Miracle-dz/UniFAR-FTUs huggingface dataset:
Health effects of climate change: an overview of systematic reviews

Objectives We aimed to develop a systematic synthesis of systematic reviews of health impacts of climate change, by synthesising studies’ characteristics, climate impacts, health outcomes and key findings. Design We conducted an overview of systematic reviews of health impacts of climate change. We registered our review in PROSPERO (CRD42019145972). No ethical approval was required since we used secondary data. Additional data are not available. Data sources On 22 June 2019, we searched Medline, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, Cochrane and Web of Science. Eligibility criteria We included systematic reviews that explored at least one health impact of climate change. Data extraction and synthesis We organised systematic reviews according to their key characteristics, including geographical regions, year of publication and authors’ affiliations. We mapped the climate effects and health outcomes being studied and synthesised major findings. We used a modified version of A MeaSurement Tool to Assess systematic Reviews-2 (AMSTAR-2) to assess the quality of studies. Results We included 94 systematic reviews. Most were published after 2015 and approximately one-fifth contained meta-analyses. Reviews synthesised evidence about five categories of climate impacts; the two most common were meteorological and extreme weather events. Reviews covered 10 health outcome categories; the 3 most common were (1) infectious diseases, (2) mortality and (3) respiratory, cardiovascular or neurological outcomes. Most reviews suggested a deleterious impact of climate change on multiple adverse health outcomes, although the majority also called for more research. Conclusions Most systematic reviews suggest that climate change is associated with worse human health. This study provides a comprehensive higher order summary of research on health impacts of climate change. Study limitations include possible missed relevant reviews, no meta-meta-analyses, and no assessment of overlap. Future research could explore the potential explanations between these associations to propose adaptation and mitigation strategies and could include broader sociopsychological health impacts of climate change.
MUSE Analysis of Gas around Galaxies (MAGG) – II: metal-enriched halo gas around z ∼ 1 galaxies

We present a study of the metal-enriched cool halo gas traced by Mg ii absorption around 228 galaxies at z ∼ 0.8–1.5 within 28 quasar fields from the MUSE Analysis of Gas around Galaxies survey. We observe no significant evolution in the Mg ii equivalent width versus impact parameter relation and in the Mg ii covering fraction compared to surveys at z ≲ 0.5. The stellar mass, along with distance from galaxy centre, appears to be the dominant factor influencing the Mg ii absorption around galaxies. With a sample that is 90 per cent complete down to a star formation rate of ≈0.1 $\rm M_\odot yr^{-1}$ and up to impact parameters ≈250–350 kpc from quasars, we find that the majority ($67^{+12}_{-15}$ per cent or 14/21) of the Mg ii absorption systems are associated with more than one galaxy. The complex distribution of metals in these richer environments adds substantial scatter to previously reported correlations. Multiple galaxy associations show on average five times stronger absorption and three times higher covering fraction within twice the virial radius than isolated galaxies. The dependence of Mg ii absorption on galaxy properties disfavours the scenario in which a widespread intragroup medium dominates the observed absorption. This leaves instead gravitational interactions among group members or hydrodynamic interactions of the galaxy haloes with the intragroup medium as favoured mechanisms to explain the observed enhancement in the Mg ii absorption strength and cross-section in rich environments.
SoRC - Evaluation of Computational Molecular Co-Localization Analysis in Mass Spectrometry Images

The computational analysis of Mass Spectrometry Imaging (MSI) data aims at the identification of interesting mass co-localizations and the visualization of their lateral distribution in the sample, usually a tissue cross section. But as the morphological structure of tissues and the different kinds of mass co-localization naturally show a huge diversity, the selection and tuning of the computational method is a time-consuming effort. In this work we address the special problem of computationally grouping mass channel images according to their similarities in their lateral distribution patterns. Such an analysis is driven by the idea, that groups of molecules that feature a similar distribution pattern may have a functional relation. But the selection of the similarity function and other parameters is often done by a time-consuming and unsatsifactory trial and error. We propose a new flexible workflow scheme called SoRC (sum of ranked cluster indices) for automating this tuning step and making it much more efficient. We test SoRC using three different data sets acquired from the lab for three different kinds of samples (barley seed, mouse bladder tissue, human PXE skin). We show, that SORC can be applied to score and visualize the results obtained with the applied methods in short time without too much effort. In our application example, the SoRC results for the three data sets reveal that a) some well-known similarity functions are suited to achieve good results for all three data sets and b) for the MSI data featuring a higher degree of irregularity improved results can be achieved by applying non-standard similarity functions. The SoRC scores computed with our approach indicate that an automated testing and scoring of different methods for mass channel image grouping can improve the final outcome of a study by finally selecting the methods of the highest scores.
Statistical Field Theory and Networks of Spiking Neurons

This paper models the dynamics of a large set of interacting neurons within the framework of statistical field theory. We use a method initially developed in the context of statistical field theory [44] and later adapted to complex systems in interaction [45][46]. Our model keeps track of individual interacting neurons dynamics but also preserves some of the features and goals of neural field dynamics, such as indexing a large number of neurons by a space variable. Thus, this paper bridges the scale of individual interacting neurons and the macro-scale modelling of neural field theory.
Efficient Kernel Transfer in Knowledge Distillation

Knowledge distillation is an effective way for model compression in deep learning. Given a large model (i.e., teacher model), it aims to improve the performance of a compact model (i.e., student model) by transferring the information from the teacher. An essential challenge in knowledge distillation is to identify the appropriate information to transfer. In early works, only the final output of the teacher model is used as the soft label to help the training of student models. Recently, the information from intermediate layers is also adopted for better distillation. In this work, we aim to optimize the process of knowledge distillation from the perspective of kernel matrix. The output of each layer in a neural network can be considered as a new feature space generated by applying a kernel function on original images. Hence, we propose to transfer the corresponding kernel matrix (i.e., Gram matrix) from teacher models to student models for distillation. However, the size of the whole kernel matrix is quadratic to the number of examples. To improve the efficiency, we decompose the original kernel matrix with Nystr{o}m method and then transfer the partial matrix obtained with landmark points, whose size is linear in the number of examples. More importantly, our theoretical analysis shows that the difference between the original kernel matrices of teacher and student can be well bounded by that of their corresponding partial matrices. Finally, a new strategy of generating appropriate landmark points is proposed for better distillation. The empirical study on benchmark data sets demonstrates the effectiveness of the proposed algorithm. Code will be released.
User-item matching for recommendation fairness: a view from item-providers

As we all know, users and item-providers are two main groups of participants in recommender systems. The main task of this paper is to significantly improve the coverage fairness (item-provider oriented objective), and simultaneously keep the recommendation accuracy in a high level (user oriented objective). First, an effective and totally robust approach of improving the coverage fairness is proposed, that is to constrain the allowed recommendation times of an item to be proportional to the frequency of its being purchased in the past. Second, in this constrained recommendation scenario, a serial of heuristic strategies of user-item matching priority are proposed to minimize the loss of recommendation accuracy. The parameterized strategy among them is validated to achieve better recommendation accuracy than the baseline algorithm in regular recommendation scenario, and it has an overwhelming superiority in coverage fairness over the regular algorithm. Third, to get the optimal solution of this user-item matching problem, we design a Minimum Cost Maximum Flow model, which achieves almost the same value of coverage fairness and even better accuracy performance than the parameterized heuristic strategy. Finally, we empirically demonstrate that, even compared with several state-of-the-art enhanced versions of the baseline algorithm, our framework of the constrained recommendation scenario coupled with the MCMF user-item matching priority strategy still has a several-to-one advantage in the coverage fairness, while its recommendation precision is more than 90% of the best value of all the enhanced algorithms. What is more, our proposed framework is parameter-free and thus achieves this superior performance without the time cost of parameter optimization, while all the above existing enhanced algorithms have to traverse their intrinsic parameter to get the best performance.
VMO spaces associated with Neumann Laplacian

In this paper, we establish several different characterizations of the vanishing mean oscillation space associated with Neumann Laplacian $\Delta_N$, written ${\rm VMO}_{\Delta_N}(\mathbb{R}^n)$. We first describe it with the classical ${\rm VMO}(\mathbb{R}^n)$ and certain ${\rm VMO}$ on the half-spaces. Then we demonstrate that ${\rm VMO}_{\Delta_N}(\mathbb{R}^n)$ is actually ${\rm BMO}_{\Delta_N}(\mathbb{R}^n)$-closure of the space of the smooth functions with compact supports. Beyond that, it can be characterized in terms of compact commutators of Riesz transforms and fractional integral operators associated to the Neumann Laplacian. Additionally, by means of the functional analysis, we obtain the duality between certain ${\rm VMO}$ and the corresponding Hardy spaces on the half-spaces. Finally, we present an useful approximation for ${\rm BMO}$ functions on the space of homogeneous type, which can be applied to our argument and otherwhere.
LEBANONUPRISING: a thorough study of Lebanese tweets

Recent studies showed a huge interest in social networks sentiment analysis. Twitter, which is a microblogging service, can be a great source of information on how the users feel about a certain topic, or what their opinion is regarding a social, economic and even political matter. On October 17, Lebanon witnessed the start of a revolution; the LebanonUprising hashtag became viral on Twitter. A dataset consisting of a 100,0000 tweets was collected between 18 and 21 October. In this paper, we conducted a sentiment analysis study for the tweets in spoken Lebanese Arabic related to the LebanonUprising hashtag using different machine learning algorithms. The dataset was manually annotated to measure the precision and recall metrics and to compare between the different algorithms. Furthermore, the work completed in this paper provides two more contributions. The first is related to building a Lebanese to Modern Standard Arabic mapping dictionary that was used for the preprocessing of the tweets and the second is an attempt to move from sentiment analysis to emotion detection using emojis, and the two emotions we tried to predict were the "sarcastic" and "funny" emotions. We built a training set from the tweets collected in October 2019 and then we used this set to predict sentiments and emotions of the tweets we collected between May and August 2020. The analysis we conducted shows the variation in sentiments, emotions and users between the two datasets. The results we obtained seem satisfactory especially considering that there was no previous or similar work done involving Lebanese Arabic tweets, to our knowledge.
EMR: A New Metric to Assess the Resilience of Directional mmWave Channels to Blockages

Millimeter-wave (mmWave) communication systems require narrow beams to increase communication range. If the dominant communication direction is blocked by an obstacle, an alternative and reliable spatial communication path should be quickly identified to maintain connectivity. In this paper, we introduce a new metric to quantify the effective multipath richness (EMR) of a directional communication channel by considering the strength and spatial diversity of the resolved paths, while also taking into account beamwidth and blockage characteristics. The metric is defined as a weighted sum of the number of multipath component (MPC) clusters, where clustering is performed based on the cosine-distance between the MPCs that have power above a certain threshold. This process returns a single scalar value for a transmitter (TX)/receiver (RX) location pair in a given environment. It is also possible to represent the EMR of the whole environment with a probability distribution function of the metric by considering a set of TX/RX locations. Using this proposed metric, one can assess the scattering richness of different communication environments to achieve a particular quality of service (QoS). This metric is especially informative and useful at higher frequencies, such as mmWave and terahertz (THz), where the propagation path loss and penetration loss are high, and directional non-light-of-sight (NLOS) communication is critical for the success of the network. We evaluate the proposed metric using our channel measurements at 28 GHz in a large indoor environment at a library setting for LOS and NLOS scenarios.
Regression to the tail: Why the Olympics blow up

The Olympic Games are the largest, highest-profile, and most expensive megaevent hosted by cities and nations. Average sports-related costs of hosting are $12.0 billion. Non-sports-related costs are typically several times that. Every Olympics since 1960 has run over budget, at an average of 172 percent in real terms, the highest overrun on record for any type of megaproject. The paper tests theoretical statistical distributions against empirical data for the costs of the Games, in order to explain the cost risks faced by host cities and nations. It is documented, for the first time, that cost and cost overrun for the Games follow a power-law distribution. Olympic costs are subject to infinite mean and variance, with dire consequences for predictability and planning. We name this phenomenon "regression to the tail": it is only a matter of time until a new extreme event occurs, with an overrun larger than the largest so far, and thus more disruptive and less plannable. The generative mechanism for the Olympic power law is identified as strong convexity prompted by six causal drivers: irreversibility, fixed deadlines, the Blank Check Syndrome, tight coupling, long planning horizons, and an Eternal Beginner Syndrome. The power law explains why the Games are so difficult to plan and manage successfully, and why cities and nations should think twice before hosting. Based on the power law, two heuristics are identified for better decision making on hosting. Finally, the paper develops measures for good practice in planning and managing the Games, including how to mitigate the extreme risks of the Olympic power law.
Discussions on Inverse Kinematics based on Levenberg-Marquardt Method and Model-Free Adaptive (Predictive) Control

In this brief, the current robust numerical solution to the inverse kinematics based on Levenberg-Marquardt (LM) method is reanalyzed through control theory instead of numerical method. Compared to current works, the robustness of computation and convergence performance of computational error are analyzed much more clearly by analyzing the control performance of the corrected model free adaptive control (MFAC). Then mainly motivated by minimizing the predictive tracking error, this study suggests a new method of model free adaptive predictive control (MFAPC) to solve the inverse kinematics problem. At last, we apply the MFAPC as a controller for the robotic kinematic control problem in simulation. It not only shows an excellent control performance but also efficiently acquires the solution to inverse kinematic.
Helping restricted Boltzmann machines with quantum-state representation by restoring symmetry

The variational wave functions based on neural networks have recently started to be recognized as a powerful ansatz to represent quantum many-body states accurately. In order to show the usefulness of the method among all available numerical methods, it is imperative to investigate the performance in challenging many-body problems for which the exact solutions are not available. Here, we construct a variational wave function with one of the simplest neural networks, the restricted Boltzmann machine (RBM), and apply it to a fundamental but unsolved quantum spin Hamiltonian, the two-dimensional J 1–J 2 Heisenberg model on the square lattice. We supplement the RBM wave function with quantum-number projections, which restores the symmetry of the wave function and makes it possible to calculate excited states. Then, we perform a systematic investigation of the performance of the RBM. We show that, with the help of the symmetry, the RBM wave function achieves state-of-the-art accuracy both in ground-state and excited-state calculations. The study shows a practical guideline on how we achieve accuracy in a controlled manner.
Apéry extensions

The Apéry numbers of Fano varieties are asymptotic invariants of their quantum differential equations. In this paper, we initiate a program to exhibit these invariants as (mirror to) limiting extension classes of higher cycles on the associated Landau–Ginzburg (LG) models — and thus, in particular, as periods. We also construct an Apéry motive, whose mixed Hodge structure is shown, as an application of the decomposition theorem, to contain the limiting extension classes in question. Using a new technical result on the inhomogeneous Picard–Fuchs equations satisfied by higher normal functions, we illustrate this proposal with detailed calculations for LG‐models mirror to several Fano threefolds. By describing the “elementary” Apéry numbers in terms of regulators of higher cycles (i.e., algebraic ‐theory/motivic cohomology classes), we obtain a satisfying explanation of their arithmetic properties. Indeed, in each case, the LG‐models are modular families of surfaces, and the distinction between multiples of and (or ) translates ultimately into one between algebraic and of the family.
Towards a Multi-modal, Multi-task Learning based Pre-training Framework for Document Representation Learning

In this paper, we propose a multi-task learning-based framework that utilizes a combination of self-supervised and supervised pre-training tasks to learn a generic document representation. We design the network architecture and the pre-training tasks to incorporate the multi-modal document information across text, layout, and image dimensions and allow the network to work with multi-page documents. We showcase the applicability of our pre-training framework on a variety of different real-world document tasks such as document classification, document information extraction, and document retrieval. We conduct exhaustive experiments to compare performance against different ablations of our framework and state-of-the-art baselines. We discuss the current limitations and next steps for our work.
Gas Bubble Disease in the African Clawed Frog, Xenopus laevis

Exposure of African clawed frogs (Xenopus laevis) to gas supersaturated water pro- duced gas bubbles in the interdigital webbing, progressing to inflammation, hemorrhages, and erosion of the skin. Due to extensive formation of gas bubbles in the webbing and under the skin, the frogs floated prior to death. Death resulted from accumulation of gas within the vascular system and secondary bacteremia. Reducing the dissolved gas levels of the water resulted in a reversal of clinical signs and complete recovery in many cases. Bubble formation in the webbing of Xenopus resulting from gas supersaturation has been reported at one other location. Gas supersaturation may be a significant primary cause of "red leg" disease of amphibians in both natural and laboratory systems.
Conjunction search revisited

Search for conjunctions of highly discriminable features can be rapid or even parallel. This article explores three possible accounts based on (a) perceptual segregation, (b) conjunction detectors, and (c) inhibition controlled separately by two or more distractor features. Search rates for conjunctions of color, size, orientation, and direction of motion correlated closely with an independent measure of perceptual segregation. However, they appeared unrelated to the physiology of single-unit responses. Each dimension contributed additively to conjunction search rates, suggesting that each was checked independently of the others. Unknown targets appear to be found only by serial search for each in turn. Searching through 4 sets of distractors was slower than searching through 2. The results suggest a modification of feature integration theory, in which attention is controlled not only by a unitary "window" but also by a form of feature-based inhibition. Language: en
Effectiveness of the Laser Application in Temporomandibular Joint Disorder: A Systematic Review of 1172 Patients

Objective The aim of this systematic review was to evaluate the effectiveness of laser application in temporomandibular joint disorder. Methods PubMed, SCOPUS, Science Direct, Web of Science, and Google Scholar electronic databases were searched systematically with restricting the languages to only English and year (January 2001 to March 2020), and studies were selected based on the inclusion criteria. Study quality and publication bias were assessed by using the Robvis, a software package of R statistical software. Results This systematic review included 32 studies (1172 patients) based on the inclusion and exclusion criteria. Most of the studies reported significant reduction of pain by the use of the laser during TMD treatment. Two-thirds of the study (78.13%) found a better outcome comparing with conventional one. According to Robvis, 84.4% of the studies were high methodological studies with low risk of bias. Conclusion TMD patients suffer with continuous pain for long time even after conventional treatment. Laser therapy shows a promising outcome of pain reduction for TMD patients. Therefore, laser therapy can be recommended for the TMD patients' better outcome. This trial is registered with PROSPERO (CRD42020177562).