Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic thermal management via architectural adaptation

Exponentially rising cooling/packaging costs due to high power density call for architectural and software-level thermal management. Dynamic thermal management (DTM) techniques continuously monitor the on-chip processor temperature. Appropriate mechanisms (e.g., dynamic voltage or frequency scaling (DVFS), clock gating, fetch gating, etc.) are engaged to lower the temperature if it exceeds a threshold. However, all these mechanisms incur significant performance penalty. We argue that runtime adaptation of micro-architectural parameters, such as instruction window size and issue width, is a more effective mechanism for DTM. If the architectural parameters can be tailored to track the available instruction-level parallelism of the program, the temperature is reduced with minimal performance degradation. Moreover, synergistically combining architectural adaptation with DVFS and fetch gating can achieve the best performance under thermal constraints. The key difficulty in using multiple mechanisms is to select the optimal configuration at runtime for time varying workloads. We present a novel software-level thermal management framework that searches through the configuration space at regular intervals to find the best performing design point that is thermally safe. The central components of our framework are (1) a neural-network based classifier that filters the thermally unsafe configurations, (2) a fast performance prediction model for any configuration, and (3) an efficient configuration space search algorithm. Experimental results indicate that our adaptive scheme achieves 59% reduction in performance overhead compared to DVFS and 39% reduction in overhead compared to DVFS combined with fetch gating.
Ieee Transactions on Evolutionary Computation Femoea: a Fast and Efficient Multi-objective Evolutionary Algorithm

— A new multi-objective evolutionary algorithm which can be applied to many nonlinear multi-objective optimization problems is proposed. Its aim is to obtain a fixed size discrete set approximating the complete Pareto-front quickly. It adapts ideas from different multi-and single-objective optimization evolutionary algorithms, although it also incorporates new devices, namely, a new method to improve the efficiency of points (no gradient information is used) and a new stopping rule, which help to improve the quality of the obtained approximation of the Pareto-front and to reduce the computational requirements, respectively. In order to analyze its performace, it has been compared with the reference algorithms NSGA-II and SPEA2 on a set of twenty benchmark problems. Several quality indicators have been considered, namely, hypervolume, average distance, additive epsilon indicator, spread and spacing. According to the computational results and statistical analysis performed, the new algoritm, named FEMOEA, outperforms, in average, both NSGA-II and SPEA2 for all the quality indicators.
To Max or Not to Max: Online Learning for Speeding Up Optimal Planning

It is well known that there cannot be a single "best" heuristic for optimal planning in general. One way of overcoming this is by combining admissible heuristics (e.g. by using their maximum), which requires computing numerous heuristic estimates at each state. However, there is a tradeoff between the time spent on computing these heuristic estimates for each state, and the time saved by reducing the number of expanded states. We present a novel method that reduces the cost of combining admissible heuristics for optimal search, while maintaining its benefits. Based on an idealized search space model, we formulate a decision rule for choosing the best heuristic to compute at each state. We then present an active online learning approach for that decision rule, and employ the learned model to decide which heuristic to compute at each state. We evaluate this technique empirically, and show that it substantially outperforms each of the individual heuristics that were used, as well as their regular maximum.
Poverty and Internalizing Symptoms: The Indirect Effect of Middle Childhood Poverty on Internalizing Symptoms via an Emotional Response Inhibition Pathway

Childhood poverty is a pervasive problem that can alter mental health outcomes. Children from impoverished circumstances are more likely than their middle-income counterparts to develop internalizing problems such as depression and anxiety. To date, however, the emotional-cognitive control processes that link childhood poverty and internalizing symptoms remain largely unexplored. Using the Emotion Go/NoGo paradigm, we examined the association between poverty and emotional response inhibition in middle childhood. We further examined the role of emotional response inhibition in the link between middle childhood poverty and internalizing symptoms. Lower income was associated with emotional response inhibition difficulties (indexed by greater false alarm rates in the context of task irrelevant angry and sad faces). Furthermore, emotional response inhibition deficits in the context of angry and sad distracters were further associated with child-report internalizing problems. The results of the current study demonstrate the significance of understanding the emotional-cognitive control vulnerabilities of children raised in poverty and their association with mental health outcomes.
Follicular lymphoma international prognostic index 2: a new prognostic index for follicular lymphoma developed by the international follicular lymphoma prognostic factor project.

PURPOSE The aim of the F2 study was to verify whether a prospective collection of data would enable the development of a more accurate prognostic index for follicular lymphoma (FL) by using parameters which could not be retrospectively studied before, and by choosing progression-free survival (PFS) as principal end point. PATIENTS AND METHODS Between January 2003 and May 2005, 1,093 patients with a newly diagnosed FL were registered and 942 individuals receiving antilymphoma therapy were selected as the study population. The variables we used for score definition were selected by means of bootstrap resampling procedures on 832 patients with complete data. Procedures to select the model that would minimize errors were also performed. RESULTS After a median follow-up of 38 months, 261 events for PFS evaluation were recorded. beta2-microglobulin higher than the upper limit of normal, longest diameter of the largest involved node longer than 6 cm, bone marrow involvement, hemoglobin level lower than 12 g/dL, and age older than 60 years were factors independently predictive for PFS. Using these variables, a prognostic model was devised to identify three groups at different levels of risk. The 3-year PFS rate was 91%, 69%, and 51% for patients at low, intermediate, and high risk, respectively (log-rank = 64.6; P < .00001). The 3-year survival rate was 99%, 96%, and 84% for patients at low, intermediate, and high risk, respectively (P < .0001). CONCLUSION Follicular Lymphoma International Prognostic Index 2 is a simple prognostic index based on easily available clinical data and may represent a promising new tool for the identification of patients with FL at different risk in the era of immunochemotherapy.
Direct Access to the Order Parameter: Parameterized Symmetry Modes and Rigid Body Movements as a Function of Temperature

The first order phase transition of CsFeO2 was investigated using synchrotron powder diffraction data as a function of temperature. Two alternative approaches were used to describe the deviation of the framework crystal structure relative to the high-symmetry parent structure: symmetry (a.k.a. distortion) modes and polyhedral-tilt parameters. In both cases, the relevant parameters were refined as a function of temperature using the method of parametric Rietveld refinement. We demonstrate a semi-automated and generally applicable method for the determination of spontaneous lattice strain variations, order parameters and power-law exponents as derived from Landau theory.
Expression of Aspergillus hemoglobin domain activities in Aspergillus oryzae grown on solid substrates improves growth rate and enzyme production

DNA fragments coding for hemoglobin domains (HBD) were isolated from Aspergillus oryzae and Aspergillus niger. The HBD activities were expressed in A. oryzae by introduction of HBD gene fragments under the control of the promoter of the constitutively expressed gpdA gene. In the transformants, oxygen uptake was significantly higher, and during growth on solid substrates the developed biomass was at least 1.3 times higher than that of the untransformed wild‐type strain. Growth rate of the HBD‐activity‐producing strains was also significantly higher compared to the wild type. During growth on solid cereal substrates, the amylase and protease activities in the extracts of the HBD‐activity‐producing strains were 30–150% higher and glucoamylase activities were at least 9 times higher compared to the wild‐type strain. These results suggest that the Aspergillus HBD‐encoding gene can be used in a self‐cloning strategy to improve biomass yield and protein production of Aspergillus species.
The Drosophila Helicase MLE Targets Hairpin Structures in Genomic Transcripts

RNA hairpins are a common type of secondary structures that play a role in every aspect of RNA biochemistry including RNA editing, mRNA stability, localization and translation of transcripts, and in the activation of the RNA interference (RNAi) and microRNA (miRNA) pathways. Participation in these functions often requires restructuring the RNA molecules by the association of single-strand (ss) RNA-binding proteins or by the action of helicases. The Drosophila MLE helicase has long been identified as a member of the MSL complex responsible for dosage compensation. The complex includes one of two long non-coding RNAs and MLE was shown to remodel the roX RNA hairpin structures in order to initiate assembly of the complex. Here we report that this function of MLE may apply to the hairpins present in the primary RNA transcripts that generate the small molecules responsible for RNA interference. Using stocks from the Transgenic RNAi Project and the Vienna Drosophila Research Center, we show that MLE specifically targets hairpin RNAs at their site of transcription. The association of MLE at these sites is independent of sequence and chromosome location. We use two functional assays to test the biological relevance of this association and determine that MLE participates in the RNAi pathway.
Support vector machines based on K-means clustering for real-time business intelligence systems

Support vector machines (SVM) have been applied to build classifiers, which can help users make well-informed business decisions. Despite their high generalisation accuracy, the response time of SVM classifiers is still a concern when applied into real-time business intelligence systems, such as stock market surveillance and network intrusion detection. This paper speeds up the response of SVM classifiers by reducing the number of support vectors. This is done by the K-means SVM (KMSVM) algorithm proposed in this paper. The KMSVM algorithm combines the K-means clustering technique with SVM and requires one more input parameter to be determined: the number of clusters. The criterion and strategy to determine the input parameters in the KMSVM algorithm are given in this paper. Experiments compare the KMSVM algorithm with SVM on real-world databases, and the results show that the KMSVM algorithm can speed up the response time of classifiers by both reducing support vectors and maintaining a similar testing accuracy to SVM.
Efficient motion estimation utilizing quadrature filters

This contribution introduces a computationally-efficient scheme for phase-based motion estimation. The local phase for consecutive dyadic scales and six different directions is retrieved through a complex-valued subband decomposition. It is obtained by a successive use of a recursive Hilbert transformer and recursive power-complementary half-band filter pairs. The so-called approximately linear-phase recursive half-band filter proposed by Renfors and Saramäki is used as a start-up filter for generating both the Hilbert transformer and the half-band filter pairs. Experiments with synthetic image sequences demonstrate that by properly designing the start-up filter, the proposed technique provides, with a considerably reduced number of computations, a performance similar to that in a recently introduced method.
The shaping of genetic variation in edge-of-range populations under past and future climate change

With rates of climate change exceeding the rate at which many species are able to shift their range or adapt, it is important to understand how future changes are likely to affect biodiversity at all levels of organisation. Understanding past responses and extent of niche conservatism in climatic tolerance can help predict future consequences. We use an integrated approach to determine the genetic consequences of past and future climate changes on a bat species, Plecotus austriacus. Glacial refugia predicted by palaeo-modelling match those identified from analyses of extant genetic diversity and model-based inference of demographic history. Former refugial populations currently contain disproportionately high genetic diversity, but niche conservatism, shifts in suitable areas and barriers to migration mean that these hotspots of genetic diversity are under threat from future climate change. Evidence of population decline despite recent northward migration highlights the need to conserve leading-edge populations for spearheading future range shifts.
An Adaptive Hysteresis Band Controller for Single Phase PV Inverters

Hysteresis current controllers are widely used in PV inverters. The main drawback of conventional hysteresis current control is that the switching frequency is variable, as the hysteresis band is fixed. In this paper an adaptive band hysteresis control algorithm with no derivative calculation is presented. As it will be shown, this algorithm can limit the switching frequency variation within a small range and the algorithm itself is simple and easy to be implemented. The performance of this algorithm, together with the one [10] and conventional hysteresis control has been evaluated by means of simulations performed with PSIM and SIMULINK. Simulation results show the effectiveness of the suggested hysteresis current controller.
Ultrashort pulse temporal contrast enhancement based on noncollinear optical-parametric amplification.

A pulse clean technique in time domain combining noncollinear optical-parametric amplification and second-harmonic generation is demonstrated. The measurement-limited >10(10) temporal contrast ratio of the 0.5 mJ/40 fs pulse near 800 nm is obtained over a large temporal range extending from <1 ps before the main pulse. The contrast ratio enhancement as fourth power of the initial contrast is demonstrated. The total efficiency is >6% for the 8.2 mJ/40 fs initial incident femtosecond pulse.
Passwords for Everyone: Secure Mnemonic-based Accessible Authentication

In many environments, a computer system is severely constrained to the extent that the practical input mechanisms are merely binary switches. Requiring the user to remember a long random bit string and to authenticate by entering each bit in the available binary input mechanism, is completely impractical. This paper deals with the question of authentication in such environments where the inputs are constrained to be yes/no responses to statements displayed on the user's screen. We present PassWit, a mnemonic-based system for such environments that combines good usability with high security, and has many additional features such as (to mention a few) resistance to phishing, keystroke-logging, and compatibility with currently deployed systems and password file formats (hence it can co-exist with existing login mechanisms).
Immunohistochemical identification of arteriolar development using markers of smooth muscle differentiation. Evidence that capillary arterialization proceeds from terminal arterioles.

Arteriolar growth is an important event in the adaptation of normal tissues as well as in important pathologies, but the site of origin of new arterioles remains unknown. The network pattern of arteriolar development in skeletal muscle was detected by use of a new immunohistochemical technique that is based on the observation that fully differentiated (mature) vascular smooth muscle (SM) cells express both SM alpha-actin and the two myosin heavy chains (MHCs) SM-1 and SM-2, whereas less differentiated (immature) vascular SM cells do not express MHC. The anterior gracilis muscle microvasculatures of 4- and 9-week-old Sprague-Dawley rats were labeled with monoclonal antibodies to SM alpha-actin and to SM MHC. Whole transverse arteriole networks were observed, and terminal arterioles, defined as terminal segments labeled with SM alpha-actin, were classified on the basis of the presence or absence of SM MHC. A significantly different percentage of terminal arteriolar endings per network without SM MHC was observed in the two groups (66.1 +/- 17.3% for 4 weeks and 27.1 +/- 18.5% for 9 weeks), suggesting that arteriolar development is more nearly complete in the older animals. Sparsely distributed capillaries exhibited thin extensions of SM alpha-actin that crossed collecting venules and joined similar extensions from an adjacent transverse arteriole, effectively forming the basis for new arcade arterioles. SM alpha-actin and SM MHC labeling in terminal arterioles was always continuous with upstream arterioles. (ABSTRACT TRUNCATED AT 250 WORDS)
MULTI-VIEW DOCUMENT CLUSTERING WITH DIFFERENT SIMILARITY MEASUREMENTS VIA ENSEMBLE

Clustering ensembles are a common approach to clustering problem, which combine a collection of clustering into a superior solution. The key issues are how to generate different candidate solutions and how to combine them. Common approach for generating candidate clustering solutions ignores the multiple representations of the data and the standard approach of simply selecting the best solution from candidate clustering solutions ignores the fact that there may be a set of clusters from different candidate clustering solutions which can form a better clustering solution.Multi view clustering can be applied at various stages of the clustering paradigm. This paper proposes a novel multi-view clustering algorithm that combines different ensemble techniques via various similarity metrics have been used to measure the similarity between data objects. Cluster based similarity matrix is used for ensemble clustering by the use of a consensus function. This consensus function is based on the theory of graph partitions. Normalized Discounted Cumulative Gain (NDCG) which is a family of ranking measures widely used in practice. Kullback–Leibler divergence (KLD) is a non-symmetric measure of the difference between two probability distributions P and Q of two distance values between data object.Based on these similarity matrices on the individual datasets and aggregates these to form a combined similarity matrix, which is then used to obtain the final clustering. A proposed ensemble method is compared with existing algorithms on three data sets that have increasing difficulty. The results show that our method significantly outperforms other methods.
Swiss EMBnet node web server

EMBnet is a consortium of collaborating bioinformatics groups located mainly within Europe (http://www.embnet.org). Each member country is represented by a 'node', a group responsible for the maintenance of local services for their users (e.g. education, training, software, database distribution, technical support, helpdesk). Among these services a web portal with links and access to locally developed and maintained software is essential and different for each node. Our web portal targets biomedical scientists in Switzerland and elsewhere, offering them access to a collection of important sequence analysis tools mirrored from other sites or developed locally. We describe here the Swiss EMBnet node web site (http://www.ch.embnet.org), which presents a number of original services not available anywhere else.
Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research

Summary Objectives We examine recent published research on the extraction of information from textual documents in the Electronic Health Record (EHR). Methods Literature review of the research published after 1995, based on PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers already included. Results 174 publications were selected and are discussed in this review in terms of methods used, pre-processing of textual documents, contextual features detection and analysis, extraction of information in general, extraction of codes and of information for decision-support and enrichment of the EHR, information extraction for surveillance, research, automated terminology management, and data mining, and de-identification of clinical text. Conclusions Performance of information extraction systems with clinical text has improved since the last systematic review in 1995, but they are still rarely applied outside of the laboratory they have been developed in. Competitive challenges for information extraction from clinical text, along with the availability of annotated clinical text corpora, and further improvements in system performance are important factors to stimulate advances in this field and to increase the acceptance and usage of these systems in concrete clinical and biomedical research contexts.
DIASUMM: Flexible Summarization of Spontaneous Dialogues in Unrestricted Domains

In this paper, we present a summarization system for spontaneous dialogues which consists of a novel multi-stage architecture. It is specifically aimed at addressing issues related to the nature of the texts being spoken vs. written and being dialogical vs. monological. The system is embedded in a graphical user interface and was developed and tested on transcripts of recorded telephone conversations in English and Spanish (CALLHOME).
Iterative Row Sampling

There has been significant interest and progress recently in algorithms that solve regression problems involving tall and thin matrices in input sparsity time. Given a n * d matrix where n &ge; d, these algorithms find an approximation with fewer rows, allowing one to solve a poly(d) sized problem instead. In practice, the best performances are often obtained by invoking these routines in an iterative fashion. We show these iterative methods can be adapted to give theoretical guarantees comparable to and better than the current state of the art. Our approaches are based on computing the importances of the rows, known as leverage scores, in an iterative manner. We show that alternating between computing a short matrix estimate and finding more accurate approximate leverage scores leads to a series of geometrically smaller instances. This gives an algorithm whose runtime is input sparsity plus an overhead comparable to the cost of solving a regression problem on the smaller approximation. Our results build upon the close connection between randomized matrix algorithms, iterative methods, and graph sparsification.