Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Probabilistic Causal Structure Discovery

Complex causal networks underlie many real-world problems, from the regulatory interactions between genes to the environmental patterns used to understand climate change. Computational methods seek to infer these causal networks using observational data and domain knowledge. In this paper, we identify three key requirements for inferring the structure of causal networks for scientific discovery: (1) robustness to noise in observed measurements; (2) scalability to handle hundreds of variables; and (3) flexibility to encode domain knowledge and other structural constraints. We first formalize the problem of joint probabilistic causal structure discovery. We develop an approach using probabilistic soft logic (PSL) that exploits multiple statistical tests, supports efficient optimization over hundreds of variables, and can easily incorporate structural constraints, including imperfect domain knowledge. We compare our method against multiple well-studied approaches on biological and synthetic datasets, showing improvements of up to 20% in F1-score over the best performing baseline in realistic settings.
Genomic Prediction of Breeding Values Using a Subset of SNPs Identified by Three Machine Learning Methods

The analysis of large genomic data is hampered by issues such as a small number of observations and a large number of predictive variables (commonly known as “large P small N”), high dimensionality or highly correlated data structures. Machine learning methods are renowned for dealing with these problems. To date machine learning methods have been applied in Genome-Wide Association Studies for identification of candidate genes, epistasis detection, gene network pathway analyses and genomic prediction of phenotypic values. However, the utility of two machine learning methods, Gradient Boosting Machine (GBM) and Extreme Gradient Boosting Method (XgBoost), in identifying a subset of SNP makers for genomic prediction of breeding values has never been explored before. In this study, using 38,082 SNP markers and body weight phenotypes from 2,093 Brahman cattle (1,097 bulls as a discovery population and 996 cows as a validation population), we examined the efficiency of three machine learning methods, namely Random Forests (RF), GBM and XgBoost, in (a) the identification of top 400, 1,000, and 3,000 ranked SNPs; (b) using the subsets of SNPs to construct genomic relationship matrices (GRMs) for the estimation of genomic breeding values (GEBVs). For comparison purposes, we also calculated the GEBVs from (1) 400, 1,000, and 3,000 SNPs that were randomly selected and evenly spaced across the genome, and (2) from all the SNPs. We found that RF and especially GBM are efficient methods in identifying a subset of SNPs with direct links to candidate genes affecting the growth trait. In comparison to the estimate of prediction accuracy of GEBVs from using all SNPs (0.43), the 3,000 top SNPs identified by RF (0.42) and GBM (0.46) had similar values to those of the whole SNP panel. The performance of the subsets of SNPs from RF and GBM was substantially better than that of evenly spaced subsets across the genome (0.18–0.29). Of the three methods, RF and GBM consistently outperformed the XgBoost in genomic prediction accuracy.
A memory-deduplication side-channel attack to detect applications in co-resident virtual machines

Virtualization offers the possibility of hosting services of multiple customers on shared hardware. When more than one Virtual Machine (VM) run on the same host, memory deduplication can save physical memory by merging identical pages of the VMs. However, this comes at the cost of leaking information between VMs. Based on that, we propose a novel timing-based side-channel attack that allows to identify software versions running in co-resident VMs or on the host. Our attack tests for the existence of memory pages in co-resident VMs that are unique among all versions of the respective software. Our evaluation results indicate that with few repetitions of our attack we can precisely identify software versions within reasonable time frames.
A multi-task neural network for multilingual sentiment classification and language detection on Twitter

In this paper, we propose a novel approach for classifying both the sentiment and the language of tweets. Our proposed architecture comprises a convolutional neural network (ConvNet) with two distinct outputs, each of which designed to minimize the classification error of either sentiment assignment or language identification. Results show that our method outperforms both single-task and multi-task state-of-the-art approaches for classifying multilingual tweets.
League of lasers: a superhuman sport using motion tracking

League of Lasers is a motion-based game where two teams compete in a mix between football and Pong [2]. Players use `virtual mirrors' to try to guide a laser pulse towards the opponent team's target. The game aims at stimulating interaction between players by making cooperation a vital part of the gameplay, while having them physically move near each other. The game tracks the physical movement of players as the main control actions. A demonstration trailer can be found here: https://goo.gl/v7mu6k
Evaluation of Individual and Group Lending under Asymmetric information

The paper attempts to find the socially best loan contract by comparing exante welfare, interest and default rates of individual and group lending. We introduce a general framework which allows auditing policies and interest rates to be simultaneously determined by maximising the social welfare. Both variables vary with the types of risk considered: independently identically distributed and positively correlated risk. An individual project outcome is private information of its owner, but reported outcomes can be audited at a cost which then publicly reveals the true project outcome. We find that incentive compatibility in a group loan context is delicate: the conditions for truth telling vary with the borrowers’ perception of the overall solvency of the group. In addition, group loans are often made to local groups who have established local networks. This may mean that the group has cheaper policing of truthtelling, but also that the risks on projects within the group are likely to be correlated. To explore this, we numerically solve for the optimal contracts with varying audit cost differences and correlation, using a betabinomial distribution. We find that with an audit cost advantage, small group loans (typically to two borrowers) dominate individual loans even with correlation. But if audit costs are identical, the individual loan dominates. In the larger the group, the higher the audit probability is required to ensure truthtelling. Our finding provides an argument for why the number of borrowers should be limited to 2-5.
On the I/O characteristics of the mobile web browsers

Recent advancement of mobile device technologies played a key role in increasing the user base, and a large number of people are using the device to gather information and browse the Internet. Web browsers are in the center of such activities. In this paper, we analyze the I/O characteristics of five popular web browsers: Chrome, Firefox, Opera, Dolphin, and UC. We chose a website that has 52 objects and 509KB as the sum of all the objects. After analyzing the ratio of read and write I/Os, we found that browsing is a write-intensive activity. Three of the browsers issued more than 50% of I/Os using fsync() and fdatasync(). About 50% of I/Os are for updating file system metadata and journal. The number of I/Os that a browser generates for SQLite and cache files is more than 50% at the lowest and 90% at the highest of the total I/O counts. In this paper, we define write amplification of web browsers to address the amount of extra I/Os browsers generate. We found that web browsers write on average of 11.7 times more data to the storage than the sum of original web contents.
The Disarticulated Movement: Barriers to Maya Mobilization in Post-Conflict Guatemala

Abstract Over the last decades, indigenous movements have propelled the political empowerment of historically marginalized groups in Latin America. The Maya struggle for ethnic equality in Guatemala, however, since its reawakening during the peace process, has reached an impasse. Based on field research consisting of dozens of elite interviews, this article analyzes the patterns of and obstacles to present-day Maya mobilization. It combines movement-internal and -external factors in an overarching theoretical argument about indigenous movements' capacity to construct strong collective voices. In the Guatemalan case, organizational sectorization, the lack of elite consensus on key substantive issues, and unclear alliance strategies compromise the effectiveness of horizontal voice among Maya organizations. These problems are exacerbated by the lasting effects of the country's unique history of violence and state strategies of divide and rule, preventing the emergence of a strong vertical voice capable of challenging the Guatemalan state.
Single-Stage Switched-Capacitor Module (S3CM) Topology for Cascaded Multilevel Inverter

A two-stage switched-capacitor based multilevel inverter possesses a drawback such that switches in the second stage (i.e., H-bridge) endure higher voltage stress. To resolve this problem, this letter proposes a single-stage switched-capacitor module (S3CM) topology for cascaded multilevel inverter, which ensures the peak inverse voltage across all the switches within the dc source voltage. A total of nine voltage levels can be generated with only one dc source and two incorporated capacitors. Hence, the number of isolated dc sources is significantly reduced compared to a cascaded H-bridge. In addition, voltage boosting gain of two is achieved. A comparative analysis of the recent topology reveals that the proposed S3CM topology achieves switch count reduction. The operation of the proposed topology is validated through circuit analysis followed by simulation and experimental results of a single-module (9-level) prototype.
Protective Effects of Lactic Acid Bacteria Against TLR4 Induced Inflammatory Response in Hepatoma HepG2 Cells Through Modulation of Toll-Like Receptor Negative Regulators of Mitogen-Activated Protein Kinase and NF-κB Signaling

The beneficial effects of probiotics in several liver diseases have been investigated in both animal and clinical models; however, the precise mechanisms responsible for their effects have not yet been elucidated. Gut transmitted endotoxins such as LPS have been shown to play critical roles in hepatic inflammation and injury. Therefore, in this study, we investigated the beneficial role of selected lactic acid bacteria (LABs) on reduction of hepatic steatosis (HS) and attenuation of LPS induced inflammatory response in vitro. Total cellular fluid (TCF) of LABs treatment reduced HS by decreasing the amount of lipid accumulation in vitro. Additionally, HepG2 cells exposed to LPS showed increased expression of exacerbated inflammatory cytokines, such as IL-6, CXCL8, CCL2, and TNF-α, but these effects were counteracted when cells were treated with TCF of LABs prior to LPS challenge. Moreover, TCF of LABs was able to modulate mRNA levels of TLR negative regulators and protein levels of p38 MAPK and p65 NF-κB transcription factors. However, these modulations were differed remarkably between both free fatty acid treated and untreated HepG2 cells. Heat-killed LABs were also indirectly suppressed THP-1 cells to produce higher level of IL-10, TLR4, and lower at genes level of TGF-β, IL-1β, and IL-6, and at protein level of TNF-α in response to LPS. Taken together, our findings indicate that selected LABs exhibit profound immunoregulatory effects on liver cells via modulation of TLR negative regulators of the MAPK and NF-κB pathways.
Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters

In this work, we consider the problem of estimating a behaviour policy for use in Off-Policy Policy Evaluation (OPE) when the true behaviour policy is unknown. Via a series of empirical studies, we demonstrate how accurate OPE is strongly dependent on the calibration of estimated behaviour policy models: how precisely the behaviour policy is estimated from data. We show how powerful parametric models such as neural networks can result in highly uncalibrated behaviour policy models on a real-world medical dataset, and illustrate how a simple, non-parametric, k-nearest neighbours model produces better calibrated behaviour policy estimates and can be used to obtain superior importance sampling-based OPE estimates.
Performance Evaluation of Gated Pipes Technique for Improving Surface Irrigation Efficiency in Maize Hybrids

Waterlogging and low application efficiency are the main problems inherent with surface irrigation in the Nile Delta. Develop surface irrigation using gated pipes (GP) is a new method to be used to distribute water into furrow irrigated fields as strategy based on water saving. Laboratory calibration was conducted out to evaluate the hydraulic characteristics of pipe gates. Field experiments were conducted at the Experimental Farm of the Agriculture Faculty, Minufiya University during 2013 and 2014 seasons to evaluate the performance of utilize gated pipes technique for irrigating five maize varieties (S.C 10, S.C 130, S.C 131, S.C 2031 and T.W.C 321). The results revealed that the highest amount of water applied was with traditional surface irrigation (6423.81 m3·ha-1. Use of gated pipes system GP1 as compared to traditional irrigation reduced water application by 923.81 m3·ha-1 with grain and stover yields increases of 5.7% and 3.4%, respectively. Traditional irrigation system achieved lowest irrigation performance parameters compared to gated pipes systems. Maize physiological attributes, yield, water use efficiency (WUE) and nitrogen accumulation were significantly decreased by either deficit or surplus irrigation than of GP1 rate. S.C 2031 variety significantly surpassed other varieties in abovementioned traits. Significant interaction effects were detected in both seasons. Maize varieties respond differently to irrigation systems. The highest values of grain yield (11062.6 and 10911.8 kg·ha-1 and stover yield (13639.0 and 13902.2 kg·ha-1) were obtained by S.C 2031 irrigated with GP1 system in both seasons. From the above mentioned results, it is concluded that the gated pipes technique is better than traditional irrigation for improving WUE and maize productivity under Nile Delta conditions.
Solving Atari Games Using Fractals And Entropy

In this paper, we introduce a novel MCTS based approach that is derived from the laws of the thermodynamics. The algorithm coined Fractal Monte Carlo (FMC), allows us to create an agent that takes intelligent actions in both continuous and discrete environments while providing control over every aspect of the agent behavior. Results show that FMC is several orders of magnitude more efficient than similar techniques, such as MCTS, in the Atari games tested.
Improving the performance of MO-RV-GOMEA on problems with many objectives using tchebycheff scalarizations

The Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) has been shown to exhibit excellent performance in solving various bi-objective benchmark and real-world problems. We assess the competence of MO-RV-GOMEA in tackling many-objective problems, which are normally defined as problems with at least four conflicting objectives. Most Pareto dominance-based Multi-Objective Evolutionary Algorithms (MOEAs) typically diminish in performance if the number of objectives is more than three because selection pressure toward the Pareto-optimal front is lost. This is potentially less of an issue for MO-RV-GOMEA because its variation operator creates each offspring solution by iteratively altering a currently existing solution in a few decision variables each time, and changes are only accepted if they result in a Pareto improvement. For most MOEAs, integrating scalarization methods is potentially beneficial in the many-objective context. Here, we investigate the possibility of improving the performance of MO-RV-GOMEA by further guiding improvement checks during solution variation in MO-RV-GOMEA with carefully constructed Tchebycheff scalarizations. Results obtained from experiments performed on a selection of well-known problems from the DTLZ and WFG test suites show that MO-RV-GOMEA is by design already well-suited for many-objective problems. Moreover, by enhancing it with Tchebycheff scalarizations, it outperforms M0EA/D-2TCHMFI, a state-of-the-art decomposition-based MOEA.
A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities

We present a weakly supervised deep learning model for classifying thoracic diseases and identifying abnormalities in chest radiography. In this work, instead of learning from medical imaging data with region-level annotations, our model was merely trained on imaging data with image-level labels to classify diseases, and is able to identify abnormal image regions simultaneously. Our model consists of a customized pooling structure and an adaptive DenseNet front-end, which can effectively recognize possible disease features for classification and localization tasks. Our method has been validated on the publicly available ChestX-ray14 dataset. Experimental results have demonstrated that our classification and localization prediction performance achieved significant improvement over the previous models on the ChestX-ray14 dataset. In summary, our network can produce accurate disease classification and localization, which can potentially support clinical decisions.
Situation-dependent combination of long-term and session-based preferences in group recommendations: an experimental analysis

A major challenge for conversational group recommender systems is how to properly exploit the user's preferences induced by the interactions between group members, which may deviate from the user's long-term ones. We argue that the relative importance of the long-term and group-induced preferences should vary according to the specific group settings. To support this claim, we compare alternative ways of combining these two types of user's preferences, under three diverse settings that may influence users' behavior. Our experimental study shows how a combination scheme that weighs more the long-term preferences can better serve the group when the group discussion has no impact on group members' preferences, but when the group context pushes members to have either more or less similar preferences, then users benefit from a recommender that weighs more the group-induced preferences.
Neural Basis of Depression Related to a Dominant Right Hemisphere: A Resting-State fMRI Study

Background In the past, studies on the lateralization of the left and right hemispheres of the brain suggested that depression is dominated by the right hemisphere of the brain, but the neural basis of this theory remains unclear. Method Functional magnetic resonance imaging of the brain was performed in 22 depressive patients and 15 healthy controls. The differences in the mean values of the regional homogeneity (ReHo) of two groups were compared, and the low-frequency amplitudes of these differential brain regions were compared. Results The results show that compared with healthy subjects, depressive patients had increased ReHo values in the right superior temporal gyrus, right middle temporal gyrus, left inferior temporal gyrus, left middle temporal gyrus, right middle frontal gyrus, triangular part of the right inferior frontal gyrus, orbital part of the right inferior frontal gyrus, right superior occipital gyrus, right middle occipital gyrus, bilateral anterior cingulate, and paracingulate gyri; reduced ReHo values were seen in the right fusiform gyrus, left middle occipital gyrus, left lingual gyrus, and left inferior parietal except in the supramarginal and angular gyri. Conclusions The results show that regional homogeneity mainly occurs in the right brain, and the overall performance of the brain is such that right hemisphere synchronization is enhanced while left hemisphere synchronization is weakened. ReHo abnormalities in the resting state can predict abnormalities in individual neurological activities that reflect changes in the structure and function of the brain; abnormalities shown with this indicator are the neuronal basis for the phenomenon that the right hemisphere of the brain has a dominant effect on depression.
Nitrogen fertilization as ammonium or nitrate-N on Hippeastrum hybridum bulb growth.

Hippeastrum (Hippeastrum hybridum), a native of Central and South America, is a bulbous ornamental flowering plant in the Amaryllidaceae family. However, the correct balance of NH4 to NO3-nitrogen in a fertilizer mix for Hippeastrum plants is largely unknown. Nitrogen was applied 2x weekly following irrigation at either 0.6 g (high), 0.3 g (medium) or 0.15 g (low) total N every four months. Nitrogen was supplied in different combinations of NO3 and/or NH4. Nitrate:NH4-N ratios were either 100% NO3:0% NH4 (100NO3), 70% NO3:30% NH4 (70NO3), 50% NO3:50% NH4 (50NO3) (second group only), 30%NO3:70%NH4 (30NO3), or 0% NO3/100% NH4 (100NH4). Growth in bulb diameter after one year of fertilizer treatments not only increased from 0.15 to 0.6 g N (low to high level), but also differed with the form of N supplied to the plant. The largest diameter bulbs were produced in the 70NO3 and 50NO3 high N treatments. Within any NO3/NH4-N ratio grouping, fertilization at the high N rate resulted in larger diameter bulbs. No significant differences existed between treatments in the number of bulbs produced. Bulb growth was greater with a portion of N supplied as NO3 than with NH4-N alone. These results indicate that application of N as a mixture of NH4 and NO3 at 0.6 g per 4 months produces the largest increase in bulb diameter.
Biosynthesis of Schwertmannite and Goethite in a Bioreactor with Acidophilic Fe(II)-Oxidizing Betaproteobacterium Strain GJ-E10

Iron oxide minerals, schwertmannite and goethite, which are naturally occurring in acidic environments are attractive nanostructured materials because of the potential diverse applications. Although the biosynthesis of schwertmannite by acidophilic Fe(II)-oxidizing microorganisms has been investigated, little is known about goethite biosynthesis under acidic conditions. To examine a biological approach to the synthesis of these minerals, bioreactor experiments were conducted with a newly isolated acidophilic betaproteobacterium. The bioproducts were characterized by powder X-ray diffraction, cryogenic Mossbauer spectroscopy, and electron microscopy. The cultures growing at 25 °C and pH 3.0 or at 37 °C and pH 2.5 oxidized Fe(II) and precipitated schwertmannite rapidly. Increasing pH at each temperature resulted in the concomitant production of goethite, and 90% pure goethite was obtained at 37 °C and pH 3.5. The goethite phase was nano-sized and had relatively large specific surface area (133 m2·g−1), leading to high sorption capacity for metal oxyanions. Schwertmannite was also a good adsorbent for oxyanions, regardless of the smaller specific surface area. Our results indicate that these acidophilic microbial cultures serve as a simple rapid system for the synthesis of nanostructured goethite as well as schwertmannite.