Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Nearest-Neighbor Music Recommendations

Automated recommendations for next tracks to listen to or to include in a playlist are a common feature on modern music platforms. Correspondingly, a variety of algorithmic approaches for determining tracks to recommend have been proposed in academic research. The most sophisticated among them are often based on conceptually complex learning techniques which can also require substantial computational resources or special-purpose hardware like GPUs. Recent research, however, showed that conceptually more simple techniques, e.g., based on nearest-neighbor schemes, can represent a viable alternative to such techniques in practice. In this paper, we describe a hybrid technique for next-track recommendation, which was evaluated in the context of the ACM RecSys 2018 Challenge. A combination of nearest-neighbor techniques, a standard matrix factorization algorithm, and a small set of heuristics led our team KAENEN to the 3rd place in the "creative" track and the 7th one in the "main" track, with accuracy results only a few percent below the winning teams. Given that offline prediction accuracy is only one of several possible quality factors in music recommendation, practitioners have to validate if slight accuracy improvements truly justify the use of highly complex algorithms in real-world applications.
Outcomes of children receiving Group-Early Start Denver Model in an inclusive versus autism-specific setting: A pilot randomized controlled trial

A major topic of debate is whether children with autism spectrum disorder should be educated in inclusive or specialized settings. We examined the feasibility and preliminary effectiveness of delivering the Group-Early Start Denver Model to children with autism spectrum disorder in inclusive versus specialized classrooms. We randomly assigned 44 preschoolers with autism spectrum disorder to receive the Group-Early Start Denver Model across one school calendar year in classrooms that included only children with autism spectrum disorder or mostly children who were typically developing. Blind-rated indicators of teaching quality showed similar results across settings, which were above the local benchmark. Children showed improvements across blinded proximal measures of spontaneous vocalization, social interaction, and imitation and across distal measures of verbal cognition, adaptive behavior, and autism symptoms irrespective of intervention setting. Mothers of participants experienced a reduction in stress irrespective of child intervention setting. Across both settings, age at intervention start was negatively associated with gains in verbal cognition. Delivery of Group-Early Start Denver Model in an inclusive setting appeared to be feasible, with no significant differences in teaching quality and child improvements when the program was implemented in inclusive versus specialized classrooms.
Deep Probabilistic Video Compression

We propose a variational inference approach to deep probabilistic video compression. Our model uses advances in variational autoencoders (VAEs) for sequential data and combines it with recent work on neural image compression. The approach jointly learns to transform the original video into a lower-dimensional representation as well as to entropy code this representation according to a temporally-conditioned probabilistic model. We split the latent space into local (per frame) and global (per segment) variables, and show that training the VAE to utilize both representations leads to an improved rate-distortion performance. Evaluation on small videos from public data sets with varying complexity and diversity show that our model yields competitive results when trained on generic video content. Extreme compression performance is achieved for videos with specialized content if the model is trained on similar videos.
Properties of familiar face representations: Only contrast positive faces contain all information necessary for efficient recognition.

It is difficult to recognize the identity of a face presented in negative contrast. This difficulty, however, is substantially reduced when only the eye region is contrast positive in an otherwise negative face image, and recognition of these so-called contrast chimeras approaches performance with full positive faces. This apparently similar accuracy has led researchers to suggest that familiar face representations are built around the eye region. The present study used the N250r, an event-related brain potential correlate of repetition priming, to examine whether chimera recognition is similarly efficient as positive face recognition. In a series of 3 experiments, we found a clear N250r for positive but reduced or even absent repetition effects for negative and chimera faces. This finding held true independent of whether the same basic pictures of familiar faces were used as prime and target stimuli (Experiment 1) or not (Experiments 2 and 3). Similar results were also obtained independent of whether positive, negative or chimera primes preceded full positive targets (Experiments 1 and 2) or targets in the same respective contrast format (Experiment 3). These results indicate that only positive faces contain all information necessary for optimal face recognition and that even though contrast chimeras are recognized highly accurately, the underlying processes work less efficiently as compared with normal face recognition. We conclude that familiar face representations are not built around the eyes but comprise detailed information from other regions of the face. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Antioxidative Effects of Piperine against Cadmium-Induced Oxidative Stress in Cultured Human Peripheral Blood Lymphocytes

Abstract Cadmium (Cd) has a remarkable property of generating oxidative stress. It upregulates the level of reactive oxygen species, which generates damage to lipids, proteins, and DNA The level of oxidative stress by Cd is observed by inhibitory effects of antioxidant enzymes such as catalase, superoxide dismutase, glutathione reductase, and glutathione peroxidase. Piperine is one of the plant-derived alkaloids isolated from Piper nigrum and Piper longum. It diminishes the level of oxidative damage by quenching the free radicals and reactive oxygen species and inhibiting the lipid peroxidation. It is well known to induce stress proteins, including metallothionein (MT), a metal-binding protein and heat shock protein (HSP70). In the present work, antioxidative effects of piperine have been studied against Cd-induced oxidative stress. We observed its ameliorative effects using various biomarkers, such as comet and lipid peroxidation (LPA) assays in cultured human peripheral blood lymphocytes from healthy individuals. Piperine at 35 and 50 µM concentrations significantly reduces the tail moment and peroxidation of lipids. We also explored the relationship of MT2A gene polymorphism on oxidative and antioxidative effects of Cd and piperine, respectively. At selected concentration, we observed a decrease in the mean value of tail moment and lipid peroxidation level. However, the overall effect was statistically nonsignificant (p > .05).
Contradiction processing in schizophrenia

ABSTRACT Introduction: Patients with schizophrenia present clinically with difficulties in manipulating contradictory information in the form of loose associations, surface contradictions and delusional beliefs. It is to date unclear whether patients can detect and process information that contradicts their beliefs and prior knowledge and whether this capacity is related to their symptoms and the nature of contradictory stimuli (e.g., personally significant information, emotional information). Methods: We probed contradiction processing in patients with schizophrenia and healthy controls using sentence verification tasks that involve self-referential judgements (Experiment 1) and general knowledge (Experiment 2), while manipulating the emotional content of the stimuli. Results: We found no differences between patients and controls either on reaction time (Experiment 1 & 2) or accuracy measures (Experiment 1). Conclusions: Our results show no general impairment in contradiction processing in schizophrenia. Rather, failures to detect and correct contradictions in symptoms such as formal though disorder or delusions could arise through a complex interplay between executive dysfunctions, stress and the emotional content of the information.
Counting homomorphisms in plain exponential time

In the counting Graph Homomorphism problem (#GraphHom) the question is: Given graphs G,H, find the number of homomorphisms from G to H. This problem is generally #P-complete, moreover, Cygan et al. proved that unless the ETH is false there is no algorithm that solves this problem in time O(|V(H)|^{o(|V(G)|)}. This, however, does not rule out the possibility that faster algorithms exist for restricted problems of this kind. Wahlstrom proved that #GraphHom can be solved in plain exponential time, that is, in time k^{|V(G)|+V(H)|}\poly(|V(H)|,|V(G)|) provided H has clique width k. We generalize this result to a larger class of graphs, and also identify several other graph classes that admit a plain exponential algorithm for #GraphHom.
CrowdPop: Leveraging Multi-Source Crowd-Contributed Data for App Evolutionary Pattern Analysis and Popularity Prediction

The popularity prediction of mobile apps provides substantial value to a broad range of applications, ranging from app development to targeted advertising. However, most previous studies do this work by establishing regression models for impact factors, or using clustering and classification algorithms. It does not fully investigate the process of popularity evolution and the reasons behind it. In this paper, we discuss and analyze the potential predictors, especially the impact of early evolutionary patterns on future popularity. To this end, we first explore six basic evolutionary patterns and six impact factors that are closely related to app popularity. After detailed analysis, we present CrowdPop, a popularity prediction model based on the Random Forest algorithm, to quantify patterns and factors as predictors of CrowdPop. The experiment results with a real-world dataset of 126 apps indicate that, compared with baseline methods, our CrowdPop performs better in mobile app popularity prediction.
POLO: a POLicy-based Optimization library

We present POLO --- a C++ library for large-scale parallel optimization research that emphasizes ease-of-use, flexibility and efficiency in algorithm design. It uses multiple inheritance and template programming to decompose algorithms into essential policies and facilitate code reuse. With its clear separation between algorithm and execution policies, it provides researchers with a simple and powerful platform for prototyping ideas, evaluating them on different parallel computing architectures and hardware platforms, and generating compact and efficient production code. A C-API is included for customization and data loading in high-level languages. POLO enables users to move seamlessly from serial to multi-threaded shared-memory and multi-node distributed-memory executors. We demonstrate how POLO allows users to implement state-of-the-art asynchronous parallel optimization algorithms in just a few lines of code and report experiment results from shared and distributed-memory computing architectures. We provide both POLO and POLO.jl, a wrapper around POLO written in the Julia language, at this https URL under the permissive MIT license.
Q-map: a Convolutional Approach for Goal-Oriented Reinforcement Learning

Goal-oriented learning has become a core concept in reinforcement learning (RL), extending the reward signal as a sole way to define tasks. However, as parameterizing value functions with goals increases the learning complexity, efficiently reusing past experience to update estimates towards several goals at once becomes desirable but usually requires independent updates per goal. Considering that a significant number of RL environments can support spatial coordinates as goals, such as on-screen location of the character in ATARI or SNES games, we propose a novel goal-oriented agent called Q-map that utilizes an autoencoder-like neural network to predict the minimum number of steps towards each coordinate in a single forward pass. This architecture is similar to Horde with parameter sharing and allows the agent to discover correlations between visual patterns and navigation. For example learning how to use a ladder in a game could be transferred to other ladders later. We show how this network can be efficiently trained with a 3D variant of Q-learning to update the estimates towards all goals at once. While the Q-map agent could be used for a wide range of applications, we propose a novel exploration mechanism in place of ε-greedy that relies on goal selection at a desired distance followed by several steps taken towards it, allowing long and coherent exploratory steps in the environment. We demonstrate the accuracy and generalization qualities of the Q-map agent on a grid-world environment and then demonstrate the efficiency of the proposed exploration mechanism on the notoriously difficult Montezuma’s Revenge and Super Mario All-Stars games.
Improving problem identification via automated log clustering using dimensionality reduction

Background: Continuous engineering practices, such as continuous integration and continuous deployment, see increased adoption in modern software development. A frequently reported challenge for adopting these practices is the need to make sense of the large amounts of data that they generate. Goal: We consider the problem of automatically grouping logs of runs that failed for the same underlying reasons, so that they can be treated more effectively, and investigate the following questions: (1) Does an approach developed to identify problems in system logs generalize to identifying problems in continuous deployment logs? (2) How does dimensionality reduction affect the quality of automated log clustering? (3) How does the criterion used for merging clusters in the clustering algorithm affect clustering quality? Method: We replicate and extend earlier work on clustering system log files to assess its generalization to continuous deployment logs. We consider the optional inclusion of one of these dimensionality reduction techniques: Principal Component Analysis (PCA), Latent Semantic Indexing (LSI), and Non-negative Matrix Factorization (NMF). Moreover, we consider three alternative cluster merge criteria (Single Linkage, Average Linkage, and Weighted Linkage), in addition to the Complete Linkage criterion used in earlier work. We empirically evaluate the 16 resulting configurations on continuous deployment logs provided by our industrial collaborator. Results: Our study shows that (1) identifying problems in continuous deployment logs via clustering is feasible, (2) including NMF significantly improves overall accuracy and robustness, and (3) Complete Linkage performs best of all merge criteria analyzed. Conclusions: We conclude that problem identification via automated log clustering is improved by including dimensionality reduction, as it decreases the pipeline's sensitivity to parameter choice, thereby increasing its robustness for handling different inputs.
The Identification of the Factors Related to Household Food Insecurity among Indigenous People (Orang Asli) in Peninsular Malaysia under Traditional Food Systems

Over the course of 16 years, a high percentage of Orang Asli (OA) households in Malaysia has been found to be burdened with food insecurity. Therefore, a study was conducted to improve the understanding of the challenges faced by the OA in Peninsular Malaysia to achieve food security under traditional food systems. In this study, in-depth interview sessions, which were assisted by an interview protocol, were conducted with 61 OA women from nine villages that were selected purposefully across three states (Kelantan, Pahang, and Perak) in Peninsular Malaysia. Furthermore, thematic analysis was performed during data analysis. As a result, four themes were identified, namely (i) the failure in agriculture (sub-themes: threats from wild animals and insufficient land supply), (ii) ineffectiveness of traditional food-seeking activities (sub-themes: exhausting, tiring, dangerous, and time-consuming journey for food-seeking activities, depletion of natural commodities, reduced demands of natural commodities, and lack of equipment), (iii) weather (sub-themes: rainy and dry seasons), and (iv) water issues (subthemes: continuity of water supply and cleanliness of water). The identified modifiable factors of this issue should be incorporated into future schemes of food security intervention in order to efficiently manage the food shortage among the OA.
Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability

We introduce a new concept, data irrecoverability, and show that the well-studied concept of data privacy is sufficient but not necessary for data irrecoverability. We show that there are several regularized loss minimization problems that can use perturbed data with theoretical guarantees of generalization, i.e., loss consistency. Our results quantitatively connect the convergence rates of the learning problems to the impossibility for any adversary for recovering the original data from perturbed observations. In addition, we show several examples where the convergence rates with perturbed data only increase the convergence rates with original data within a constant factor related to the amount of perturbation, i.e., noise. A full version of this paper is accessible at: http://arxiv.org/pdf/1805.07645. pdf
Vertebral body stent augmentation to reconstruct the anterior column in neoplastic extreme osteolysis

Background Extensive lytic lesions of the vertebral body (VB) increase risk of fracture and instability and require stabilization of the anterior column. Vertebral augmentation is an accepted treatment option, but when osteolysis has extensively destroyed the VB cortical boundaries (a condition herein defined as ‘extreme osteolysis’), the risk of cement leakage and/or insufficient filling is high. Vertebral body stents (VBSs) might allow partial restoration of VB height, cement containment, and reinforcement, but their use in extreme osteolysis has not been investigated. Objective To assess retrospectively the feasibility and safety of VBS augmentation in patients with ‘extreme osteolysis’ of the VB. Methods We retrospectively analyzed 41 treated vertebrae (from T1 to L5). VB reconstruction was assessed on postprocedure CT images and rated on a qualitative 4-point scale (poor-fair-good-excellent). Clinical and radiological follow-up was performed at 1 month and thereafter at intervals in accordance with oncological protocols. Results VBS augmentation was performed at 12 lumbar and 29 thoracic levels, with bilateral VBS in 23/41. VB reconstruction was judged satisfactory (good or excellent) in 37/41 (90%) of levels. Bilateral VBS received higher scores than unilateral (p=0.057, Pearson’s X2). We observed no periprocedural complications. Cement leaks (epidural or foraminal) occurred at 5/41 levels (12.2%) without clinical consequences. Follow-up data were available for 27/29 patients, extending beyond 6 months for 20 patients (7–28 months, mean 15.3 months). VBS implant stability was observed in 40/41 cases (97.5%). Conclusions Our results support the use of VBS as a minimally invasive, safe and effective option for reconstructing the anterior column in prominent VB osteolysis.
Background field removal for susceptibility mapping of human brain with large susceptibility variations

In quantitative susceptibility mapping (QSM) of human brain, the background field induced by air‐tissue interface varies significantly with respect to the rotation angle between the head and the static field, which may result in substantial error in the estimated magnetic susceptibility values. The goal of this study was to develop a strategy to better remove such orientation dependent background field.
Metabolic analysis of the melatonin biosynthesis pathway using chemical labeling coupled with liquid chromatography‐mass spectrometry

Characterization of the melatonin (MLT) biosynthesis pathway in plants is still limited. Additionally, a metabolomic analysis of MLT biosynthesis in plants is still a challenge due to analyte structural and chemical diversity, low analyte abundances, and plant matrix complexities. Herein, a sensitive liquid chromatography‐mass spectrometry (LC‐MS) method enabling the simultaneous determination of seven plant MLT biosynthetic metabolites was developed. In the proposed strategy, the targeted metabolites, which included tryptophan (Trp), tryptamine (TAM), 5‐hydroxytryptophan (5HTP), serotonin (5HT), N‐acetylserotonin (NAS), 5‐methoxytryptamine (5MT), and MLT, were purified from plant extracts using a one‐step dispersive solid‐phase extraction (DSPE). The samples were then chemically labeled with dansyl chloride (DNS‐Cl), followed by analysis using LC‐MS. The limit of detection (LOD) values ranged from 0.03 to 1.36 pg/mL and presented a 22‐ to 469‐fold decrease when compared to the unlabeled metabolites. Due to the high sensitivity of the proposed method, the consumption of plant materials was reduced to 10 mg FW. Ultimately, the established method was utilized to examine the distributions of MLT and its intermediates in rice shoots and roots with or without cadmium (Cd) stress. The results suggested that under normal condition, MLT may also be generated via a Trp/TAM/5HT/5MT/MLT path (Pathway II) in addition to the previously reported Trp/TAM/5HT/NAS/MLT path (Pathway I), although Pathway I was shown to be dominant. During Cd stress, MLT was also shown to be produced through these two pathways, with Pathway II shown to be dominant in rice shoots and roots.