Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid therapy for treatment of newly diagnosed toddlers with autism spectrum disorders

Abstract Background: Time is of the essence in treating autism spectrum disorders (ASD). Intensive and effective therapies can advance acquisition of adaptive and communication skills during the lag time until entry into a pre-school program. Objective: To jump start treatment in very young toddlers with ASD and good cognitive potential, therapeutic modifications of the Early Start Denver Model (ESDM) were examined. Method: The changes involved more intensive cooperative treatments, with two different therapists (“hybrid”), reinforcing behavioral manipulations with the children. The intensified therapies were to maximize gains over shorter periods of time, dictated by limitations of resources. Results: Thirty children (mean age 24 m., average duration 4.1 m.) improved on their goal attainment scale (GAS) ratings on nearly all functional measures, (p<0.001). Of 23 ADOS scores, 19 improved, the average pre-treatment ADOS score was 17.7 (SD 3.4) and post-treatment 13.3 (SD 5.34). The correlation between GAS and ADOS score improvement was significant (R=0.67, p<0.001). Conclusion: The acceleration in social communication and adaptive skill gains in the treatment of very young children using modifications in treatment therapies already in wide- scale use, indicates the as-yet untapped treatment potential for many young children with ASD.
An Independent Investigation Into the Psychometric Properties of the Adult Scale of Hostility and Aggression (A-SHARP)

ABSTRACT Background: The Adult Scale of Hostility and Aggression (A-SHARP) rating scale assesses the frequency/severity (problem scale) and the reactive-proactive motivation (provocation scale) of aggressive behaviors in adults with intellectual disabilities (ID). Items are assigned to five subscales (Verbal Aggression, Physical Aggression, Hostile Affect, Covert Aggression, and Bullying). Although psychometric properties reported by the scale’s developers were very good, we wanted to corroborate them independently. We were also interested in whether the reactive-proactive distinction of aggressive behavior is related to a behavioral/functional classification. Method: Staff at a day-treatment program for adults with ID completed ratings for 155 clients using the A-SHARP, the Behavior Problems Inventory-01 (BPI-01), and the Questions about Behavioral Function (QABF). Results: Internal consistency was found to be excellent, and the A-SHARP Physical Aggression subscale had good congruent and clinical validity. Confirmatory factor analysis showed sufficient evidence toward the factorial validity of the A-SHARP’s problem scale. The reactive-proactive classification of aggressive behavior motivation by the A-SHARP’s provocation scale was independent of the functional classification. Conclusions: The A-SHARP is a useful addition to a small number of existing instruments for assessing aggressive behavior in adults with ID, especially its problem scale. We discuss how the A-SHARP provocation scales might provide therapy-relevant information. Empirical evidence for the clinical utility of the A-SHARP provocation scale will have to be established by future research.
A Low-Power Single-Bit Continuous-Time ΔΣ Converter with 92.5 dB Dynamic Range for Biomedical Applications

A third-order single-bit CT-ΔΣ modulator for generic biomedical applications is implemented in a 0.15 µm FDSOI CMOS process. The overall power efficiency is attained by employing a single-bit ΔΣ and a subthreshold FDSOI process. The loop-filter coefficients are determined using a systematic design centering approach by accounting for the integrator non-idealities. The single-bit CT-ΔΣ modulator consumes 110 µW power from a 1.5 V power supply when clocked at 6.144 MHz. The simulation results for the modulator exhibit a dynamic range of 94.4 dB and peak SNDR of 92.4 dB for 6 kHz signal bandwidth. The figure of merit (FoM) for the third-order, single-bit CT-ΔΣ modulator is 0.271 pJ/level.
Semidefinite relaxations for parallel machine scheduling

We consider the problem of scheduling unrelated parallel machines so as to minimize the total weighted completion time of jobs. Whereas the best previously known approximation algorithms for this problem are based on LP relaxations, we give a 3/2-approximation algorithm that relies on a convex quadratic programming relaxation. For the special case of two machines we present a further improvement to a 1.2752-approximation; we introduce a more sophisticated semidefinite programming relaxation and apply the random hyperplane technique introduced by M.X. Goemans and D.P. Williamson (1995) for the MAXCUT problem and its refined version of U. Feige and M.X. Goemans (1995). To the best of our knowledge, this is the first time that convex and semidefinite programming techniques (apart from LPs) are used in the area of scheduling.
Incremental maintenance of length normalized indexes for approximate string matching

Approximate string matching is a problem that has received a lot of attention recently. Existing work on information retrieval has concentrated on a variety of similarity measures TF/IDF, BM25, HMM, etc.) specifically tailored for document retrieval purposes. As new applications that depend on retrieving short strings are becoming popular(e.g., local search engines like YellowPages.com, Yahoo! Local, and Google Maps) new indexing methods are needed, tailored for short strings. For that purpose, a number of indexing techniques and related algorithms have been proposed based on length normalized similarity measures. A common denominator of indexes for length normalized measures is that maintaining the underlying structures in the presence of incremental updates is inefficient, mainly due to data dependent, precomputed weights associated with each distinct token and string. Incorporating updates usually is accomplished by rebuilding the indexes at regular time intervals. In this paper we present a framework that advocates lazy update propagation with the following key feature: Efficient, incremental updates that immediately reflect the new data in the indexes in a way that gives strict guarantees on the quality of subsequent query answers. More specifically, our techniques guarantee against false negatives and limit the number of false positives produced. We implement a fully working prototype and illustrate that the proposed ideas work really well in practice for real datasets.
Metric Learning with Multiple Kernels

Metric learning has become a very active research field. The most popular representative–Mahalanobis metric learning–can be seen as learning a linear transformation and then computing the Euclidean metric in the transformed space. Since a linear transformation might not always be appropriate for a given learning problem, kernelized versions of various metric learning algorithms exist. However, the problem then becomes finding the appropriate kernel function. Multiple kernel learning addresses this limitation by learning a linear combination of a number of predefined kernels; this approach can be also readily used in the context of multiple-source learning to fuse different data sources. Surprisingly, and despite the extensive work on multiple kernel learning for SVMs, there has been no work in the area of metric learning with multiple kernel learning. In this paper we fill this gap and present a general approach for metric learning with multiple kernel learning. Our approach can be instantiated with different metric learning algorithms provided that they satisfy some constraints. Experimental evidence suggests that our approach outperforms metric learning with an unweighted kernel combination and metric learning with cross-validation based kernel selection.
Suspiciously high correlations in brain imaging research

Edited by Scott O. Lilienfeld and Irwin D. Waldman. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc. In early 2005, a speaker visiting our department reported that blood‐oxygen‐level‐ dependent (BOLD) activity in a small region of the brain accounted for the great majority of the variance in speed with which subjects walk out of the experiment several hours later (this finding was never published, as far as we know). This result struck us as puzzling, to say the least. It made us wonder about various apparent implications – for example: Are walking speeds really so reliable that most of their variability can be predicted? Does a single, localized cortical region chiefly determine walking speeds? Are walking speeds largely predetermined hours in advance? These implications all struck us as far‐fetched. Browsing the literature, we were s urprised to find that similarly high correlations between fMRI data and individual differences in social behavior and other individual differences were not at all uncommon in the literature. And yet, when we tried to estimate the maximum plausible population correlation between fMRI measures and social behavior based on psychometric considerations, it seemed that the upper bound should be around 0.75 (given a generous estimate of the reliability of the behavioral and brain measures). And yet, correlations exceeding this upper bound were very common (Figure 11.1). Our efforts to figure out what was amiss to yield such high correlations led to a 2009 article – initially titled “Voodoo Correlations in Social Neuroscience” – which g enerated far more interest and controversy than we had even remotely anticipated. The source of these suspicious correlations turned out to be a fairly simple selection bias, namely, a selective analysis procedure that effectively reports the highest observed sample correlations chosen out of a very large set of candidates. The problem with such circular selective analyses – namely, that they provide grossly inflated estimates of effect size – were described as far back as 1950 by Edward Cureton in the context of practitioners using the same data to develop and validate Suspiciously High Correlations in Brain Imaging Research
Semantic Representation of Objects and Function

Natural human activities involve using and manipulating objects, often several at a time, simultaneously and continuously. For example, changing lanes while driving requires using a steering wheel and pedals, all while observing the road and making sure that lanes are clear. Not only are we recognizing these objects, but we know what to do with them (i.e., we can perceive object affordances). We use our imagination and invoke mental simulations to construct variations of objects and actions to learn these affordances. Learning how to use objects is a highly desirable skill for robots, as well. Unfortunately, although robots are proficient at recognizing object features, they are less-skilled at recognizing what can be done with these objects. In this paper, we sketch a novel approach based on Dempster-Shafer (DS) theory [1] and uncertain logic for inferring object affordances using a mental simulation framework. As part of this effort, we are in the process of developing a computational model for affordance that can represent complicated activities and can account for the dynamic and continuous nature of real-world scenarios.
DETECTING AND TRACKING HUMAN FACES IN COMPRESSED DOMAIN FOR CONTENT BASED VIDEO INDEXING

2002 i ACKNOWLEDGEMENT I have been at NUS for almost two years. Initially, I had several difficulties to deal with: getting familiar with a new school and a new country, means of doing my research here, etc. Fortunately, my family, friends and colleagues made things easy. I have to thank my advisor, Mohan S Kankanhalli for leading me to the area of multimedia, and computer vision research. He taught me not only the knowledge of science, but also how to do a good research. His focused attitude towards research and work influenced me always. Thanks to my " the second boss " Philippe Mulhem for his encouragement, valuable advice and his humor, our Friday meetings went easy. Thanks especially to R.K, my collaborator and coauthor in most of the work presented in Chapter 4, for teaching how to write more formal English and for introducing me to delicious Indian foods. Thanks to Hadi and Weiqi for constant advice and guidance. Thanks to Yunlong for his code which was my start point and for his valuable suggestions. Thanks for Lixin Fan for the conversations we had that give me good insights into the work in my hands. Thanks all friends and other group members: And finally, thanks to my parents, my brother and my girlfriend. Their constant love, encouragement and support never let me feel lonely. ii SUMMARY With recent advances in broadband networks, image/video compression standards (JPEG/MPEG) and consumer electronics (including amateur digital video cameras), video data now ranges from simple home videos to sophisticated movie productions. The huge amounts of video data generated everyday make it imperative to index the data in a way that enables fast content-based search and retrieval. This has resulted in active research into developing efficient video indexing techniques. Visual features resulting from recorded human activities are more likely to be of value for content description and indexing in the video sequences (news, movies, home video, and etc.), than anything else. So human faces often constitute the most important content in the video sequences. Therefore to be able to recognize faces and index them would be a crucial feature of any indexing system. As the first step to face identification, which is a part of the DIVA presented in this thesis aims to find human faces in video for the purpose of the content based indexing. In order to use all the information which video …
Auxiliary particle filter-based WLAN indoor tracking algorithm

The particle filter (PF) has been implemented for location tracking in Wireless Local Area Network (WLAN) based indoor positioning system. However, the traditional PF technology is based on the sampling importance resampling (SIR), which has the inherent blindness. Therefore, its tracking performance in WLAN indoor environment is degraded. The auxiliary particle filter (APF) can solve this problem very well by making use of the current observation information during the production of new particles, so this paper employs the auxiliary particle filter (APF) for location tracking in WLAN fingerprinting positioning system to improve the WLAN indoor tracking performance. In the simulation, the weighted k-nearest neighbors method (WKNN) is chosen as the fingerprinting positioning algorithm. Simulation results show that APF based tracking algorithm performs better than PF based tracking algorithm. The APF based WLAN indoor tracking algorithm decreases the mean tracking error by 7.7% and 26.9% than PF based tracking algorithm and WKNN algorithm respectively.
Sustained Interleukin-1β Exposure Modulates Multiple Steps in Glucocorticoid Receptor Signaling, Promoting Split-Resistance to the Transactivation of Prominent Anti-Inflammatory Genes by Glucocorticoids

Clinical treatment with glucocorticoids (GC) can be complicated by cytokine-induced glucocorticoid low-responsiveness (GC-resistance, GCR), a condition associated with a homogeneous reduction in the expression of GC-receptor- (GR-) driven anti-inflammatory genes. However, GR level and phosphorylation changes modify the expression of individual GR-responsive genes differently. As sustained IL-1β exposure is key in the pathogenesis of several major diseases with prevalent GCR, we examined GR signaling and the mRNA expression of six GR-driven genes in cells cultured in IL-1β and afterwards challenged with GC. After a GC challenge, sustained IL-1β exposure reduced the cytoplasmic GR level, GRSer203 and GRSer211 phosphorylation, and GR nuclear translocation and led to selective GCR in the expression of the studied genes. Compared to GC alone, in a broad range of GC doses plus sustained IL-1β, FKBP51 mRNA expression was reduced by 1/3, TTP by 2/3, and IRF8 was completely knocked down. In contrast, high GC doses did not change the expression of GILZ and DUSP1, while IGFBP1 was increased by 5-fold. These effects were cytokine-selective, IL-1β dose- and IL-1R1-dependent. The integrated gain and loss of gene functions in the “split GCR” model may provide target cells with a survival advantage by conferring resistance to apoptosis, chemotherapy, and GC.
Wave-Like Solutions of General One-Dimensional Spatially Coupled Systems

We establish the existence of wave-like solutions to spatially coupled graphical models which, in the large size limit, can be characterized by a one-dimensional real-valued state. This is extended to a proof of the threshold saturation phenomenon for all such models, which includes spatially coupled irregular LDPC codes over the BEC, but also addresses hard-decision decoding for transmission over general channels, the CDMA multiple-access problem, compressed sensing, and some statistical physics models. For traditional uncoupled iterative coding systems with two components and transmission over the BEC, the asymptotic convergence behavior is completely characterized by the EXIT curves of the components. More precisely, the system converges to the desired fixed point, which is the one corresponding to perfect decoding, if and only if the two EXIT functions describing the components do not cross. For spatially coupled systems whose state is one-dimensional a closely related graphical criterion applies. Now the curves are allowed to cross, but not by too much. More precisely, we show that the threshold saturation phenomenon is related to the positivity of the (signed) area enclosed by two EXIT-like functions associated to the component systems, a very intuitive and easy-to-use graphical characterization. In the spirit of EXIT functions and Gaussian approximations, we also show how to apply the technique to higher dimensional and even infinite-dimensional cases. In these scenarios the method is no longer rigorous, but it typically gives accurate predictions. To demonstrate this application, we discuss transmission over general channels using both the belief-propagation as well as the min-sum decoder.
Inducing Good Behavior : Bonuses versus Fines in Inspection Games by

We examine the effectiveness of bonuses and fines in an ‘inspection game’ where an employer can learn the effort of a worker through costly inspection. Standard game theoretic analysis predicts that fines discourage shirking, whereas bonuses encourage shirking. In contrast, ownpayoff effects suggest that both fines and bonuses discourage shirking. In an experiment we find that fines are more effective than bonuses in reducing shirking. However, we do not find that bonuses encourage shirking. Behavioral theories based on Impulse Balance Equilibrium or Quantal Response Equilibrium provide a good account of deviations from Nash equilibrium predictions.
Improving the representation of time structure in front-ends for automatic speech recognition

This paper describes investigations into the use of ‘excitation-synchronous’ spectral analysis to provide acoustic features for automatic speech recognition. Within each 10 ms frame the region of maximum power is located and used as the centre for the window in a subsequent Fourier transform. The method has been found to be effective in locating stop bursts and vocal-tract responses to glottal closures. This excitation-synchronous analysis has been compared with the more conventional fixed-interval analysis for window lengths ranging from 5 to 25 ms. In connected-digit recognition experiments using mel-cepstrum features, the excitation-synchronous analysis with a window length of 10 ms gave a 10% improvement in recognition performance when compared with the best of the fixed-window conditions.
Ranking Entities on the Web using Social Network Mining and Ranking Learning

Social networks have garnered much attention recently. Several studies have been undertaken to extract social networks among people, companies, and so on automatically from the web. For use in social sciences, social networks enable analyses of the performance and valuation of companies. This paper describes an attempt to learn ranking of entities from a social network that has been mined from the web. In our approach, we first extract different kinds of relational data from the web. We construct social networks using several relevance measures in addition to text analysis. Subsequently, the relations are integrated to maximize the ranking predictability. We also integrate several relations into a combined-relational network and use the latest ranking learning algorithm to obtain the ranking model. Additionally, we propose the use of centrality scores of companies on the network as features for ranking. We conducted two experiments on a social network among companies to learn the ranking of market capitalization, and on a social network among researchers for ranking of researchers’ productivity. This study specifically examines a new approach to using web information for advanced analysis by integrating multiple relations among named entities.
A Droplet Clustering and Residue Removal Technique for Cross-contamination Avoidance in Digital Microfluidic Biochip

Progressive research on design automation of digital microfluidic biochip is helping biochips to emerge as an effective alternative for several pathological experiments in the laboratories. This is often deployed for multiplexing several bioassays under optimized space and time constraints. We refer the droplets associated with bioassay of sample or reagents as serviceable droplets. During these assay operations the electrodes often endure multiple utilizations to provide a time optimized routing schedule. Here the residue left by one biomolecule may contaminate the serviceable droplets used in the subsequent assays. Minimization of such cross-contaminations under the strict adherence of time is an important aspect of research on design automation of digital microfluidic biochip. In this paper, a time optimized routing scheme has been proposed through clustering of routing areas for different serviceable droplets by area analysis. This method helps to reduce both intra and inter sub-problem cross-contamination. In this method, the droplet traces belonging to different subproblems are partitioned into some regions to produce a contamination minimized routing schedule. This technique effectively keeps track of cross-contamination factors between two successive sub-problems. However the method cannot totally eliminate the possibilities of cross-contamination among multiple sub-problems. Thus residue removal through rinsing of selective electrodes is still required where contamination can’t be avoided. Accordingly a time optimized residue removal operation has been proposed for intersecting electrodes through wash droplet scheduling. Experimental study of the proposed technique shows better result over some standard algorithms.
Adaptation of acoustic models for multilingual recognition

This paper evaluates the recognition performance of a system using acoustic models transformed across language boundaries. Parameters of hidden Markov models (HMMs) trained on speaker independent English data are adapted using Afrikaans adaptation data to realise speaker dependent, multispeaker and speaker independent Afrikaans models. Adaptation is performed using maximum a posteriori probability (MAP) and maximum likelihood linear regression (MLLR) methods on context independent and context dependent phones. Results show that MLLR transformation of English models using Afrikaans adaptation data signi(cid:12)cantly improves model performance and for context dependent models achieves better performance on speaker independent tests than achievable by direct training on the adaptation data.