Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic and Epigenetic Determinants of Aggressiveness in Cribriform Carcinoma of the Prostate

Among prostate cancers containing Gleason pattern 4, cribriform morphology is associated with unfavorable clinicopathologic factors, but its genetic features and association with long-term outcomes are incompletely understood. In this study, genetic, transcriptional, and epigenetic features of invasive cribriform carcinoma (ICC) tumors were compared with non-cribriform Gleason 4 (NC4) in The Cancer Genome Atlas (TCGA) cohort. ICC (n = 164) had distinctive molecular features when compared with NC4 (n = 102). These include: (i) increased somatic copy number variations (SCNV), specifically deletions at 6q, 8p and 10q, which encompassed PTEN and MAP3K7 losses and gains at 3q; (ii) increased SPOPmut and ATMmut; (iii) enrichment for mTORC1 and MYC pathways by gene expression; and (iv) increased methylation of selected genes. In addition, when compared with the metastatic prostate cancer, ICC clustered more closely to metastatic prostate cancer than NC4. Validation in clinical cohorts and genomically annotated murine models confirmed the association with SPOPmut (n = 38) and PTENloss (n = 818). The association of ICC with lethal disease was evaluated in the Health Professionals Follow-up Study (HPFS) and Physicians' Health Study (PHS) prospective prostate cancer cohorts (median follow-up, 13.4 years; n = 818). Patients with ICC were more likely to develop lethal cancer [HR, 1.62; 95% confidence interval (CI), 1.05–2.49], independent from Gleason score (GS). Implications: ICC has a distinct molecular phenotype that resembles metastatic prostate cancer and is associated with progression to lethal disease.
A Short Introduction to Local Graph Clustering Methods and Software

Graph clustering has many important applications in computing, but due to the increasing sizes of graphs, even traditionally fast clustering methods can be computationally expensive for real-world graphs of interest. Scalability problems led to the development of local graph clustering algorithms that come with a variety of theoretical guarantees. Rather than return a global clustering of the entire graph, local clustering algorithms return a single cluster around a given seed node or set of seed nodes. These algorithms improve scalability because they use time and memory resources that depend only on the size of the cluster returned, instead of the size of the input graph. Indeed, for many of them, their running time grows linearly with the size of the output. In addition to scalability arguments, local graph clustering algorithms have proven to be very useful for identifying and interpreting small-scale and meso-scale structure in large-scale graphs. As opposed to heuristic operational procedures, this class of algorithms comes with strong algorithmic and statistical theory. These include statistical guarantees that prove they have implicit regularization properties. One of the challenges with the existing literature on these approaches is that they are published in a wide variety of areas, including theoretical computer science, statistics, data science, and mathematics. This has made it difficult to relate the various algorithms and ideas together into a cohesive whole. We have recently been working on unifying these diverse perspectives through the lens of optimization as well as providing software to perform these computations in a cohesive fashion. In this note, we provide a brief introduction to local graph clustering, we provide some representative examples of our perspective, and we introduce our software named Local Graph Clustering (LGC).
Evolutionary MCTS for Multi-Action Adversarial Games

Turn-based multi-action adversarial games are games in which each player turn consists of a sequence of atomic actions, resulting in an extremely high branching factor. Many strategy board, card, and video games fall into this category, for which the current state of the art is Online Evolutionary Planning (OEP) – an evolutionary algorithm (EA) that treats atomic actions as genes, and complete action sequences as genomes. In this paper, we introduce Evolutionary Monte Carlo Tree Search (EMCTS) to tackle this challenge, combining the tree search of MCTS with the sequence-based optimization of EAs. Experiments on the game Hero Academy show that EMCTS convincingly outperforms several baselines including OEP and an improved variant of OEP introduced in this paper, at different time settings and numbers of atomic actions per turn. EMCTS also scales better than any existing algorithm with the complexity of the problem.
African Histoplasmosis in HIV-Negative Patients, Kimpese, Democratic Republic of the Congo

We describe a case series of histoplasmosis caused by Histoplasma capsulatum var. duboisii during July 2011–January 2014 in Kimpese, Democratic Republic of the Congo. Cases were confirmed by histopathology, immunohistochemistry, and reverse transcription PCR. All patients were HIV negative. Putative sources for the pathogen were cellar bats and guano fertilizer exploitation.
Reinforcement Learning for Proactive Caching of Contents with Different Demand Probabilities

A mobile user randomly accessing a dynamic content library over a wireless channel is considered. At each time instant, a random number of contents are added to the library and each content remains relevant to the user for a random period of time. Contents are classified into finitely many classes such that whenever the user accesses the system, he requests each content randomly with a class-specific demand probability. Contents are downloaded to the user equipment (UE) through a wireless link whose quality also varies randomly with time. The UE has a cache memory of finite capacity, which can be used to proactively store contents before they are requested by the user. Any time contents are downloaded, the system incurs a cost (energy, bandwidth, etc.) that depends on the channel state at the time of download, and scales linearly with the number of contents downloaded. Our goal is to minimize the expected long-term average cost. The problem is modeled as a Markov decision process, and the optimal policy is shown to exhibit a threshold structure; however, since finding the optimal policy is computationally infeasible, parametric approximations to the optimal policy are considered, whose parameters are optimized using the policy gradient method. Numerical simulations show that the performance gain of the resulting scheme over traditional reactive content delivery is significant, and increases with the cache capacity. Comparisons with two performance lower bounds, one computed based on infinite cache capacity and another based on non-casual knowledge of the user access times and content requests, demonstrate that our scheme can perform close to the theoretical optimum.
Cognitive Analysis for Reading and Writing of Bengali Conjuncts

In this paper, we study the difficulties arising in reading and writing of Bengali conjunct characters by human-beings. Such difficulties appear when the human cognitive system faces certain obstructions in effortlessly reading/writing. In our computer-based investigation, we consider the reading/writing difficulty analysis task as a machine learning problem supervised by human perception. To this end, we employ two distinct models: (a) an auto-derived feature-based Inception network and (b) a hand-crafted feature-based SVM (Support Vector Machine). Two commonly used Bengali printed fonts and three contemporary handwritten databases are used for collecting subjective opinion scores from human readers/writers. On this corpus, which contains the perceptive ground-truth opinion of reading/writing complications, we have undertaken to conduct the experiments. The experimental results obtained on various types of conjunct characters are promising.
A New Word Embedding Approach to Evaluate Potential Fixes for Automated Program Repair

Debugging is frequently a manual and costly task. Some work recently presented automated program repair methods aiming to reduce debugging time. Despite different approaches, the process of evaluating source code patches (potential fixes) is crucial for most of them, e.g., generate-and-validate systems. The evaluation is a complex task given that patches with different syntaxes might share the same semantics, behaving equally for typically limited specifications. Hence, many approaches fail to better explore the search space of patches, leading them to not reach the patch that fixes the bug. Some research points that a buggy code is more entropic, i.e., less natural than its fixed version. So, this work proposes applying Word2vec, a word embedding model, to improve the repair evaluation process based on the naturalness obtained from a corpus of known fixes. Word2vec captures co-occurrence relationships between words in a given context and then predicts the contextual words of a given word. This technique has been applied to deal with richer semantic relationships in a text. Word2vec evaluates patches according to distances of document vectors and a softmax output layer. We analyze the performance of our proposal with mutated patches created from correct source codes and we simulate potential fixes generated by automated program repair approaches. Thus, the main contribution of this paper is a new method to evaluate patches used in automated program repair methods. The results show that Word2vec-based metrics are capable of analyzing source code naturalness and be used to evaluate source code patches.
Two new species of the Neotropical Lophocampahyalinipuncta (Rothschild) group (Lepidoptera, Erebidae, Arctiinae)

Abstract Two new species of Lophocampa Harris are described and illustrated, Lophocampaazuayensissp. n. , and Lophocampacarpishensissp. n. Both new species were confused with Lophocampahyalinipuncta (Rothschild, 1909), and a comparative diagnosis is provided.
Learning for and with Efficient Computing Systems

Machine learning approaches have been widely adopted in recent years due to their capability of learning from data rather than hand-tuning features manually. We investigate two important aspects of machine learning methods, i.e., (i) applying machine learning in computing system optimization and (ii) optimizing machine learning algorithms, especially deep convolutional neural networks, so they can train and infer efficiently. As power emerges as the main constraint for computing systems, controlling power consumption under a given Thermal Design Power (TDP) while maximizing the performance becomes increasingly critical. Meanwhile, systems have certain performance constraints that the applications should satisfy to ensure Quality of Service (QoS). Learning approaches have drawn significant attention recently due to the ability to adapt to the ever-increasing complexity of the system and applications. In this thesis, we propose On-line Distributed Reinforcement Learning (OD-RL) based algorithms for many-core system performance improvement under both power and performance constraints. The experiments show that compared to the state-of-the-art algorithms, our approach: 1) produces up to 98% less budget overshoot, 2) up to 23% higher energy efficiency, and 3) two orders of magnitude speedup over state-of-the-art techniques for systems with hundreds of cores, while an improved version can better satisfy performance constraints. To further improve the sample-efficiency of RL algorithms, we propose a novel Bayesian Optimization approach to speed up reinforcement learning-based DVFS control by 37.4x while maintaining the performance of the best rule-based DVFS algorithm. Convolutional Neural Networks (CNNs) have shown unprecedented capability in visual learning tasks. While accuracy-wise CNNs provide unprecedented performance, they are also known to be computationally intensive and energy demanding for modern computer systems. We propose Virtual Pooling (ViP), a model-level approach to improve inference speed and energy consumption of CNN-based image classification and object detection tasks, with provable error bound. We show the efficacy of ViP through exten-
2 Timing Side-Channel Debugging with D

What properties about the internals of a program explain the possible differences in its overall running time for different inputs? In this paper, we propose a formal framework for considering this question we dub trace-set discrimination. We show that even though the algorithmic problem of computing maximum likelihood discriminants is NP-hard, approaches based on integer linear programming (ILP) and decision tree learning can be useful in zeroing-in on the program internals. On a set of Java benchmarks, we find that compactly-represented decision trees scalably discriminate with high accuracy—more scalably than maximum likelihood discriminants and with comparable accuracy. We demonstrate on three larger case studies how decision-tree discriminants produced by our tool are useful for debugging timing side-channel vulnerabilities (i.e., where a malicious observer infers secrets simply from passively watching execution times) and availability vulnerabilities.
Concrete Cracks Detection Based on Deep Learning Image Classification

This work aims at developing a machine learning-based model to detect cracks on concrete surfaces. Such model is intended to increase the level of automation on concrete infrastructure inspection when combined to unmanned aerial vehicles (UAV). The developed crack detection model relies on a deep learning convolutional neural network (CNN) image classification algorithm. Provided a relatively heterogeneous dataset, the use of deep learning enables the development of a concrete cracks detection system that can account for several conditions, e.g., different light, surface finish and humidity that a concrete surface might exhibit. These conditions are a limiting factor when working with computer vision systems based on conventional digital image processing methods. For this work, a dataset with 3500 images of concrete surfaces balanced between images with and without cracks was used. This dataset was divided into training and testing data at an 80/20 ratio. Since our dataset is rather small to enable a robust training of a complete deep learning model, a transfer-learning methodology was applied; in particular, the open-source model VGG16 was used as basis for the development of the model. The influence of the model’s parameters such as learning rate, number of nodes in the last fully connected layer and training dataset size were investigated. In each experiment, the model’s accuracy was recorded to identify the best result. For the dataset used in this work, the best experiment yielded a model with accuracy of 92.27%, showcasing the potential of using deep learning for concrete crack detection.
A Quantitative Microbial Risk Assessment to Evaluate Zoonotic Risks : Human Salmonellosis through Household Consumption of Fresh Minced Pork Meat in Belgium as Example

A quantitative microbial risk assessment (QMRA) according to the Codex Alimentarius Principles is conducted to evaluate the risk on human salmonellosis through household consumption of fresh minced pork meat in Belgium. The quantitative exposure assessment is carried out by building a modular risk model, called the XXX-model, which covers the pork production from farm to fork. In the XXX-model, the food production pathway is split up in six consecutive modules: (1) primary production, (2) transport & lairage, (3) slaughterhouse, (4) post-processing, (5) distribution & storage and (6) preparation & consumption. All the modules are developed to resemble as closely as possible the Belgian situation making use of the available national data. Several statistical refinements and improved modeling techniques are proposed. The model produces highly realistic results. The baseline predicted number of annual salmonellosis cases is 20513 (st. dev. 9061.45). The risk is estimated higher for the susceptible population (est. 4.713 × 10−5; st. dev. 1.466 × 10−5) compared to the normal population (est. 7.704 × 10−6; st. dev. 5.414 × 10−6) and is mainly due to undercooking and for a smaller extent to cross contamination in the kitchen via cook’s hands.
Indoor RGB-D Compass from a Single Line and Plane

We propose a novel approach to estimate the three degrees of freedom (DoF) drift-free rotational motion of an RGB-D camera from only a single line and plane in the Manhattan world (MW). Previous approaches exploit the surface normal vectors and vanishing points to achieve accurate 3-DoF rotation estimation. However, they require multiple orthogonal planes or many consistent lines to be visible throughout the entire rotation estimation process; otherwise, these approaches fail. To overcome these limitations, we present a new method that estimates absolute camera orientation from only a single line and a single plane in RANSAC, which corresponds to the theoretical minimal sampling for 3-DoF rotation estimation. Once we find an initial rotation estimate, we refine the camera orientation by minimizing the average orthogonal distance from the endpoints of the lines parallel to the MW axes. We demonstrate the effectiveness of the proposed algorithm through an extensive evaluation on a variety of RGB-D datasets and compare with other state-of-the-art methods.
Effect of CO 2 , nutrients and light on coastal plankton . III . Trophic cascade , size structure and composition

We investigated the impacts of climate change-associated abiotic factors on the species composition and size structure of coastal phytoplankton communities. Surface coastal water collected off the coast of Málaga (Spain) was incubated outdoors during a 7 d microcosm experi ment. The natural phytoplankton communities were exposed to high and low conditions of CO2, nutrients and light. During the first 2 d, a positive response to increased CO2 and nutrient concentration was observed in terms of abundance and chlorophyll in all size fractions (<2, 2 to 20, and >20 μm). After 2 d, a trophic cascade effect was observed within the phytoplankton communities for all treatments. The absence of mesozooplankton led to an increase in microzooplankton abundance, which coincided with a decrease in the abundance of phytoplankton <6 μm equivalent spherical dia meter (ESD). At the same time, an increased concentration of larger phytoplankton was ob served. Consequently, a diatom bloom dominated by Leptocylindrus danicus and Chaetoceros sp. developed, peaking on Day 5 in the high-light treatment and on Day 6 in the low-light treatment. The cascade effect was evident in both the smaller and the larger ranges of the size−abundance spectra (SAS). Although this trophic interaction occurred in all treatments in a similar way, there were still significant differences among treatments. Diatoms with cell sizes >20 μm ESD showed a positive response to the effects of increasing CO2 and nutrient concentration. These results high light the importance of trophic interactions other than abiotic factors such as CO2 and nutrient availability in shaping the size structure of Mediterranean phytoplankton. More specifically, this work shows the importance of trophic cascade effects in scaling the plankton SAS and should be considered in both enclosure experiments and field measurements that deal with size distribution.
Low-Rank Approximation to Heterogeneous Elliptic Problems

In this work, we investigate the low-rank approximation of elliptic problems in heterogeneous media by means of Kolmogrov $n$-width and asymptotic expansion. This class of problems arises in many practical applications involving high-contrast media, and their efficient numerical approximation often relies crucially on certain low-rank structure of the solutions. We provide conditions on the permeability coefficient $\kappa$ that ensure a favorable low-rank approximation. These conditions are expressed in terms of the distribution of the inclusions in the coefficient $\kappa$, e.g., the values, locations, and sizes of the heterogeneous regions. Further, we provide a new asymptotic analysis for high-contrast elliptic problems based on the perfect conductivity problem and layer potential techniques, which allows deriving new estimates on the spectral gap for such high-contrast problems. These results provide theoretical underpinnings for several multiscale model reduction algorithms.
Variation in discrimination factors (Δ15N and Δ13C): the effect of diet isotopic values and applications for diet reconstruction

Summary 1The use of stable isotopic techniques to study animal diets and trophic levels requires a priori estimates of discrimination factors (Δ13C and Δ15N, also called fractionation factors), which are the differences in isotopic composition between an animal and its diet. Previous studies have shown that these parameters depend on several sources of variation (e.g. taxon, environment, tissue) but diet as a source of variation still needs assessment. 2We conducted an extensive review of the literature (66 publications) concerning estimates of animal-diet Δ13C (n = 290) and Δ15N (n = 268). We analysed this data set to test the effect of diet isotopic ratio on the discrimination factor, taking into account taxa, tissues, environments and lipid extraction treatments. Our results showed differences among taxonomic classes for Δ13C, but not for Δ15N, and significant differences among tissues for both Δ13C and Δ15N. We found a significant negative relationship between both, Δ13C and Δ15N, with their corresponding diet isotopic ratios. This relationship was found also within taxonomic classes for mammals (Δ13C and Δ15N), birds (Δ13C), fishes (Δ13C and Δ15N) and invertebrates (Δ13C and Δ15N). From these relationships, we propose a method to calculate discrimination factors based on data on diet isotope ratios (termed the ‘Diet-Dependent Discrimination Factor’, DDDF). 3To investigate current practice in the use of discrimination factors, we reviewed studies that used multi-resource isotopic models. More than 60% of models used a discrimination factor coming from a different species or tissues, and in more than 70% of models, only one Δ13C or Δ15N was used for all resources, even if resources had very different isotopic ratios. Also, we estimated DDDFs for the studies that used isotopic models. More than 40% used Δ15N values and more than 33% used Δ13C values differing > 2‰ from estimated DDDFs. 4Synthesis and applications. Over the last decade, applied ecologists have discovered the potential of stable isotopes for animal diet reconstruction, but the successful adoption of the method relies on a good estimation of discrimination factors. We draw attention to the high variability in discrimination factors, advise caution in the use of single discrimination factors in isotopic models, and point to a method for obtaining adequate values for this parameter when discrimination factors cannot be measured experimentally. Future studies should focus on understanding why discrimination factors vary as a function of the isotopic value of the diet.