Miracle-dz/UniFAR-FTUs huggingface dataset:
Behavioral management in children with intellectual disabilities in a resource-poor setting in Barwani, India

Background: Management of behavioral problems in children with intellectual disabilities (ID) is a great concern in resource-poor areas in India. This study attempted to analyze the efficacy of behavioral intervention provided in resource-poor settings. Objective: This study was aimed to examine the outcome of behavioral management provided to children with ID in a poor rural region in India. Materials and Methods: We analyzed data from 104 children between 3 and 18 years old who received interventions for behavioral problems in a clinical or a community setting. The behavioral assessment scale for Indian children with mental retardation (BASIC-MR) was used to quantify the study subjects’ behavioral problems before and after we applied behavioral management techniques (baseline and post-intervention, respectively). The baseline and post-intervention scores were analyzed using the following statistical techniques: Wilcoxon matched-pairs signed-rank test for the efficacy of intervention; χ2 for group differences. Results: The study demonstrated behavioral improvements across all behavior domains (P < 0.05). Levels of improvement varied for children with different severities of ID (P = 0.001), between children who did and did not have multiple disabilities (P = 0.011). Conclusion: The outcome of this behavioral management study suggests that behavioral intervention can be effectively provided to children with ID in poor areas.
Edinburgh Research Explorer Provenance management in curated databases

Curated databases in bioinformatics and other disciplines are the result of a great deal of manual annotation, correction and transfer of data from other sources. Provenance information concerning the creation, attribution, or version history of such data is crucial for assessing its integrity and scientiﬁc value. General purpose data-base systems provide little support for tracking provenance, especially when data moves among databases. This paper investigates general-purpose techniques for recording provenance for data that is copied among databases. We describe an approach in which we track the user’s actions while browsing source databases and copying data into a curated database, in order to record the user’s actions in a convenient, queryable form. We present an implementation of this technique and use it to evaluate the feasibility of database support for provenance management. Our experiments show that although the overhead of a na¨ıve approach is fairly high, it can be decreased to an acceptable level using simple optimizations.
Demonstration of Link Traversal SPARQL ery Processing over the Decentralized Solid Environment

To tackle economic and societal problems originating from the centralization of Knowledge Graphs on the Web, there has been an increasing interest towards decentralizing Knowledge Graphs across a large number of small authoritative sources. In order to eﬀectively build user-facing applications, there is a need for eﬃ‐ cient query engines that abstract the complexities around access‐ ing such massively Decentralized Knowledge Graphs (DKGs). As such, we have designed and implemented novel Link Traversal ery Processing algorithms into the Comunica query engine framework that are capable of eﬃciently evaluating SPARQL queries across DKGs provided by the Solid decentralization initia‐ tive. In this article, we demonstrate this query engine through a Web-based interface over which SPARQL queries can be executed over simulated and real-world Solid environments. Our demon‐ stration shows the beneﬁts of a traversal-based approach towards querying DKGs, and uncovers opportunities for further optimiza‐ tions in future work in terms of both query execution and discov‐ ery algorithms.
Hierarchies of Pertubation Theories as Post-Hartree-Fock and Post-Kohn-Sham shemes and a Jacob’s Ladder for Pauli kinetic energy density functional approximations

A hierarchy and comparison of pertubation theories (PT) as Møller-Plesset (MP) pertubation theory, pertubation theory along the adiabatic-connection (AC) such as Görling-Levy pertubation theory (GLPT) and the Pertubation theory along the Pauli contribution by Redeker is presented with a classification as post-Hartree-Fock (HF) and post-Kohn-Sham (KS) method relative to their fundamental framework. Lastly a Jacob’s Ladder is given for approximating the Pauli kinetic energy density functional (KEDF).
Regional climate trends and topographic influence over the Western Ghat catchments of India

This study investigates the role of elevation stratification and climate change on the hydrology of Western Ghat catchments during the period from 1951 to 2013 using gridded data. The trend analysis of rainfall and temperature was conducted using the Mann–Kendall trend test, and the hydrological modelling of the rivers was conducted using the Soil and Water Assessment Tool (SWAT) model. To characterize the spatial distribution of rainfall and streamflow based on elevation stratification, contemporary rainfall zones were delineated and the response of each zone was evaluated. The results indicated that the maximum rainfall occurs at certain distance on the windward side from the crest of the Western Ghats. On the leeward side (eastern plateau), the rainfall is maximum at crest (Western Ghats) and decreases with distance. The rivers in the southern portion of the Western Ghats of India were highly vulnerable to changing climate followed by the central portion. The annual and monsoon rainfall in the southern river decreased at 0.43 and 0.30% decade−1 (1% significance level), respectively. The summer rainfall in the river of the central portion (Netravathi River) decreased at 0.44% decade−1. The annual air temperature of the southern river catchment (Vamanapuram) increased at the rate of 0.12 °C decade−1 (at 0.1% significance level), and the air temperature of the central rivers increased at the rate of 0.09, 0.08, and 0.07 °C (0.1% significance level), respectively. The streamflow response of the southern and central rivers was discernible as the monsoon flow decreased at 37% decade−1 (0.1% significance level) in the southern river and 10% decade−1 (5% significance level) in the central river. Interestingly, the pristine Aghanashini River demonstrated resilience to climate change with an increase in annual rainfall and streamflow at 115 mm decade−1 (5% significance level) and 0.71 Mm3 decade−1 (0.1% significance level), respectively.
A Non-local Low-Rank Framework for Ultrasound Speckle Reduction

Speckle refers to the granular patterns that occur in ultrasound images due to wave interference. Speckle removal can greatly improve the visibility of the underlying structures in an ultrasound image and enhance subsequent post processing. We present a novel framework for speckle removal based on low-rank non-local filtering. Our approach works by first computing a guidance image that assists in the selection of candidate patches for non-local filtering in the face of significant speckles. The candidate patches are further refined using a low-rank minimization estimated using a truncated weighted nuclear norm (TWNN) and structured sparsity. We show that the proposed filtering framework produces results that outperform state-of-the-art methods both qualitatively and quantitatively. This framework also provides better segmentation results when used for pre-processing ultrasound images.
Quadrotor Flight Simulation in a CFD-generated Urban Wind Field

This paper presents a software pipeline that enables simulating a quadrotor’s ﬂight in realistic urban wind ﬁelds where complex wind phenomena are common and have signiﬁcant impact on vehicle dynamics. The pipeline integrates the OpenStreetMap database for obtaining real-world building geometry, the OpenFOAM computational ﬂuid dynamics (CFD) solver for computing a three-dimensional, steady, wind ﬁeld, the Gazebo robotics simulation environment, and the PX4 software-in-the-loop autopilot. A 3D wind plugin is developed to interpolate a pre-computed CFD wind ﬁeld at runtime during the simulation. The approach is demonstrated by comparing the ﬂight performance of a quadrotor ﬂying over a university campus environment with and without the wind ﬁeld.
Accelerating Neural Recommendation Training with Embedding Scheduling

Deep learning recommendation models (DLRM) are extensively adopted to support many online services. Typical DLRM training frameworks adopt the parameter server (PS) in CPU servers to maintain memory-intensive embedding tables, and leverage GPU workers with embedding cache to accelerate compute-intensive neural network computation and enable fast embedding lookups. However, such distributed systems suffer from significant communication overhead caused by the embedding transmissions between workers and PS. Prior work reduces the number of cache embedding transmissions by compromising model accuracy, including oversam-pling hot embeddings or applying staleness-tolerant updates. This paper reveals that many of such transmissions can be avoided given the predictability and infrequency natures of in-cache embedding accesses in distributed training. Based on this observation, we explore a new direction to accelerate distributed DLRM training without compromising model accuracy, i.e., embedding scheduling—with the core idea of proactively determining "where embeddings should be trained" and "which embeddings should be synchronized" to increase the cache hit rate and decrease unnecessary updates, thus achieving a low communication overhead. To realize this idea, we design Herald, a real-time embedding scheduler consisting of two main components: an adaptive location-aware inputs allocator to determine where embeddings should be trained and an optimal communication plan generator to determine which embeddings should be synchronized. Our experiments with real-world workloads show that Herald reduces 48%- 89% embedding transmissions, leading up to 2 . 11 × and up to 1 . 61 × better performance with TCP and RDMA, respectively, over 100 Gbps Ethernet for end-to-end DLRM training.
Constraint and Satisﬁability Reasoning for Graph Coloring

Graph coloring is an important problem in combinatorial optimization and a major component of numerous allocation and scheduling problems. In this paper we introduce a hybrid CP/SAT approach to graph coloring based on the addition-contraction recurrence of Zykov. Decisions correspond to either adding an edge between two non-adjacent vertices or contracting these two vertices, hence enforcing inequality or equality, respectively. This scheme yields a symmetry-free tree and makes learnt clauses stronger by not committing to a particular color. We introduce a new lower bound for this problem based on Mycielskian graphs; a method to produce a clausal explanation of this bound for use in a CDCL algorithm; a branching heuristic emulating Br´elaz’ heuristic on the Zykov tree; and dedicated pruning techniques relying on marginal costs with respect to the bound and on reasoning about transitivity when unit propagating learnt clauses. The combination of these techniques in both a branch-and-bound and in a bottom-up search outperforms other SAT-based approaches and Dsatur on standard benchmarks both for ﬁnding upper bounds and for proving lower bounds.
Utilitarian Algorithm Conﬁguration

We present the ﬁrst nontrivial procedure for conﬁguring heuristic algorithms to maximize the utility provided to their end users while also offering theoretical guarantees about performance. Existing procedures seek conﬁgurations that minimize expected runtime. However, very recent theoretical work argues that expected runtime minimization fails to capture algorithm designers’ preferences. Here we show that the utilitarian objective also confers signiﬁcant algorithmic beneﬁts. Intuitively, this is because mean runtime is dominated by extremely long runs even when they are incredibly rare; indeed, even when an algorithm never gives rise to such long runs, conﬁguration procedures that provably minimize mean runtime must perform a huge number of experiments to demonstrate this fact. In contrast, utility is bounded and monotonically decreasing in runtime, allowing for meaningful empirical bounds on a conﬁguration’s performance. This paper builds on this idea to describe effective and theoretically sound conﬁguration procedures. We prove upper bounds on the runtime of these procedures that are similar to theoretical lower bounds, while also demonstrating their performance empirically.
Human Motion Aware Text-to-Video Generation with Explicit Camera Control

With the rise in expectations related to generative models, text-to-video (T2V) models are being actively studied. Existing text-to-video models have limitations such as in generating complex movements replicating human motions. These model often generate unintended human motions, and the scale of the subject is incorrect. To overcome these limitations and generate high-quality videos that depict human motion under plausible viewing angles, we propose a two stage framework in this study. In the first stage a text-driven human motion generation network generates three-dimensional (3D) human motion from input text prompts and then motion-to-skeleton projection module projects generated motions onto a two-dimensional (2D) skeleton. In the second stage, the projected skeletons are used to generate a video in which the movements of a subject are well-represented. We demonstrated that the proposed framework quantitatively and qualitatively outperforms the existing T2V models. Previously reported human motion generation models use texts only or texts and human skeletons. However, our framework only uses texts and outputs a video related to human motion. Moreover, our framework benefits from using skeleton as an additional condition in the text-to-human motion generation networks. To the best of our knowledge, our framework is the first of its kind that uses text-driven human motion generation networks to generate high-quality videos related to human motions.
Albanesi C., Zani B. & Cicognani E. (2012). Youth civic and political participation through the lens of gender: the Italian case. Human Affairs, 22(3), 360-374

Italy is one of the European countries with the highest levels of gender inequalities (World Economic Forum, 2011). The aims of this paper were to understand to what extent the well-documented gender gap in Italian adult society has an impact on both political and civic actions of younger generations, and whether the process of participation assumes specific features according to gender. 835 Italian participants (49.6% males; 50.4% female, aged from 16 to 26 years old; 20% under voting age) completed a self-administered questionnaire. Analyses confirmed general trends (voting at elections is the most frequently performed political action among Italian youth) and showed the existence of a small gender gap (a major orientation of young women toward civic action, while young men are more engaged in manifest political participation). The results confirmed that family cultural capital and normative support are significant predictors of political participation, in particular for girls and young women.
Validation of green supply chain performance measures in the automotive industry

This study was aimed at establishing the validity of the measures and metrics for automobile green supply chain performance measurement. The study involved statistical tests using 16 measures and 72 corresponding metrics. These statistical tests include exploratory factor analysis to investigate the construct validity of the measures and their metrics, a confirmatory factor analysis to test the model fitness and a multiple regression analysis to test the criterion validity of the measures. From the results obtained, the validity of the measures and their corresponding metrics has been established. The paper culminated with recommendations for further studies.
MIT Open Access Articles Learning a sparse codebook of facial and body microexpressions for emotion recognition

Obtaining a compact and discriminative representation of facial and body expressions is a difﬁcult problem in emotion recognition. Part of the difﬁculty is capturing microexpressions, i.e., short, invol-untary expressions that last for only a fraction of a second: at a micro-temporal scale, there are so many other subtle face and body movements that do not convey semantically meaningful information. We present a novel approach to this problem by exploiting the sparsity of the frequent micro-temporal motion patterns. Local space-time features are extracted over the face and body region for a very short time period, e.g., few milliseconds. A codebook of mi-croexpressions is learned from the data and used to encode the features in a sparse manner. This allows us to obtain a representation that captures the most salient motion patterns of the face and body at a micro-temporal scale. Experiments performed on the AVEC 2012 dataset show our approach achieving the best published performance on the expectation dimension based solely on visual features. We also report experimental results on audio-visual emotion recognition, comparing early and late data fusion techniques.
Grammar evolution and symbolic regression for astrometric centering of Hubble Space Telescope images

Symbolic regression, in general, and genetic models, in particular, are promising approaches to mathematical modeling in astrometry where it is not always clear which is the fittest analytic expression depending on the problem under consideration. Several attempts and increasing research efforts are being made in this direction mainly from the Genetic Programming (GP) viewpoint. Our proposal is, as far as we know, the first one to apply Grammatical Evolution (GE) in this domain. GE (and further GE extensions) aim to outperform GP limitations by incorporating formal languages tools to guarantee the correctness (both syntactic and semantic) of the generated expressions. The current contribution is a first proof to check the viability of GE on astrometric real datasets. Its success in finding adequate parameters for predefined families of functions in star centering (Gaussian and Moffat PSFs) with simple and naive GE experiments supports our hypothesis on taking advantage of the expressive power of GE to tackle astrometry scenarios of interest and hence greatly improve current astrometric software thanks to specific genetic approaches.
Dietary patterns are associated with cognitive function in the REasons for Geographic And Racial Differences in Stroke (REGARDS) cohort

Abstract Identifying factors that contribute to the preservation of cognitive function is imperative to maintaining quality of life in advanced years. Of modifiable risk factors, diet quality has emerged as a promising candidate to make an impact on cognition. The objective of this study was to evaluate associations between empirically derived dietary patterns and cognitive function. This study included 18 080 black and white participants aged 45 years and older from the REasons for Geographic And Racial Differences in Stroke (REGARDS) cohort. Principal component analysis on data from the Block98 FFQ yielded five dietary patterns: convenience, plant-based, sweets/fats, Southern, and alcohol/salads. Incident cognitive impairment was defined as shifting from intact cognitive status (score >4) at first assessment to impaired cognitive status (score ≤4) at latest assessment, measured by the Six-Item Screener. Learning, memory and executive function were evaluated with the Word List Learning, Word List Delayed Recall, and animal fluency assessments. In fully adjusted models, greater consumption of the alcohol/salads pattern was associated with lower odds of incident cognitive impairment (highest quintile (Q5) v. lowest quintile (Q1): OR 0·68; 95 % CI 0·56, 0·84; P for trend 0·0005). Greater consumption of the alcohol/salads pattern was associated with higher scores on all domain-specific assessments and greater consumption of the plant-based pattern was associated with higher scores in learning and memory. Greater consumption of the Southern pattern was associated with lower scores on each domain-specific assessment (all P < 0·05). In conclusion, dietary patterns including plant-based foods and alcohol intake were associated with higher cognitive scores, and a pattern including fried food and processed meat typical of a Southern diet was associated with lower scores.
Entity Type Recognition Using an Ensemble of Distributional Semantic Models to Enhance Query Understanding

We present an ensemble approach for categorizing search query entities in the recruitment domain. Understanding the types of entities expressed in a search query (Company, Skill, Job Title, etc.) enables more intelligent information retrieval based upon those entities compared to a traditional keyword-based search. Because search queries are typically very short, leveraging a traditional bag-of-words model to identify entity types would be inappropriate due to the lack of contextual information. Our approach instead combines clues from different sources of varying complexity in order to collect real-world knowledge about query entities. We employ distributional semantic representations of query entities through two models: 1) contextual vectors generated from encyclopedic corpora like Wikipedia, and 2) high dimensional word embedding vectors generated from millions of job postings using word2vec. Additionally, our approach utilizes both entity linguistic properties obtained from WordNet and ontological properties extracted from DBpedia. We evaluate our approach on a data set created at CareerBuilder, the largest job board in the US. The data set contains entities extracted from millions of job seekers/recruiters search queries, job postings, and resume documents. After constructing the distributional vectors of search entities, we use supervised machine learning to infer search entity types. Empirical results show that our approach outperforms the state-of-the-art word2vec distributional semantics model trained on Wikipedia. Moreover, we achieve micro-averaged F1 score of 97% using the proposed distributional representations ensemble.
Co-Evolution of Language and of the Language Acquisition Device

A new account of parameter setting during grammatical acquisition is presented in terms of Generalized Categorial Grammar embedded in a default inheritance hierarchy, providing a natural partial ordering on the setting of parameters. Experiments show that several expermentally effective learners can be defined in this framework. Evolutionary simulations suggest that a learner with default initial settings for parameters will emerge, provided that learning is memory limited and the environment of linguistic adaptation contains an appropriate language.
Obfuscation-Resilient Software Plagiarism Detection with JPlag

The rise of automated obfuscation techniques challenges the wide-spread assumption that evading a software plagiarism detector requires more effort than completing programming and modeling assignments in computer science education. This threatens plagiarism detectors without comprehensive obfuscation resilience and, ultimately, academic integrity. This paper summarizes recent enhancements of JPlag, a widely-used software plagiarism detector, enabling it to achieve broad resilience against automated obfuscation. The findings demonstrate that JPlag significantly outperforms the state-of-the-art in terms of obfuscation resilience.