personality traits and political ideology and how they influence our daily lives. The challenge proposed in the IberLEF 2022 Task, PoliticEs, consists of extracting political ideology traits from the text by utilising Natural Language Processing (NLP) techiques. This paper describes the participation of the URJC-Team in such task. In particular, the achievement of extracting political ideology features walks through identifying the gender, the profession, and the political spectrum from a binary and multi-class perspective. In this work, we proposed two Machine Learning models to address the binary and multiclass classification problems, a Linear Support Vector Machine and Logistic Regression. The utilized dataset comprises hundreds of tweets that are cleaned and processed to generate various representations that serve as an input for the system. Between the different proposed subtasks, the proposed classification method has obtained competitive results for the binary ideology classification task, reaching 0.81. The proposal has great room for improvement, and we have planned the following steps for it.
A moment problem in ₁ approximation

Let n be a fixed, but arbitrary, integer and consider the sign function s(A, x)=s(A) defined on [0, I] by + 1 x E (ad, aj+1) i even, (1) s(A, x) = 0 x= a 1 x E (ai, ai+i) i odd where A stands for the vector (a,, a2, * * *, an) and the convention is made that aiai+? , ao=O, an+=1. Thus s(A) is simply a step function taking on the values + 1 with at most n sign changes. Let u be a finite, nonatomic measure on [0, 1] such that every s(A) is measurable and let {f iI i = 1, 2, . , n } be n functions integrable on [0, 1]. We may now state the
Perceptual and Numerical Aspects of Spring Models

The helical spring is a structure which has seen little investigation in the context of musical acoustics; though it does not play a role in musical instrument acoustics, it is the primary vibrating structure in electromechanical spring reverberation devices. The response of a spring is, however, even under linear conditions, far more complex than that of a straight rod, and consists of multiple echoes at distinct velocities, of a highly dispersive character, subject to multiple cutoff frequencies. In the interest of developing digital emulations, it is thus useful to obtain a complete picture of the perceptual importance of the various features in a reverberation context, through an investigation of dispersion curves and associated group velocities. Additional difficulties inherent in numerical simulation methods, also with an eye towrds perceptual considerations, will also be discussed.
A simple method to control automatically the performance of three‐coil wireless power transfer systems without direct output measurement

The control and optimization of wireless power transfer (WPT) systems require the continuous monitoring of their performance or, similarly, some output parameters. The solution presented in this manuscript is based on the relationship between some figures of merit representative of the WPT systems and allows to control automatically (in a simple way, without any direct measurement at the load circuit) the performance of a three‐coil WPT system under variations on the load and/or mutual inductance between the intermediate and load coils. The qualities and limitations of the proposed method are discussed in detail. Conventional circuit analysis and practical results are included to validate the presented theory and its applicability in an automatic control scheme.
Semantics and Pragmatics of False Friends

Acknowledgments. Introduction 1. Clearing the Terrain 2. Synonymy, Polysemy and Homonymy 3. Semantics of False Friends: Borrowings, Calques and Inheritances 4. Semantics of False Friends: Tropical False Friends 5. Pragmatics of False Friends 6. Main theses Exposed and Conclusions. Notes. Bibliography. Index
Hurting all the way: The emotional antecedent and consequence of social rejection

Social rejection is cold and hurtful, but how and why it is formed remains under-investigated. Our study offers one possible explanation from the rejector’s perspective by developing a moderated mediation model on the emotional antecedent and consequence of social rejection. Specifically, envious individuals use social rejection to complement their inferiority, further triggering their negative affect. Drawing on social comparison theory, we conducted an experience sampling methodology (ESM) investigation of 55 frontline workers through a 10-workday-survey (Level 1 n = 515). As predicted, daily envy is positively associated with daily social rejection. Daily social rejection is positively related to daily negative affect. Furthermore, daily social rejection mediates the relationship between daily envy and daily negative affect. These effects are more robust for females than males, including the impact of envy on social rejection and the impact of envy on negative affect via social rejection. We suggest the recipient and the rejector make psychological and behavioral adjustments accordingly. We also recommend that future research extend our current study methodologically and theoretically.
Task-Aware Contrastive Pre-training for Spanish Named Entity Recognition in LivingNER Challenge

This paper is presented as part of the LivingNER-Species NER track (Species mention entity recognition). The goal of the task is to identify the character offsets of all species mentions (human or non-human) in a collection of plain text clinical case reports. In this paper, we explore the idea of incorporating contrastive pre-training followed by fine-tuning for this task. Contrastive pre-training is performed such that it increases the divergence between non-related entities and decrease the divergence between related entities. The model first trains the sentence in the text, and calculate the similarity between token categories based on their Gaussian-distributed embeddings in the sentences. Further, this contrastively pre-trained BERT model is combined with the classification head to perform the named entity recognition task. We demonstrate that fine-tuning a model pre-trained using the contrastive loss performs better than directly fine-tuning on the clean, annotated data. The models have been evaluated on the IberLEF 2022 challenge using the official release of the LivingNER corpus [1]. Experimental results shows that our model outperforms baseline methods on the task.
Tensor of the energy - momentum and forbiddance of the classical Lump

We show that the static solutions of massive classical fields are forbidden in the Minkowski space time with dimensions higher than $2$. The purpose of this study is to generalize the forbiddance of the time independent localized classical fields in general space time. We generalize the Derrick theorem as unstable stationary localized solutions for the nonlinear wave equation or the nonlinear Klein - Gordon equation in Minkowski space time. Our approach employs the tensor of the energy - momentum based on Weber's method. We exploite the Bateman - Caldilora - Kanai model and the Deser method for the research of the Yang - Mills Lumps as massive solutions. We also use Riemann's coordinates for parametrization of tangent space as a local framework. The important point of this article is the homomorphism between the Minkowsian and pseudo Riemann's manifolds.
Benchmarking Multi-Modal Entailment for Fact Verification (short paper)

Fake news can spread quickly on social media and it is important to detect it before it creates lot of damage. Automatic fact/claim verification has recently become a topic of interest among diverse research communities. We present the findings of the Factify shared task, which aims undertake multi-modal fact verification, organized as a part of the De-Factify workshop at AAAI’22. The task is modeled as a multi-modal entailment task, where each input needs to be classified into one of 5 classes based on entailment and modality. A total of 64 teams participated in the Factify shared task, and of them, 9 teams submitted their predictions on test set. The most successful models were BigBird or other variations of BERT. The highest F1 score averaged across all the classes was 76.82%.
FLERT-Matcher: A Two-Step Approach for Clinical Named Entity Recognition and Normalization

In recent years, the appearance of pre-trained language models has boosted the performance of several Natural Language Processing (NLP) models, achieving state of the art in many NLP tasks. Previous work in Named Entity Recognition (NER) has shown that using sentence-level context is not always enough to obtain high-quality contextualized representations while using document-level context contributes to significant improvements in the task. In this paper, we compared the performance of several domain-specific and general-domain language models to identify species mentions on the LivingNER shared task. Specifically, we fine-tuned these models using document-level context with the FLERT approach, which consists of creating the representation based on the context of the actual sentence and its neighboring sentences. Then, to obtain the codes of each entity mention, we used the output of the FLERT model and a Levenshtein distance-based approach. Finally, we trained NER models for real clinical use cases using a similar two-step system and combined these results to perform document-level classification and coding. Our submission results show that our models’ performance is far superior to the average of other systems proposed, thus being an important contribution to species recognition and normalization. To reproduce our experiments, the source code of the system is freely available at https://github.com/ plncmm/flert-matcher.
Leveraging Events Sub-Categories for Violent-Events Detection in Social Media

This paper describes our participation in the shared evaluation campaign of DA-VINCIS@IberLEF 2022. In this work, we addressed the Violent Event Identification (VEI) task by exploiting Bidirectional Encoder Representations from Transformers (BERT) in combination with Multi-Task learning approaches. Our results indicate that the proposed architecture is able to leverage information about the crime categories for effectively detect the mention of a violent act within a tweet. Our approach obtained the best performance (F1 = 0.7758) among 11 different teams and a total of 32 different submissions.
STRUCTURE, EXPRESSION, AND GENETIC LINKAGE OF THE MOUSE BCMI (0X45 OR Blast-1) ANTIGEN

An antigen named MRC OX45 in the rat, or Blast-1 in the human, is of particular interest because it forms a subset within the Ig superfamily (IgSF) t along with the CD2 and LFA3 antigens that are known to form a recognition pair (1, 2) . Since the loci for OX45/Blast-1 were recently designated Bcm-1 in mouse and BCM1 in human by the genetic nomenclature committee, for simplicity, we will call the antigen BCM1 in this report . CD2 and LFA3 are involved in adhesion reactions between T cells and accessory cells (3), and CD2 can also act as a target for mAbinduced mitogenesis on human (4) and rat (5) T lymphocytes. In the rat, the BCMI antigen is found on most leukocytes and endothelial cells, with high levels of expression on macrophages and activated T lymphocytes (6, 7) . The tissue distribution ofBCM1 resembles LFA3 (8) more than CD2 antigen, which is restricted to human T lymphocytes (4), rat T cells plus some macrophages (5), and mouse T plus B cells (9) . Both rat and human BCM1 antigens are attached to the cell surface via a glycosyl phosphatidylinositol (GPI) anchor, a property shared with LFA3 antigen, which is found in GPIor protein-anchored forms (1, 2, 10, 11) . While the mouse GPI-anchored molecules Thy-1 and Ly-6 can function as targets for mAb-induced mitogenesis of T lymphocytes in the presence of phorbol esters (12, 13), mAbs against rat BCMI have not been found to be mitogenic but, instead, inhibit T lymphocyte responses in cultures that contain macrophages (7) . The basis of this effect is obscure, but it appears to occur via potentiation ofsuppression by macrophages or other bystander cells .
FACTIFY: A Multi-Modal Fact Verification Dataset

Combating fake news is one of the burning societal crisis. It is difficult to expose false claims before they create a lot of damage. Automatic fact/claim verification has recently become a topic of interest among diverse research communities. Forums like FEVER, FNC [1, 2] aim to discuss automatic fact-checking on text. Research efforts and datasets on text fact verification could be found, but there is not much attention towards multi-modal or cross-modal fact-verification. In order to bring the attention of the research community towards understanding multimodal misinformation, we release a multimodal fact checking dataset named FACTIFY. It is notably the largest multimodal fact verification public dataset consisting of 50K data points, covering news from India and the US. FACTIFY contains images, textual claims, reference textual documents and images labeled with three broad categories namely - support, no-evidence, and refute .
A Transfer Learning Model for Polarity in Touristic Reviews in Spanish from TripAdvisor

REST-MEX 2022 (Sentiment Analysis Track) is one of the IberLEF 2022 tasks, dedicated to provide good polarity classification for reviews of tourists that traveled around Mexico and leave a review for specific places in TripAdvisor from 2002 to 2021. Polarity analysis of reviews can help to develop intelligent systems for mexican tourism to improve the quality of service and the supply of new touristic places for foreign and domestic tourism. For this task, three different approaches were considered but given the results a traditional transfer learning by using a BERT model provided the best results.
UMUTeam at EXIST 2022: Knowledge Integration and Ensemble Learning for Multilingual Sexism Identification and Categorization using Linguistic Features and Transformers

This paper presents the contribution of the UMUTeam to the second edition of the EXIST 2022 shared task at IberLEF 2022. This task deals with the identification and categorization of sexism language in English and Spanish. Specifically, two tasks were proposed. Task 1 consisting of a binary classification of sexism and Task 2 being a multi-classification task for the categorization of sexism traits. Our proposal for these tasks is based on the use of linguistic features and transformers combined using knowledge integration and ensemble learning strategies. Our team ranked 7th in Task 1 and 3rd in Task 2, achieving an accuracy of 76.47% and 67.67%, respectively.