Miracle-dz/UniFAR-FTUs huggingface dataset:
Workload-Aware Power Gating Design and Run-Time Management for Massively Parallel GPGPUs

Power gating (PG) is an effective power efficiency improvement technique. Future general-purpose graphics processing units (GPGPUs) will likely feature hundreds of compute units (CUs) and be power constrained, which leads to serious challenges to existing PG methodologies. In this paper, we propose novel design-time and run-time techniques to effectively implement power gating in future GPGPUs. Based on industrial models/measurement facilities, we show that designers must consider run-time parallelism within potential applications while implementing power gating designs to avoid incurring unnecessary design overheads. By scaling measurements from a real 28nm GPGPU to a hypothetical future 10nm node, we show that a PG granularity of 16 CU/cluster achieves 99% peak run-time performance without the excessive 53% design-time area overhead of per-CU PG. We also demonstrate that a run-time power management algorithm that is aware of the PG granularity leads to up to 18% additional performance through frequency-boosting under thermal-design power (TDP) constraints.
Emergence of Linguistic Features: Independent Component Analysis of Contexts

Independent component analysis (ICA) (Hyvarinen et al., 2001) applied on word context data gives distinct features which reflect syntactic and semantic categories. The analysis gives features or categories that are both explicit and can easily be interpreted by humans. This result can be obtained without any human supervision or tagged corpora that would have some predetermined morphological, syntactic or semantic information. The results include both an emergence of clear distinctive categories or features and a distributed representation. This is based on the fact that a word may belong to several categories simultaneously in a graded manner. Within cognitive linguistics we wish that our model can provide additional understanding on potential cognitive mechanisms in natural language learning and understanding. Our approach is based on the assumption that much of the linguistic knowledge is emergent in nature and based on specific learning mechanisms. In the full paper, we will provide a brief introduction to independent component analysis, review the earlier published results (Honkela and Hyvarinen, 2004), provide the very latest results, compare our model with some other approaches based on unsupervised learning paradigm and discuss in general the potential of the ICA model within the theme of modelling language, cognition and action.
SODA: the stack overflow dataset almanac

Stack Overflow has become a fundamental resource for developers, becoming the de facto Question and Answer (Q&A) website, and one of the standard unstructured data sources for software engineering research to mine knowledge about development. We present SODA, the Stack Overflow Dataset Almanac, a tool that helps researchers and developers to better understand the trends of discussion topics in Stack Overflow, based on the available tagging system. SODA provides an effective visualization to support the analysis of topics in different time intervals and frames, leveraging single or co-occurrent tags. We show, through simple usage scenarios, how SODA can be used to find interesting peculiar moments in the evolution of Stack Overflow discussions that closely match specific recent events in the area of software development. SODA is available at http://rio.inf. usi.ch/soda/.
Elevated survivin expression in peripheral blood mononuclear cells is central to collateral formation in coronary chronic total occlusion

Survivin is essential to angiogenesis and revascularization, but its role in coronary collateral formation remains unclear. The role of survivin in peripheral blood mononuclear cells (PBMCs) of coronary chronic total occlusion (CTO) patients was investigated. Coronary CTO patients (n=46; mean age 60.1±8.5, male 54.3%) (CTO group) and normal control patients (n=18; mean age 58.0±10.0, male 55.6%) underwent angiographic collateral vessel grading by Rentrop classification (C0 – C3) and provided peripheral blood between June 2006 and February 2007. Rat hind limb ischemia models were constructed using four equal groups of Sprague-Dawley rats (n=36): normal control, sham operation, operation and granulocyte macrophage colony-stimulating factor (GM-CSF). PBMC numbers and characteristics, collateral vessels, survivin, CD4, CD8, CD44, vascular endothelial growth factor (VEGF) and intercellular adhesion molecule-1 (ICAM-1) expression were determined using RT-PCR, flow cytometry, immunocytochemistry and western blot analysis. PBMC survivin mRNA and protein expression levels were higher in patients with good collateral circulation (C2 + C3) than in patients with no collateral flow (C0) (all P<0.05). Survivin single-positive and survivin and CD8, VEGF and ICAM-1 double-positive percentages were elevated in patients with good collateral circulation compared to those with normal and no collateral flow (all P<0.05), consistent with the rat model results, wherein higher survivin levels produced significantly larger and more visible collateral vessels. In conclusion, elevated survivin expression in PBMCs, particularly survivin and CD8, VEGF, and ICAM-1 double-positive PBMCs, may be crucial for good collateral formation in patients with coronary CTO, as confirmed by assessment of a rat model.
Automatic Anomaly Detection Using Unlabelled Log Data

In this paper, we embark on a meticulous investigation of anomaly detection within OpenStack system logs, employing a two-pronged approach that ingeniously combines a task splitting method for log grouping with a two-layer stacked Long Short-Term Memory (LSTM) network for the anomaly detection phase. Our exploration commences with the extraction of typical normal operation patterns from system logs, followed by the application of our LSTM-based anomaly detection model trained on these normal patterns. Our study reveals the remarkable potential of this combined methodology in effectively distinguishing between normal and abnormal tasks, thereby offering profound insights into system operation and potential areas of concern. This exploration stands as a proof to the immense potential of modern machine learning techniques in navigating the ever-increasing complexity and volume of log data.
Supporting management reporting: a writable web case study

The World-Wide Web was originally developed as a shared, writable, hypertext medium, a facility that is still widely needed.We have recently developed a Web-based management reporting system for a legal firm in an attempt to improve the efficiency and management of their overall business process. This paper shares our experiences in relating the firm's specific writing and issue tracking tasks to existing Web, open hypermedia, and Semantic Web research, and describes why we chose to develop a new solution --- a set of open hypermedia components collectively called the Management Reporting System --- rather than employ an existing system.
Experiences of People With Disabilities In Faith Communities: A Journey

This article portrays the findings of research on the experiences of inclusion/exclusion of people with disabilities in faith communities. This journey was undertaken with a group of people with disabilities in Pietermaritzburg, South Africa, during 2009 and 2010. In narrations of the journey, participants expressed their experiences of exclusion, of not being considered, and of their personhood not always respected in faith communities. However, the journey also revealed suggestions to how their inclusion can be enhanced by changing practices, developing understanding of the needs of people with disabilities, and raising awareness. The article will conclude with recommendations for ways that people with disabilities and others in faith communities can journey together in faith communities to the benefit of all.
Reading Feynman Into Nanotechnology: A Text for a New Science

As histories of nanotechnology are created, one question arises repeatedly: how influential was Richard Feynman‟s 1959 talk, “There‟s Plenty of Room at the Bottom”? It is often said by knowledgeable people that this talk was the origin of nanotech. It preceded events like the invention of the scanning tunneling microscope, but did it inspire scientists to do things they would not have done otherwise? Did Feynman‟s paper directly influence important scientific developments in nanotechnology? Or is his paper being retroactively read into the history of nanotechnology? To explore those questions, I trace the history of “Plenty of Room,” including its publication and republication, its record of citations in scientific literature, and the comments of eight luminaries of nanotechnology. This biography of a text and its life among other texts enables us to articulate Feynman‟s paper with the history of nanotechnology in new ways as it explores how Feynman‟s paper is read.
Homosynaptic Long-Term Synaptic Potentiation of the “Winner” Climbing Fiber Synapse in Developing Purkinje Cells

During the developmental formation of neuronal circuits, redundant synapses are eliminated and persisting synapses strengthened. In the immature cerebellum, climbing fiber–Purkinje cell synapses undergo a pronounced synaptic rewiring, from a multiple innervation around birth to a mono-innervation in adults. An early stage of this process consists in the differentiation of initially equally strong synapses into one “large” and several “small” synaptic inputs. By performing whole-cell recordings in Purkinje cells of rat cerebellar slices, we found that the coincident activation of a Purkinje cell and one of its afferent climbing fibers induces homosynaptic long-term synaptic potentiation (LTP). This LTP requires postsynaptic Ca2+ signaling and involves an increase in the single channel conductance of the postsynaptic AMPA receptors. Interestingly, LTP occurs exclusively at large synaptic inputs. It is not observed at small inputs that are eventually eliminated. Thus, we identified a new form of LTP that is expressed uniquely and just for a restricted period of early development in the large climbing fiber inputs. Our results suggest that this LTP mediates the activity-dependent maturation of the “winner” climbing fiber.
The impact of self-construals and perceived social norms on social anxiety in young adults: a cross-cultural comparison

Feelings of intense anxiety associated with social situations are a universal phenomenon. However, the expression of a social anxiety reaction and the situation in which it is elicited may differ across cultures. The main aims of the present study were to compare the frequency of social anxiety symptoms among young adults in Hong Kong and in the UK and to examine the association between social anxiety, self-construals and perceived social norms. A total of 886 young adults (451 in the UK and 435 in Hong Kong) between the ages of 18 and 26 years were investigated. They completed a set of questionnaires, which were used to measure social anxiety, self-construals and perceived social norms. Young adults in Hong Kong reported significantly higher levels of social anxiety symptoms than young adults in the UK. Participants in Hong Kong scored significantly higher than those in the UK in interdependent self-construal, whereas the Hong Kong group scored lower in independent self-construal. In both countries, independent self-construal was significantly negatively correlated with social anxiety. Cultural norms were associated with social anxiety only in the Hong Kong sample. These findings highlight the importance of cultural factors on individuals' experiences of social anxiety.
Topological and Domain Knowledge-based Subgraph Mining: Application on Protein 3D-Structures. (Fouille de Sous-graphes Basée sur la Topologie et la Connaissance du Domaine: Application sur les Structures 3D de Protéines)

This thesis is in the intersection of two proliferating research fields, namely data mining and bioinformatics. With the emergence of graph data in the last few years, many efforts have been devoted to mining frequent subgraphs from graph databases. Yet, the number of discovered frequentsubgraphs is usually exponential, mainly because of the combinatorial nature of graphs. Many frequent subgraphs are irrelevant because they are redundant or just useless for the user. Besides, their high number may hinder and even makes further explorations unfeasible. Redundancy in frequent subgraphs is mainly caused by structural and/or semantic similarities, since most discovered subgraphs differ slightly in structure and may infer similar or even identical meanings. In this thesis, we propose two approaches for selecting representative subgraphs among frequent ones in order to remove redundancy. Each of the proposed approaches addresses a specific type of redundancy. The first approach focuses on semantic redundancy where similarity between subgraphs is measured based on the similarity between their nodes' labels, using prior domain knowledge. The second approach focuses on structural redundancy where subgraphs are represented by a set of user-defined topological descriptors, and similarity between subgraphs is measured based on the distance between their corresponding topological descriptions. The main application data of this thesis are protein 3D-structures. This choice is based on biological and computational reasons. From a biological perspective, proteins play crucial roles in almost every biological process. They are responsible of a variety of physiological functions. From a computational perspective, we are interested in mining complex data. Proteins are a perfect example of such data as they are made of complex structures composed of interconnected amino acids which themselves are composed of interconnected atoms. Large amounts of protein structures are currently available in online databases, in computer analyzable formats. Protein 3D-structures can be transformed into graphs where amino acids are the graph nodes and their connections are the graph edges. This enables using graph mining techniques to study them. The biological importance of proteins, their complexity, and their availability in computer analyzable formats made them a perfect application data for this thesis.
On Classi cation and Regression

We address the problem of computing various types of expressive tests for decision tress and regression trees. Using expressive tests is promising, because it may improve the prediction accuracy of trees. The drawback is that computing an optimal test could be costly. We present a uni ed framework to approach this problem, and we revisit the design of e cient algorithms for computing important special cases. We also prove that it is intractable to compute an optimal conjunction or disjunction.
Proanthocyanidins Inhibit Photocarcinogenesis through Enhancement of DNA Repair and Xeroderma Pigmentosum Group A–Dependent Mechanism

Dietary grape seed proanthocyanidins (GSP) inhibit photocarcinogenesis in mice; however, the molecular mechanisms underlying this effect have not been fully elucidated. As ultraviolet B (UVB)-induced DNA damage in the form of cyclobutane pyrimidine dimers (CPDs) has been implicated in skin cancer risk, we studied whether dietary GSPs enhance repair of UVB-induced DNA damage and, if so, what is the potential mechanism? Supplementation of GSPs (0.5%, w/w) with AIN76A control diet significantly reduced the levels of CPD+ cells in UVB-exposed mouse skin; however, GSPs did not significantly reduce UVB-induced CPD+ cells in the skin of interleukin-12p40 (IL-12) knockout (KO) mice, suggesting that IL-12 is required for the repair of CPDs by GSPs. Using IL-12 KO mice and their wild-type counterparts and standard photocarcinogenesis protocol, we found that supplementation of control diet with GSPs (0.5%, w/w) significantly reduced UVB-induced skin tumor development in wild-type mice, which was associated with the elevated mRNA levels of nucleotide excision repair genes, such as XPA, XPC, DDB2, and RPA1; however, this effect of GSPs was less pronounced in IL-12 KO mice. Cytostaining analysis revealed that GSPs repaired UV-induced CPD+ cells in xeroderma pigmentosum complementation group A (XPA)-proficient fibroblasts from a healthy individual but did not repair in XPA-deficient fibroblasts from XPA patients. Furthermore, GSPs enhance nuclear translocation of XPA and enhanced its interactions with other DNA repair protein ERCC1. Together, our findings reveal that prevention of photocarcinogenesis by GSPs is mediated through enhanced DNA repair in epidermal cells by IL-12- and XPA-dependent mechanisms.Cancer Prev Res; 3(12); 1621–9. ©2010 AACR.
Imaging mass spectrometry based exploration of biochemical tissue composition using peak intensity weighted PCA

Imaging mass spectrometry or mass spectral imaging (MSI) is a technology that provides us with the opportunity to study the spatial distribution of biomolecules such as proteins, peptides, and metabolites throughout organic tissue sections. MSI adds a spatial dimension to mass spectrometry and biomarker-oriented studies without the requirement for labels, as is the case with more traditional techniques such as fluorescense microscopy. It has particular merit for studies where no prior hypothesis of target molecules is available, as it can simultaneously track a wide range of molecules within its mass range. This makes MSI a potent exploratory tool for elucidating the spatiobiochemical topology in tissue. This paper elaborates on the principal component analysis (PCA)-based unsupervised decomposition of an MSI-measured organic tissue section into its underlying biochemical trends. We introduce a method to control the weight that particular peak intensity ranges are allowed to exert on the final decomposition model. The extension provides a way for peak intensity-based scaling to be incorporated directly into the decomposition process, for the purpose of denoising or contrast enhancement. The method makes use of peak height transformations that are conceptually equivalent to what is known in digital image processing as gray level transformations, but rather than aiming to enhance contrast for human interpretation they are used to influence the unsupervised decomposition process. As an example, we apply a combined denoising/contrast stretching measure to the MSI-measurement of a section of rat spinal cord.
Understanding the Value and Size Premia: What can We Learn from Stock Migrations?

The majority of the realized size and value premia reflect earnings-induced price surprises and thus cannot be part of the expected risk premia. The realized premia usually happen, as a price adjustment, to the firms that receive earnings shocks and migrate across size and value categories, and only during the migration periods. In addition, the systematic risks of the migrating firms completely change after the migration. The joint evidence suggests that the expected size and value risk premia are much smaller than their realized counterparts. Finally, the size premium has not disappeared: not considering small growth firms, the size premium is as robust as ever.
Using tourism free‐choice learning experiences to promote environmentally sustainable behaviour: the role of post‐visit ‘action resources’

This paper argues the need for the providers of ecotourism and other free‐choice environmental learning experiences to promote the adoption of environmentally sustainable actions beyond their own sites, when visitors return to their home environments. Previous research indicates that although visitors often leave such experiences with a heightened awareness of conservation issues and intentions to adopt environmentally responsible behaviours, only a minority translate these intentions into real actions. Building on research and theory in relation to visitor experiences in free‐choice learning environments, the paper identifies three different stages in the educational process and proposes a strategy for facilitating the translation of visitors' behavioural intentions into the adoption of sustainable actions through the provision of post‐visit action resources.
Use of “um” in the deceptive speech of a convicted murderer

ABSTRACT Previous studies have demonstrated a link between language behaviors and deception; however, questions remain about the role of specific linguistic cues, especially in real-life high-stakes lies. This study investigated use of the so-called filler, “um,” in externally verifiable truthful versus deceptive speech of a convicted murderer. The data revealed significantly fewer instances of “um” in deceptive speech. These results are in line with our recent study of “um” in laboratory elicited low-stakes lies. Rather than constituting a filled pause or speech disfluency, “um” may have a lexical status similar to other English words and may be under the strategic control of the speaker. In an attempt to successfully deceive, humans may alter their speech, perhaps in order to avoid certain language behaviors that they think might give them away.
Patch-Clamp Recordings from Cerebellar Basket Cell Bodies and Their Presynaptic Terminals Reveal an Asymmetric Distribution of Voltage-Gated Potassium Channels

Cerebellar basket cells form highly specialized inhibitory synaptic contacts with Purkinje cells, namely the pericellular basket and pinceau nerve terminal structures, wrapping around the Purkinje cell somatic and axon hillock regions. These inhibitory synaptic contacts are ideally located to control the ultimate output of the cerebellar cortex. Previous immunohistochemical studies have shown that these synaptic structures possess a very high density of the dendrotoxin (DTX)-sensitive potassium channel subunit, Kv1.2. We have taken advantage of this unique anatomical arrangement offering a high concentration of identified Kv channel subunits by combining whole-cell patch-clamp recording and fluorescence microscopy to establish a novel preparation and perform the first recordings from unambiguously identified mammalian CNS inhibitory presynaptic terminals. We report that DTX-sensitive potassium channels are present in basket cell terminals but not in the basket cell soma. This selective cellular distribution suggests that these channels play an important role in modulating cerebellar inhibitory synaptic transmission.
Publishing Linked Sensor Data

This paper describes a linked-data platform to publish sensor data and link them to existing resource on the semantic Web. The linked sensor data platform, called Sense2Web supports flexible and interoperable descriptions and provide association of different sensor data ontologies to resources described on the semantic Web and the Web of data. The current advancements in (wireless) sensor networks and being able to manufacture low cost and energy efficient hardware for sensors has lead to a potential interest in integrating physical world data into the Web. Wireless sensor networks employ various types of hardware and software components to observe and measure physical phenomena and make the obtained data available through different networking services. Applications and users are typically interested in querying various events and requesting measurement and observation data from physical world. Using a linked data approach enables data consumers to access sensor data and query the data and relations to obtain information and/or integrate data from various sources. Global access to sensor data can provides a wide range of applications in different domains such as geographical information systems, healthcare, smart homes, and business applications and scenarios. In this paper we focus on publishing linked-data to describe sensors and link them to other existing resources on the Web.