Miracle-dz/UniFAR-FTUs huggingface dataset:
UvA-DARE ( Digital Academic Repository ) Connective tissue growth factor in renal development and injury

TGFβ1, -β2, and -β3 are involved in control of wound repair and development of fibrosis. Connective tissue growth factor expression is stimulated by all TGFβ isoforms and is abundant in glomerulosclerosis and other fibrotic disorders. CTGF is hypothesized to mediate profibrotic effects of TGFβ1 or to facilitate interaction of TGFβ1 with its receptor, but its interactions with TGFβ isoforms in nonpathological conditions are unexplored sofar. Tissue repair and remodeling may recapitulate gene transcription at play in organogenesis. To further delineate the relationship between CTGF and TGFβ, we compared expression patterns of CTGF and TGFβ isoforms in rat and human glomerulogenesis, and in various human glomerulopathies. CTGF mRNA was present in the immediate precursors of glomerular visceral and parietal epithelial cells in the commaand S-shaped stages, but not in earlier stages of nephron development. During the capillary loop and maturing glomerular stages and simultaneous with the presence of TGFβ1, -β2 and -β3 protein, CTGF mRNA expression was maximal and present only in differentiating glomerular epithelial cells. CTGF protein was also present on precursors of mesangium and glomerular endothelium, suggesting possible paracrine interaction. Concomitant with the presence of TGFβ2 and -β3 protein, and in absence of TGFβ1, CTGF mRNA and protein expression was restricted to podocytes in normal adult glomeruli. However, TGFβ1 and CTGF were again coexpressed, often with TGFβ2 and -β3, in particular in podocytes in proliferative glomerulonephritis and also in mesangial cells in diabetic nephropathy and IgA nephropathy. Coordinated expression of TGFβ isoforms and of CTGF may be involved in normal glomerulogenesis and possibly in maintenance of glomerular structure and function at adult age. Prolonged overexpression of TGFβ1 and CTGF is associated with development of severe glomerulonephritis and
INDICES: Exploiting Edge Resources for Performance-Aware Cloud-Hosted Services

Despite the known benefits of hosting cloud-based services, the longer and often unpredictable end-to-end network latencies between the end user and the cloud can be detrimental to the response time requirements of the interactive cloud-hosted applications. Existing efforts that exploit edge/fog technology to migrate services closer to clients in order to improve response times do not fully resolve this problem as they do not focus on performance and interference issues at the migrated locations. This paper proposes INDICES framework that addresses these limitations by providing a novel solution that determines when and to which MDC a service should be migrated to and thus provides the desired performance. Empirical results validating our claims are presented using a setup comprising a centralized cloud and MDCs composed of heterogeneous hardware.
BOOTSTRAPPING LANGUAGE ACQUISITION 2 1 . 1 Theoretical underpinnings Our model falls under the general umbrella of “ Semantic Bootstrapping ”

The semantic bootstrapping hypothesis proposes that children acquire their native language through exposure to sentences of the language paired with structured representations of their meaning, whose component substructures can be associated with words and syntactic structures used to express these concepts. The child’s task is then to learn a language-specific grammar and lexicon based on (probably contextually ambiguous, possibly somewhat noisy) pairs of sentences and their meaning representations (logical forms). Starting from these assumptions, we develop a Bayesian probabilistic account of semantically bootstrapped first-language acquisition in the child, based on techniques from computational parsing and interpretation of unrestricted text. Our learner jointly models (a) word learning: the mapping between components of the given sentential meaning and lexical words (or phrases) of the language, and (b) syntax learning: the projection of lexical elements onto sentences by universal construction-free syntactic rules. Using an incremental learning algorithm, we apply the model to a dataset of real syntactically complex child-directed utterances and (pseudo) logical forms, the latter including contextually plausible but irrelevant distractors. Taking the Eve section of the CHILDES corpus as input, the model simulates several well-documented phenomena from the developmental literature. In particular, the model exhibits syntactic bootstrapping effects (in which previously learned constructions facilitate the learning of novel words), sudden jumps in learning without explicit parameter setting, acceleration of word-learning (the “vocabulary spurt”), an initial bias favoring the learning of nouns over verbs, and one-shot learning of words and their meanings. The learner thus demonstrates how statistical learning over structured representations can provide a unified account for these seemingly disparate phenomena.
Time-keeping with Limited Clocks

We study the problem of measuring time given strongly limited resources using Markov chains interpreted as clocks. More precisely, we assume the existence of an accurate short-scale time tick and are interested how to measure larger time scales with informationally limited memory. We use Information Theory to uncover novel behaviour of clocks with a small state space. Introduction Most living organisms have a biological clock (Bloch et al., 2013) called the circadian rhythm. In cyanobacteria, the circadian clock is only made up of three proteins and is so simple that it has been reconstituted in vitro (Nakajima et al., 2005). Inspired by this minimalistic clock, we build small machines that give an agent information about time. An obvious mechanism for the agent’s clock would be a switch that flipped back and forth at each tick. This would be able to provide one full bit of information about current time; however, this is a purely local characterization, as it distinguishes only odd and even times. We are studying to which extent more global time information can be achieved with a limited clock. Because of this, our approach uses probabilistic machines and we study their effectiveness using Information Theory. Previous Work The Information Bottleneck Method (Tishby et al., 1999) is a method to filter information by relevance. Time measuring dynamics can emerge as side effect of other information processing in minimal agents (Klyubin et al., 2007). Investigations One of the machines which we study in most of our experiments is inspired by physical decay processes whereby excited particles have a probability to transition to a ground state. This machine, which is illustrated in Fig. 1 can be fully described by its state, either up or down, and by its decay parameter, α, which is the probability of transitioning from the up state to the down state. up start down 1− α
Microstructure Noise and the Dynamics of Volatility

To investigate the dynamic interrelationship between microstructure noise and price volatility, we devise a model under which the observed stock price is decomposed into a permanent component representing the efficient price and a transient microstructure noise. Both components are allowed to have timevarying volatilities through a GARCH-type specification, and the innovations in the two components can be non-Gaussian and will feed back to the volatility process. Bayesian techniques are used to estimate the resulting nonlinear and non-Gaussian state-space system. In particular, we develop a hierarchical sequential Monte Carlo method that samples from the posterior distribution of the fixed model parameters and that of the unobserved state variables. On the lower level and for a fixed set of model parameters, we run particle filters to obtain a point-wise estimate of the likelihood, which in essence integrates out the unobserved state variables in the likelihood. On the upper level, we obtain the posterior distribution using a sequential Monte Carlo sampler over the fixed parameters with the point-wise likelihood estimates. We show that the method works well with a finite number of particles. The heavy computational load is handled by a GPU-based parallel computer. Implementing the method on 100 firms from CRSP, we find that microstructure shocks have an important role in the volatility dynamics.
Iterative Hessian Sketch with Momentum

We propose a novel randomized linear least squares solver which is an improvement of Iterative Hessian Sketch and randomized preconditioning. In the proposed Momentum-IHS technique (M-IHS), Heavy Ball Method is used to accelerate the convergence of iterations. It is shown that for any full rank data matrix, rate of convergence depends on the ratio between the feature size and the sketch size. Unlike the Conjugate Gradient technique, the rate of convergence is unaffected by either the condition number or the eigenvalue spectrum of the data matrix. As demonstrated over many examples, the proposed M-IHS provides compatible performance with the state of the art randomized preconditioning methods such as LSRN or Blendenpik and yet, it provides a completely different perspective in the area of iterative solvers which can pave the way for future developments.
LMS Performance Issues: A Case Study of D2L Sourish Roy

This paper presents a network traﬃc measurement study of Desire2Learn (D2L), which is the Learning Management System (LMS) used by the University of Calgary. Our study is motivated by anecdotal reports of sluggish D2L performance, particularly for ﬁle uploads. Using active and passive network measurements, we identify the root causes of the poor D2L performance. The main issues identiﬁed are: (1) excessive HTTP redirections in our university’s D2L setup; (2) non-negligible network latency to the server hosting the D2L content; and (3) suboptimal TCP settings that limit end-to-end throughput. We discuss these issues, and identify potential solutions, including caching. Our discrete-event simulation results indicate that a simple Web proxy cache at our university could reduce D2L requests by 50-70%.
CLASSIFICATION PROBLEMS WITH REWARD-MODULATED INFERENCE

Denoising autoencoders are a kind of neural net which can be used for both supervised learning and unsupervised learning: for supervised learning, you train them to maximise the likelihood of the data they’re trying to predict, and for unsupervised learning you train them to competently compress and decompress their input. The combination of these two aims of the neural net is called reward modulation. This project investigated neural nets and reinforcement learning in general, and RMI using denoising autoencoders for a classification problem in particular.
Multiview Playback with Ghost in Required Condition

Modern society has recently had problems with a variety of crimes. Surveillance cameras have been installed in various locations to protect assets from these crimes. When the incident occurred, it is needed to manually find out suspicious people in a large number of footage. Manually extracting prowlers is inefficient in which a lot of surveillance cameras have been installed and have taken a large number of people. Many methods to extract prowler have been proposed. One of the methods, it must give people special devices in advance. The other methods, it does not work without around people in order to extract suspicious behavior compared to around people. To solve the problems, we proposed a method acquiring data of bony framework from the footage using distance sensor to extract suspicious behavior depending on state of each joint. Therefore, it can practically detect suspicious person than those and does not need to give special device. In experimental result, distance errors when mounting distance sensors and cost of mounting are described.
SmartEvacTrak: A people counting and coarse-level localization solution for efficient evacuation of large buildings

Efficient evacuation of large buildings which seat more than twenty thousand people is a major challenge for building management authorities. Although many enterprise buildings have access control to count those entering various areas, such controls need to be switched off at times of emergency to avoid people getting trapped at a particular location. Hence, counting people during evacuation remains largely manual and error prone. Also, last known location based on access control from business management systems can be erroneous due to issues like tail gating. Accurate people counters like Radio Frequency Identification (RFID) often prove costly in large deployments. In this paper we present “SmartEvacTrak”, a low-cost evacuation system which can count people entering and exiting with over 98% accuracy and can also localize people at a coarse level with around 97% accuracy, from results obtained from an evacuation experiment performed on a set of 350+ people randomly exiting a building.
Automatic language identification for seven Indian languages using higher level features

This paper proposes an approach for automatic language identification (LID) for seven Indian languages. The proposed system uses language dependent phonotactic features and prosodic information. Phonetic Engine (PE)which serves as the front end of the phonotactic based LID system converts input speech utterance to a sequence of phonetic symbols. Syllable boundaries are detected and phones within a syllable boundary are grouped and phono-tactic rules are applied to get syllables. Two consecutive syllables are numerically represented to get phonotactic feature vectors. Prosodic feature vectors are obtained by concatenating features of three consecutive syllables. A multilayer feed forward neural network (NN) classifier is used at the back-end for language identification. The ANN classifier is trained with two hour duration data from each of the seven languages. Target languages include Bengali, Hindi, Telugu, Urdu, Assamese, Punjabi and Manipuri.
Forest Structure and Tree Species Diversity along an Altitudinal Gradient in

A study on forest vegetation along an altitudinal gradient was conducted in Doi Inthanon National Park, Chiangmai, Thailand. The purpose of the study was to elucidate how community characteristics change from lowland to mountain vegetation in the tropical monsoon climatic zone in mainland Southeast Asia by using floristic composition and species abundance data collected from forty five plots at different altitudes and forest types. Community classification by cluster analysis suggested 45 sample stands to be classified floristically into three forest zones along an altitudinal gradient: (1) lowland forest (400-850 m as! ) composed of 2 forest groups: (a) deciduous dipterocarp forest group dominated by Shorea siamensis, Canarium subulatum, and Shorea obtusa and (b) mixed deciduous forest group dominated by Teetana grandis, Xylia xylocarpa, Lagerstroemia calyculata, and Millettia leueantha. (2) Transition forest (850-1,400 m asl), in the intermediate zone between the lowland and montane zones composed of 2 forest groups: (a) pine-dipterocarp forest and pine-oak forest groups dominated by Pinus kesiya, Dipterocarpus tuberculatus, Aporusa villosa, Wendlandia tinctoria, Schima wallichii, and Helicia nilagiriea and (b) lower montane forest group dominated by Schima wallichii, Castanopsis /erox, Castanopsis tribuloides, and Helicia nilagirica. And, (3) montane forest (1,400-2,500 m as]), in the uppermost zone composed of 2 forest groups of both montane forest dominated by (a) Mastixia euonymoides, Castanopsis calathi/ormis, and Drypetes indica and (b) Neolitsea pallens, Actinodaphne henryi, and Rapanea yunnanensis. Tree density and basal area increases with rising altitude. Diversity of trees sharply increases from the lowland zone to an altitude of 1,800 m asl and gradually decreases at an altitude above 1,800 m asl shown by low species richness indices at high altitudes. In contrast, evenness indices were not greatly different along the altitudinal gradient.
Investigation of Semantic System Defects in Adult Aphasic Patients Following Stroke

* Corresponding Author: Mehdi Alizade Zarei, PhD Address: Department of Occupational Therapy, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran. Tel: +98 (21) 22228051 E-mail: alizade.m@iums.ac. ir Introduction: Semantic system plays a key role in all areas of language including understanding and expression of language. Based on a traditional view, the left hemisphere is dominant for processing of various linguistic information, including semantic information. It is believed that lesions in the left hemisphere impair the semantic component of language. In this study, we aim to study different types of semantic impairment in patients with aphasia; so that with early diagnosis of these sematic impairment we can identify patients who need treatment.
Merging and validating heterogenous, multi-layered corpora with discoursegraphs

We present discoursegraphs, a library and command-line application for the conversion and merging of linguistic annotations written in Python. The software reads and writes numerous formats for syntactic and discourse-related annotations, but also supports generic interchange formats. discoursegraphs models primary data and its annotations as a graph and is therefore able to merge multiple independent, possibly conflicting annotation layers into a unified representation. We show how this approach is beneficial for the revision and validation of a corpus with multiple conflicting, independently annotated layers.
The palmitoyl acyltransferase HIP 14 shares a high proportion of interactors with huntingtin : implications for a role in the pathogenesis of Huntington ’ s disease

HIP14 is the most highly conserved of 23 human palmitoyl acyltransferases (PATs) that catalyze the posttranslational addition of palmitate to proteins, including huntingtin (HTT). HIP14 is dysfunctional in the presence of mutant HTT (mHTT), the causative gene for Huntington disease (HD), and we hypothesize that reduced palmitoylation of HTT and other HIP14 substrates contributes to the pathogenesis of the disease. Here we describe the yeast two-hybrid (Y2H) interactors of HIP14 in the first comprehensive study of interactors of a mammalian PAT. Unexpectedly, we discovered a highly significant overlap between HIP14 interactors and 370 published interactors of HTT, 4-fold greater than for control proteins (P 5 8 3 10). Nearly half of the 36 shared interactors are already implicated in HD, supporting a direct link between HIP14 and the disease. The HIP14 Y2H interaction set is significantly enriched for palmitoylated proteins that are candidate substrates. We confirmed that three of them, GPM6A, and the Sprouty domain-containing proteins SPRED1 and SPRED3, are indeed palmitoylated by HIP14; the first enzyme known to palmitoylate these proteins. These novel substrates functions might be affected by reduced palmitoylation in HD. We also show that the vesicular cargo adapter optineurin, an established HTT-binding protein, co-immunoprecipitates with HIP14 but is not palmitoylated. mHTT leads to mislocalization of optineurin and aberrant cargo trafficking. Therefore, it is possible that optineurin regulates trafficking of HIP14 to its substrates. Taken together, our data raise the possibility that defective palmitoylation by HIP14 might be an important mechanism that contributes to the pathogenesis of HD.
The Role of Renewable Energy, Public Health Expenditure, Logistics and Environmental Performance in Economic Growth: An Evidence from Structural Equation Modelling

We do this research to investigate the relationship between renewable energy, public health expenditure, logistics performance indices, and economic and environmental sustainability in the ASEAN member states, through the analysis of a panel data from 2007 to 2017. The study used secondary data, which is downloaded from the World Bank Website and employs SEM (Structural Equation Modelling) model for testing hypothesis. The results show that the usage of renewable energy in logistical operations would enhance the environmental and economic performance in terms of mitigating carbon emissions and greenhouse gas emissions. While, public health expenditure and environmental performance is negatively correlated, which confirms that greater environmental sustainability with lower carbon emissions and greenhouse gases will improve human health and economic growth. Moreover, greater public health expenditure and poor environmental performance has negative effect on economic growth, inefficiency and low productivity of labor slowdown to the economic activities. For another side, the usage of renewable energy and the adoption of green practices in international logistics will develop the environmental sustainability, establish better image of a country and attract foreign direct investment inflows, and also minimize carbon emissions and public health expenditure, spurring sustainable economic growth with better export opportunities in pro-environmental countries.
An Initial Study on Density-Aware Cache Management for GPU

Due to the long latency of off-chip global memory, memory access coalescing in GPUs plays a critical role in retaining the high computation power by merging per-thread memory requests from the same warp into a minimum number of cacheline-sized memory transactions. However, the effectiveness of memory coalescing is greatly affected by access patterns, due to which memory transactions are often collectively issued by different amount of threads. Our experiments reveal that GPU memory traffic is often a mix of densely and sparsely coalesced memory transactions, and the cache blocks in sparse accesses often exhibit a higher potential of reusing. Because conventional cache management policies lack the capability of differentiating dense and sparse cache accesses, their universal service to each access often fails to bridge the gap between massive parallelism and long-latency memory accesses. In this paper, we propose a block-reuse degree based cache management algorithm to exploit spatial and temporal locality among massive GPU memory traffic. This report documents the initial results from our new GPU cache management algorithm.
SpiderEyes: designing attention- and proximity-aware collaborative interfaces for wall-sized displays

With the proliferation of large multi-faceted datasets, a critical question is how to design collaborative environments, in which this data can be analysed in an efficient and insightful manner. Exploiting people's movements and distance to the data display and to collaborators, proxemic interactions can potentially support such scenarios in a fluid and seamless way, supporting both tightly coupled collaboration as well as parallel explorations. In this paper we introduce the concept of collaborative proxemics: enabling groups of people to collaboratively use attention- and proximity-aware applications. To help designers create such applications we have developed SpiderEyes: a system and toolkit for designing attention- and proximity-aware collaborative interfaces for wall-sized displays. SpiderEyes is based on low-cost technology and allows accurate markerless attention-aware tracking of multiple people interacting in front of a display in real-time. We discuss how this toolkit can be applied to design attention- and proximity-aware collaborative scenarios around large wall-sized displays, and how the information visualisation pipeline can be extended to incorporate proxemic interactions.