Miracle-dz/UniFAR-FTUs huggingface dataset:
A Note on An Abstract Model for Branching and its Application to Mixed Integer Programming

A key ingredient in branch and bound (B&B) solvers for mixed-integer programming (MIP) is the selection of branching variables since poor or arbitrary selection can affect the size of the resulting search trees by orders of magnitude. A recent article by Le Bodic and Nemhauser [Mathematical Programming, (2017)] investigated variable selection rules by developing a theoretical model of B&B trees from which they developed some new, effective scoring functions for MIP solvers. In their work, Le Bodic and Nemhauser left several open theoretical problems, solutions to which could guide the future design of variable selection rules. In this article, we first solve many of these open theoretical problems. We then implement an improved version of the model-based branching rules in SCIP 6.0, a modern open-source MIP solver, in which we observe an 11% geometric average time and node reduction on instances of the MIPLIB 2017 Benchmark Set that require large B&B trees.
Adapted and Oversegmenting Graphs: Application to Geometric Deep Learning

We propose a novel iterative method to adapt a a graph to d-dimensional image data. The method drives the nodes of the graph towards image features. The adaptation process naturally lends itself to a measure of feature saliency which can then be used to retain meaningful nodes and edges in the graph. From the adapted graph, we also propose the computation of a dual graph, which inherits the saliency measure from the adapted graph, and whose edges run along image features, hence producing an oversegmenting graph. The proposed method is computationally efficient and fully parallelisable. We propose two distance measures to find image saliency along graph edges, and evaluate the performance on synthetic images and on natural images from publicly available databases. In both cases, the most salient nodes of the graph achieve average boundary recall over 90%. We also apply our method to image classification on the MNIST hand-written digit dataset, using a recently proposed Deep Geometric Learning architecture, and achieving state-of-the-art classification accuracy, for a graph-based method, of 97.86%.
Towards a Unified Approach to Electromagnetic Analysis by Multilayer Embedded Objects

In this paper, an efficient and accurate unified approach is proposed to solve transverse magnetic scattering problems by multilayer embedded objects. In the proposed approach, an equivalent current density is derived when the equivalent theorem is recursively applied on each boundary from inner to exterior regions. Then, the objects are replaced by the background medium and the equivalent electric current density enforced on the outermost boundary remains fields in the exterior region unchanged. Therefore, the scattering problems by the multilayer embedded objects can be solved with the electric field integral equation (EFIE). Compared with other two-region formulations (TRFs), like the PMCHWT, CTF, the proposed approach shows significant benefits: only the surface electric current density instead of both the electric and magnetic current densities is required to model the complex objects. Furthermore, the single electric current density is only enforced on the outermost boundary of objects. Therefore, the overall count of unknowns can be significantly reduced, especially when the number of boundaries between different homogeneous media is large. At last, several numerical experiments are performed to validate the accuracy and efficiency of the proposed approach.
Correct, Fast Remote Persistence

Persistence of updates to remote byte-addressable persistent memory (PM), using RDMA operations (RDMA updates), is a poorly understood subject. Visibility of RDMA updates on the remote server is not the same as persistence of those updates. The remote server's configuration has significant implications on what it means for RDMA updates to be persistent on the remote server. This leads to significant implications on methods needed to correctly persist those updates. This paper presents a comprehensive taxonomy of system configurations and the corresponding methods to ensure correct remote persistence of RDMA updates. We show that the methods for correct, fast remote persistence vary dramatically, with corresponding performance trade offs, between different remote server configurations.
Automated Artery Localization and Vessel Wall Segmentation of Magnetic Resonance Vessel Wall Images using Tracklet Refinement and Polar Conversion.

Quantitative analysis of vessel wall structures by automated vessel wall segmentation provides useful imaging biomarkers in evaluating atherosclerotic lesions and plaque progression time-efficiently. To quantify vessel wall features, drawing lumen and outer wall contours of the artery of interest is required. To alleviate manual labor in contour drawing, some computer-assisted tools exist, but manual preprocessing steps, such as region of interest identification and boundary initialization are needed. In addition, the prior knowledge of the ring shape of vessel wall is not taken into consideration in designing the segmentation method. In this work, trained on manual vessel wall contours, a fully automated artery localization and vessel wall segmentation system is proposed. A tracklet refinement algorithm is used to robustly identify the centerlines of arteries of interest from a neural network localization architecture. Image patches are extracted from the centerlines and segmented in a polar coordinate system to use 3D information and to overcome problems such as contour discontinuity and interference from neighboring vessels. From a carotid artery dataset with 116 subjects (3406 slices) and a popliteal artery dataset with 5 subjects (289 slices), the proposed system is shown to robustly identify the artery of interest and segment the vessel wall. The proposed system demonstrates better performance on the carotid dataset with a Dice similarity coefficient of 0.824, compared with traditional vessel wall segmentation methods, Dice of 0.576, and traditional convolutional neural network approaches, Dice of 0.747. This vessel wall segmentation system will facilitate research on atherosclerosis and assist radiologists in image review.
Random Linear Network Coding on Programmable Switches

By extending the traditional store-and-forward mechanism, network coding has the capability to improve a network's throughput, robustness, and security. Given the fundamentally different packet processing required by this new paradigm and the inflexibility of hardware, existing solutions are based on software. As a result, they have limited performance and scalability, creating a barrier to its wide-spread adoption. By leveraging the recent advances in programmable networking hardware, in this paper we propose a random linear network coding data plane written in P4, as a first step towards a production-level platform. Our solution includes the ability to combine the payload of multiple packets and of executing the required Galois field operations, and shows promise to be practical even under the strict memory and processing constraints of switching hardware.
An algebraic inverse theorem for the quadratic Littlewood-Offord problem, and an application to Ramsey graphs

Consider a quadratic polynomial $f\left(\xi_{1},\dots,\xi_{n}\right)$ of independent Bernoulli random variables. What can be said about the concentration of $f$ on any single value? This generalises the classical Littlewood--Offord problem, which asks the same question for linear polynomials. As in the linear case, it is known that the point probabilities of $f$ can be as large as about $1/\sqrt{n}$, but still poorly understood is the "inverse" question of characterising the algebraic and arithmetic features $f$ must have if it has point probabilities comparable to this bound. In this paper we prove some results of an algebraic flavour, showing that if $f$ has point probabilities much larger than $1/n$ then it must be close to a quadratic form with low rank. We also give an application to Ramsey graphs, asymptotically answering a question of Kwan, Sudakov and Tran.
How much research shared on Facebook is hidden from public view? A comparison of public and private online activity around PLOS ONE papers

Despite its undisputed position as the biggest social media platform, Facebook has never entered the main stage of altmetrics research. In this study, we argue that the lack of attention by altmetrics researchers is not due to a lack of relevant activity on the platform, but because of the challenges in collecting Facebook data have been limited to activity that takes place in a select group of public pages and groups. We present a new method of collecting shares, reactions, and comments across the platform-including private timelines-and use it to gather data for all articles published between 2015 to 2017 in the journal PLOS ONE. We compare the gathered data with altmetrics collected and aggregated by Altmetric. The results show that 58.7% of papers shared on the platform happen outside of public view and that, when collecting all shares, the volume of activity approximates patterns of engagement previously only observed for Twitter. Both results suggest that the role and impact of Facebook as a medium for science and scholarly communication has been underestimated. Furthermore, they emphasise the importance of openness and transparency around the collection and aggregation of altmetrics.
Depth Map Estimation

The paper presents a new method of depth estimation dedicated for free-viewpoint television (FTV). The estimation is performed for segments and thus their size can be used to control a trade-off between the quality of depth maps and the processing time of their estimation. The proposed algorithm can take as its input multiple arbitrarily positioned views which are simultaneously used to produce multiple inter-view consistent output depth maps. The presented depth estimation method uses novel parallelization and temporal consistency enhancement methods that significantly reduce the processing time of depth estimation. An experimental assessment of the proposals has been performed, based on the analysis of virtual view quality in FTV. The results show that the proposed method provides an improvement of the depth map quality over the state-of-the-art method, simultaneously reducing the complexity of depth estimation. The consistency of depth maps, which is crucial for the quality of the synthesized video and thus the quality of experience of navigating through a 3D scene, is also vastly improved.
Homogeneous network embedding for massive graphs via reweighted personalized PageRank

Given an input graph G and a node v ∈ G, homogeneous network embedding (HNE) maps the graph structure in the vicinity of v to a compact, fixed-dimensional feature vector. This paper focuses on HNE for massive graphs, e.g., with billions of edges. On this scale, most existing approaches fail, as they incur either prohibitively high costs, or severely compromised result utility. Our proposed solution, called Node-Reweighted PageRank (NRP), is based on a classic idea of deriving embedding vectors from pairwise personalized PageRank (PPR) values. Our contributions are twofold: first, we design a simple and efficient baseline HNE method based on PPR that is capable of handling billion-edge graphs on commodity hardware; second and more importantly, we identify an inherent drawback of vanilla PPR, and address it in our main proposal NRP. Specifically, PPR was designed for a very different purpose, i.e., ranking nodes in G based on their relative importance from a source node's perspective. In contrast, HNE aims to build node embeddings considering the whole graph. Consequently, node embeddings derived directly from PPR are of suboptimal utility. The proposed NRP approach overcomes the above deficiency through an effective and efficient node reweighting algorithm, which augments PPR values with node degree information, and iteratively adjusts embedding vectors accordingly. Overall, NRP takes O(mlogn) time and O(m) space to compute all node embeddings for a graph with m edges and n nodes. Our extensive experiments that compare NRP against 18 existing solutions over 7 real graphs demonstrate that NRP achieves higher result utility than all the solutions for link prediction, graph reconstruction and node classification, while being up to orders of magnitude faster. In particular, on a billion-edge Twitter graph, NRP terminates within 4 hours, using a single CPU core.
El Addenbrooke's Cognitive Examination en español para el diagnóstico de demencia y para la diferenciación entre enfermedad de Alzheimer y demencia frontotemporal

Introduccion. Existe una creciente necesidad de contar con un instrumento breve y fiable que permita detectar demencia en los estadios iniciales. Objetivo. Validar la traduccion al espanol del Addenbrooke? s Cognitive Examination (ACE), una bateria breve recientemente desarrollada y validada en ingles para detectar demencia y diferenciar la demencia tipo Alzheimer (DTA) de la demencia frontotemporal (DFT). Pacientes y metodos. Se estudiaron los siguientes grupos: controles (n = 52), DFT segun criterios del consenso de Lund y Manchester (n = 22) y pacientes con probable DTA (n = 54) segun criterios del NINCDS-ADRDA. A todos los pacientes se les realizo una bateria neuropsicologica estandar, inventarios neuropsiquiatricos y neuroimagenes. La diferencia entre grupos se evaluo con ANOVA y la consistencia interna del cuestionario mediante el coeficiente a de Cronbach. La eleccion del punto de corte se realizo mediante la curva ROC (del ingles receiver operating characteristics). Resultados. El test mostro una aceptable coherencia interna. El cut-off de 86 arrojo una sensibilidad del 92% (IC 95%: 83,6-97,0) y especificidad del 96,2% (IC 95%: 86,8-99,4). La curva ROC demostro mayor sensibilidad y especificidad del ACE sobre el Mini-Mental State Examination para discriminar entre grupo control y demencia. El coeficiente VLOM (fluidez verbal + lenguaje) / (orientacion + recuerdo diferido) permite orientar el diagnostico hacia DFT si el valor es menor de 1,82 y hacia DTA si es mayor de 4,87. Conclusion. La version en espanol del ACE posee buena especificidad y sensibilidad para la deteccion de demencia en poblacion de alto nivel educativo y permite orientar el diagnostico diferencial entre DFT y DTA. Se necesitan nuevos estudios en poblacion de menor escolaridad.
A Unified Neural Coherence Model

Recently, neural approaches to coherence modeling have achieved state-of-the-art results in several evaluation tasks. However, we show that most of these models often fail on harder tasks with more realistic application scenarios. In particular, the existing models underperform on tasks that require the model to be sensitive to local contexts such as candidate ranking in conversational dialogue and in machine translation. In this paper, we propose a unified coherence model that incorporates sentence grammar, inter-sentence coherence relations, and global coherence patterns into a common neural framework. With extensive experiments on local and global discrimination tasks, we demonstrate that our proposed model outperforms existing models by a good margin, and establish a new state-of-the-art.
MRI Reconstruction Using Deep Bayesian Inference

Purpose: To develop a deep learning-based Bayesian inference for MRI reconstruction. Methods: We modeled the MRI reconstruction problem with Bayes's theorem, following the recently proposed PixelCNN++ method. The image reconstruction from incomplete k-space measurement was obtained by maximizing the posterior possibility. A generative network was utilized as the image prior, which was computationally tractable, and the k-space data fidelity was enforced by using an equality constraint. The stochastic backpropagation was utilized to calculate the descent gradient in the process of maximum a posterior, and a projected subgradient method was used to impose the equality constraint. In contrast to the other deep learning reconstruction methods, the proposed one used the likelihood of prior as the training loss and the objective function in reconstruction to improve the image quality. Results: The proposed method showed an improved performance in preserving image details and reducing aliasing artifacts, compared with GRAPPA, $\ell_1$-ESPRiT, and MODL, a state-of-the-art deep learning reconstruction method. The proposed method generally achieved more than 5 dB peak signal-to-noise ratio improvement for compressed sensing and parallel imaging reconstructions compared with the other methods. Conclusion: The Bayesian inference significantly improved the reconstruction performance, compared with the conventional $\ell_1$-sparsity prior in compressed sensing reconstruction tasks. More importantly, the proposed reconstruction framework can be generalized for most MRI reconstruction scenarios.
Knowledge Distillation for End-to-End Person Search

We introduce knowledge distillation for end-to-end person search. End-to-End methods are the current state-of-the-art for person search that solve both detection and re-identification jointly. These approaches for joint optimization show their largest drop in performance due to a sub-optimal detector. We propose two distinct approaches for extra supervision of end-to-end person search methods in a teacher-student setting. The first is adopted from state-of-the-art knowledge distillation in object detection. We employ this to supervise the detector of our person search model at various levels using a specialized detector. The second approach is new, simple and yet considerably more effective. This distills knowledge from a teacher re-identification technique via a pre-computed look-up table of ID features. It relaxes the learning of identification features and allows the student to focus on the detection task. This procedure not only helps fixing the sub-optimal detector training in the joint optimization and simultaneously improving the person search, but also closes the performance gap between the teacher and the student for model compression in this case. Overall, we demonstrate significant improvements for two recent state-of-the-art methods using our proposed knowledge distillation approach on two benchmark datasets. Moreover, on the model compression task our approach brings the performance of smaller models on par with the larger models.
Social Influence-based Attentive Mavens Mining and Aggregative Representation Learning for Group Recommendation

Frequent group activities of human beings have become an indispensable part in their daily life. Group recommendation can recommend satisfactory activities to group members in the recommender systems, and the key issue is how to aggregate preferences in different group members. Most existing group recommendation employed the predefined static aggregation strategies to aggregate the preferences of different group members, but these static strategies cannot simulate the dynamic group decision-making. Meanwhile, most of these methods depend on intuitions or assumptions to analyze the influence of group members and lack of convincing theoretical support. We argue that the influence of group members plays a particularly important role in group decision-making and it can better assist group profile modeling and perform more accurate group recommendation. To tackle the issue of preference aggregation for group recommendation, we propose a novel attentive aggregation representation learning method based on sociological theory for group recommendation, namely SIAGR (short for "Social Influence-based Attentive Group Recommendation"), which takes attention mechanisms and the popular method (BERT) as the aggregation representation for group profile modeling. Specifically, we analyze the influence of group members based on social identity theory and two-step flow theory and exploit an attentive mavens mining method. In addition, we develop a BERT-based representation method to learn the interaction of group members. Lastly, we complete the group recommendation under the neural collaborative filtering framework and verify the effectiveness of the proposed method by experimenting.
The Influence of Working in Stone Crushing Factories on Specific Respiratory Parameters

Working in stone-cutting factories is a known source for silica dust exposure, which has pathogenic effects on the respiratory system resulting finally in silicosis which may end in malignancy. Many changes may occur in the pulmonary system including restrictive and obstructive respiratory patterns which can be detected by spirometrical testing. In this study, forced spirometry was used to measure the forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), FEV1/FVC and the respiratory pattern for 98 stone cutting factories in Palestine. Information about the use of breath protective methods, respiratory presenting complaints and occupational history were collected. The results showed that 20% of the workers complained from at least one respiratory symptom, including cough, sputum production and chest pain. The majority of the workers showed restrictive pattern accounting for 69% of all the workers, 67% of them showed only mild restriction. On the other hand, only 11.2% showed obstructive pattern, and the remaining had normal pattern. There was a non-significant decrease in the FEV1 values (p = 0.76), but a significant decrease in the FVC values ( p < 0.001). The FEV1/FVC values were significantly high (p < 0.001). The results were compared to the mean of the measured predictive values. It is concluded that working in stone-cutting factories influences the respiratory function in a negative way, and so necessitates urgent intervention to prevent health complications. يعد العمل في مصانع تقطيع الحجر مصدرا معروفا للتعرض لغبار السيليكا، والتي لها أثر مرضي على الجهاز التنفسي مؤدية إلى الإصابة بمرض silicosis، والذي قد يؤدي إلى الإصابة بالأورام الخبيثة. قد ينتج عن ذلك التعرض تغيرات في الجهاز التنفسي بما فيها تغير نمط التنفس إلى تنفس محدود أو تنفس مغلق والتي يمكن اكتشافها باستخدام جهاز Spirometer. في هذه الدراسة، تم استخدام قياس التنفس القصري لحساب حجم الزفير القصري في الثانية الأولى(FEV1) والسعة الحيوية القصرية(FVC) ، والنسبة بينهما (EFV1/FVC)، و نوع نمط التنفس ل 98 عاملا في مصانع تقطيع الحجر في فلسطين. ومن خلال المعلومات التي تم جمعها حول استخدام العمال لوسائل حماية الجهاز التنفسي و الأعراض المرضية المتعلقة بالجهاز التنفسي التي يعاني منها العمال و تاريخ العمل لديهم. أظهرت النتائج أن 20% من العمال كانوا يعانون من عرض مرضي تنفسي واحد على الأقل مثل القحة و البلغم و ألم الصدر و أظهرت القراءات أن معظم العمال يعانون من نمط تنفسي محدود ونسبتهم 69% من إجمالي عدد العمال، وعانى 67% منهم من النوع البسيط من التنفس المحدود و من جهة أخرى كان قراءات 11.2% من العمال تظهر وجود نمط تنفسي ضيق، أما بقية العمال فقد كانت قراءاتهم طبيعية. كما كان هناك انخفاض قليل في قراءات FEV1 لدى العمال (P=0.07)، وانخفاض مهم في قيمة FVC لديهم (P<0.001). أما قيمFEV1/FVC فقد كانت مرتفعة بشكل مهم (P<0.001). لقد تمت مقارنة نتائج الفحوص بمتوسط القيم المتوقعة عند العمال أنفسهم. يستنتج من هذه الدراسة أن العمل في مصانع تقطيع الحجر له تأثير سلبي على الجهاز التنفسي مما يستوجب تدخلا عاجلا للحد من هذه المشكلة الصحية.
Role of the vacuum energy in the thermodynamics of neutron matter

The only way neutron matter can couple to the electromagnetic field is through an anomalous coupling, which plays an important role in the thermodynamics of pure neutron matter. Such theories are, however, perturbatively non-renormalisable, which presents a difficulty in terms of the unambiguous treatment of the divergencies. Here we show that despite this, an unambiguous expression can be obtained for the vacuum energy contribution to the grand canonical potential in the case of a constant magnetic field. We find that this contribution is quite small, which justifies the no-sea approximation usually made. We also discuss the density and temperature dependence of the full grand canonical potential.