Miracle-dz/UniFAR-FTUs huggingface dataset:
Entity-relationship queries over wikipedia

Wikipedia is the largest user-generated knowledge base. We propose a structured query mechanism, entity-relationship query, for searching entities in Wikipedia corpus by their properties and inter-relationships. An entity-relationship query consists of arbitrary number of predicates on desired entities. The semantics of each predicate is specified with keywords. Entity-relationship query searches entities directly over text rather than pre-extracted structured data stores. This characteristic brings two benefits: (1) Query semantics can be intuitively expressed by keywords; (2) It avoids information loss that happens during extraction. We present a ranking framework for general entity-relationship queries and a position-based Bounded Cumulative Model for accurate ranking of query answers. Experiments on INEX benchmark queries and our own crafted queries show the effectiveness and accuracy of our ranking method.
Image Manipulation Detection Using Sensor Linear Pattern

In this work, we introduce a new method for localizing image manipulations in a single digital image, such as identifying added, removed (spliced or in-painted), or deformed objects. The method utilizes the so-called Linear Pattern (LP) of digital images as a global template whose integrity can be assessed in a localized manner. The consistency of the linear pattern estimated from the image noise residual is evaluated in overlapping blocks of pixels. The manipulated region is identified by the lack of similarity in terms of the correlation coe cient computed between the power spectral density (PSD) of the LP in that region and the PSD averaged over the entire image. The method is potentially applicable to all images of su cient resolution as long as the LP in the unmodified parts of the image has di erent spectral properties from that in the tampered area. No side information, such as the EXIF header or the camera model, is needed to make the method work. Experiments show the capability and limitations of the proposed method, which is robust to mild JPEG compression.
What form of control function can drive a discontinuous-mode boost converter to chaos via period-doubling?

Period-doubling routes to chaos in dc/dc converters operating in discontinuous mode have been reported in Reference 1 for the boost-type converter, and in Reference 2 for the buck-type converter. The chaotic behaviour associated with such systems is due to the presence of the feedback controller since these systems are open-loop stable.3 Thus, the choice of the feedback control function is very important to the identi cation and control of chaos in such systems. Up to now, no rigorous attempts have been made to derive formally the condition leading to chaos in such systems. In this letter we establish condition on the form of control function that may give rise to period-doubling cascades in the boost regulator operating in discontinuous mode. In Reference 1 a closed-form iterative map that describes the dynamics of the boost regulator operating in discontinuous mode has been derived. Using this map, period-doubling cascades have been predicted when the regulator is controlled under a simple proportional feedback, the bifurcation parameter being the feedback gain. Experimental measurement bears witness to the predicted chaos.1 Although the work op. cit. gives no theoretical insights into the underlying cause of chaos in such systems, it does prompt the important practical question of what form of feedback control may give rise to chaotic behaviour. This letter attempts to answer this question in the light of the Schwarzian derivative. The system under study is periodically driven with period T , and is in general described by a rst-order iterative map of the form:
Exogenous Schwann Cells Migrate, Remyelinate and Promote Clinical Recovery in Experimental Auto-Immune Encephalomyelitis

Schwann cell (SC) transplantation is currently being discussed as a strategy that may promote functional recovery in patients with multiple sclerosis (MS) and other inflammatory demyelinating diseases of the central nervous system (CNS). However this assumes they will not only survive but also remyelinate demyelinated axons in the chronically inflamed CNS. To address this question we investigated the fate of transplanted SCs in myelin oligodendrocyte glycoprotein (MOG)-induced experimental autoimmune encephalomyelitis (EAE) in the Dark Agouti rat; an animal model that reproduces the complex inflammatory demyelinating immunopathology of MS. We now report that SCs expressing green fluorescent protein (GFP-SCs) allografted after disease onset not only survive but also migrate to remyelinate lesions in the inflamed CNS. GFP-SCs were detected more frequently in the parenchyma after direct injection into the spinal cord, than via intra-thecal delivery into the cerebrospinal fluid. In both cases the transplanted cells intermingled with astrocytes in demyelinated lesions, aligned with axons and by twenty one days post transplantation had formed Pzero protein immunoreactive internodes. Strikingly, GFP-SCs transplantation was associated with marked decrease in clinical disease severity in terms of mortality; all GFP-SCs transplanted animals survived whilst 80% of controls died within 40 days of disease.
Comparing Corpus-based to Web-based Lookup Techniques for Automatic English Inclusion Detection

The influence of English as a global language continues to grow to an extent that its words and expressions permeate the original forms of other languages. This paper evaluates a modular Web-based sub-component of an existing English inclusion classifier and compares it to a corpus-based lookup technique. Both approaches are evaluated on a German gold standard data set. It is demonstrated to what extent the Web-based approach benefits from the amount of data available online and the fact that this data is constantly updated.
Performance Analysis of Retina and DoG Filtering Applied to Face Images for Training Correlation Filters

Recognizing facial images with nonhomogeneous illumina- tion is a challenging task. Retina and difference of Gaussiansltering have been applied to facial images in order to remove variations in illumination. This paper presents a study on how these preprocessing operations affect or improve the performance of the correlationlters in a face recognition task. These preprocessing operations were applied to CMU and YaleB facial databases, which containing images with homogeneous and nonhomogeneous illumination, respectively. Results show that these operations improve the performance of correlationlters for recognizing facial images with variable illumination.
Is Burnout a Depressive Disorder ? A Reexamination

Whether burnout and depression cover the same psychopathology remains to be elucidated. To date, subtypes of depression have been overlooked in research on the burnout–depression overlap. Our aim was to estimate the prevalence of depressive disorders in workers with burnout while examining the overlap of burnout with the atypical subtype of depression. The present study included 5,575 schoolteachers (mean age 41 years; 78% female). Burnout was assessed with the Maslach Burnout Inventory. Depression was measured with the 9-item depression scale of the Patient Health Questionnaire (PHQ-9). Atypical features of depression were examined using a dedicated module, referenced to the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; American Psychiatric Association, 1994). We found that 90% of the teachers identified as burned out met diagnostic criteria for depression. Among them, 92% scored 15 or higher on the PHQ-9, a threshold at which active treatment with pharmacotherapy and/or psychotherapy is recommended. The features of atypical depression were observed in 63% of the burned-out participants with major depression. Emotional exhaustion, the hallmark of burnout, was more strongly associated with depression than with depersonalization and reduced personal accomplishment, the 2 other putative dimensions of burnout. The present study suggests
Analytical method for blind binary signal separation

The blind separation of multiple co-channel binary digital signals using an antenna array involves finding a factorization of a data matrix X into X=AS, where all entries of S are +1 or -1. It is shown that this problem can be solved exactly and non-iteratively, via a certain generalized eigenvalue decomposition. As indicated by simulations, the algorithm is robust in the presence of noise. An interesting implication is that certain cluster segmentation problems can be solved using eigenvalue techniques.
A Single Mating-Type Locus Composed of Homeodomain Genes Promotes Nuclear Migration and Heterokaryosis in the White-Rot Fungus Phanerochaete chrysosporium

ABSTRACT The white-rot basidiomycete fungus Phanerochaete chrysosporium (Agaricomycetes) is a model species that produces potent wood-degrading enzymes. The mating system of the species has been difficult to characterize due to its cryptic fruiting habit and lack of clamp connections in the heterokaryotic phase. By exploiting the draft genome sequence, we reevaluated the mating system of P. chrysosporium by studying the inheritance and segregation of putative mating-type gene homologues, the homeodomain transcription factor genes (MAT-A) and the pheromone receptors (MAT-B). A pattern of mating incompatibility and fructification consistent with a bipolar system with a single MAT locus was observed, but the rejection response was much weaker than that seen in other agaricomycete species, leading to stable heterokaryons with identical MAT alleles. The homeodomain genes appear to comprise the single MAT locus because they are heterozygous in wild strains and hyperpolymorphic at the DNA sequence level and promote aspects of sexual reproduction, such as nuclear migration, heterokaryon stability, and basidiospore formation. The pheromone receptor loci that might constitute a MAT-B locus, as in many other Agaricomycetes, are not linked to the MAT-A locus and display low levels of polymorphism. This observation is inconsistent with a bipolar mating system that includes pheromones and pheromone receptors as mating-type determinants. The partial uncoupling of nuclear migration and mating incompatibility in this species may be predicted to lead to parasexual recombination and may have contributed to the homothallic behavior observed in previous studies.
Detectability of Arctic methane sources at six sites performing continuous atmospheric measurements

Abstract. Understanding the recent evolution of methane emissions in the Arctic is necessary to interpret the global methane cycle. Emissions are affected by significant uncertainties and are sensitive to climate change, leading to potential feedbacks. A polar version of the CHIMERE chemistry-transport model is used to simulate the evolution of tropospheric methane in the Arctic during 2012, including all known regional anthropogenic and natural sources, in particular freshwater emissions which are often overlooked in methane modelling. CHIMERE simulations are compared to atmospheric continuous observations at six measurement sites in the Arctic region. In winter, the Arctic is dominated by anthropogenic emissions; emissions from continental seepages and oceans, including from the East Siberian Arctic Shelf, can contribute significantly in more limited areas. In summer, emissions from wetland and freshwater sources dominate across the whole region. The model is able to reproduce the seasonality and synoptic variations of methane measured at the different sites. We find that all methane sources significantly affect the measurements at all stations at least at the synoptic scale, except for biomass burning. In particular, freshwater systems play a decisive part in summer, representing on average between 11 and 26 % of the simulated Arctic methane signal at the sites. This indicates the relevance of continuous observations to gain a mechanistic understanding of Arctic methane sources. Sensitivity tests reveal that the choice of the land-surface model used to prescribe wetland emissions can be critical in correctly representing methane mixing ratios. The closest agreement with the observations is reached when using the two wetland models which have emissions peaking in August–September, while all others reach their maximum in June–July. Such phasing provides an interesting constraint on wetland models which still have large uncertainties at present. Also testing different freshwater emission inventories leads to large differences in modelled methane. Attempts to include methane sinks (OH oxidation and soil uptake) reduced the model bias relative to observed atmospheric methane. The study illustrates how multiple sources, having different spatiotemporal dynamics and magnitudes, jointly influence the overall Arctic methane budget, and highlights ways towards further improved assessments.
Source Extraction Representing Thumb and Little Finger Cortical Networks and Intra-Hand-Area Evoked Synchrony

A novel cerebral source extraction method is proposed (Functional Source Separation, FSS) starting from extra-cephalic magnetoencephalographic (MEG) signals in humans, based on source functional reactivity to the external stimulation. Their activity is obtained all along the rest state and during processing of a simple separate sensory stimuli of thumb or little finger. This method provides cerebral sources describing thumb and little finger primary representations, as demonstrated by their higher responsiveness to the corresponding finger stimuli and their different positions consistent with the homuncular organization. A dynamical index describing intra-regional synchrony was introduced, which showed higher levels when stimulating the thumb with respect to little finger stimulation, selectively in the gamma band ([33, 44]Hz). This indicates that the stimulation of a functionally prevalent finger (thumb) activates a cortical network more synchronized in the gamma band than a nonprevalent one (little finger).
Interpreting CNNs via Decision Trees

This paper aims to quantitatively explain the rationales of each prediction that is made by a pre-trained convolutional neural network (CNN). We propose to learn a decision tree, which clarifies the specific reason for each prediction made by the CNN at the semantic level. I.e., the decision tree decomposes feature representations in high conv-layers of the CNN into elementary concepts of object parts. In this way, the decision tree tells people which object parts activate which filters for the prediction and how much each object part contributes to the prediction score. Such semantic and quantitative explanations for CNN predictions have specific values beyond the traditional pixel-level analysis of CNNs. More specifically, our method mines all potential decision modes of the CNN, where each mode represents a typical case of how the CNN uses object parts for prediction. The decision tree organizes all potential decision modes in a coarse-to-fine manner to explain CNN predictions at different fine-grained levels. Experiments have demonstrated the effectiveness of the proposed method.
Neuropeptide Y Stimulates Proliferation and Migration of Vascular Smooth Muscle Cells from Pregnancy Hypertensive Rats via Y1 and Y5 Receptors

The increased proliferation and migration of vascular smooth muscle cells (VSMCs) play important roles in pathophysiological remodeling of arteries during hypertension in pregnancy. However, the mechanisms involved in this process remain unclear. We hypothesized that Neuropeptide Y (NPY), which is a potent mitogenic peptide, participates in modulating proliferation and migration of VSMCs during hypertension in pregnancy. Using pregnant hypertensive rats, induced by intraperitoneal injection of L-nitro-arginine methylester (L-NAME), the plasma concentration of NPY was detected. Open angle, which reflects the non-uniform remodeling with high sensitivity, was used to detect the pathophysiological vascular remodeling in vivo. The results revealed that NPY concentration and artery open angle were both significantly increased in rats with hypertension in pregnant. The underlying mechanism of elevated NPY on vascular remodeling were further analyzed by using cultured VSMCs in vitro. In cultured VSMCs, NPY most effectively stimulated the migration and proliferation of VSMCs at 10-6 mol/L, similar to the plasma concentration in L-NAME hypertension in pregnant rats. NPY up-regulated the expressions of both Y1 and Y5 receptors, increased the phosphorylations of STAT3 on Tyr705 and Ser727 residues, and induced the expression of c-Fos. The NPY-induced VSMCs proliferation was reduced by Y5 receptor antagonist, and fully blocked by combinations with other antagonist, such as Y2+Y5, Y1+Y5, and Y1+Y2+Y5. In contrast, the NPY-induced VSMC migration was blocked by either Y receptor antagonist or any combination of Y receptor antagonists. These results suggest that the elevated plasma concentration of NPY during hypertension in pregnancy may induce VSMC proliferation mainly via Y5 receptor, which subsequently modulate STAT3 and c-Fos signaling pathways to result in the vascular remodeling. These results also suggest that NPY mainly acts on VSMCs in vitro via Y1, Y5 receptors and in vascular tissues in vivo via Y5 receptor.
2016 Ieee International Conference on Big Data (big Data) Entity Resolution Acceleration Using the Automata Processor

—Entity Resolution (ER), the process of finding identical entities across different databases, is critical to many information-integration applications. As sizes of databases explode in the big-data era, it becomes computationally expensive to recognize identical entities among all records with variations allowed across multiple databases. Profiling results show that approximate matching is the primary bottleneck. The Au-tomata Processor (AP), an efficient and scalable semiconductor architecture for parallel automata processing, provides a new opportunity for hardware acceleration for ER. We propose an AP-accelerated ER solution, which accelerates the performance bottleneck of fuzzy matching for similar but potentially inexactly-matched names, and use several different real-world applications to illustrate its effectiveness. We compared the proposed method with several conventional methods and achieved both promising speedups and better accuracy (more correct pairs and less generalized merge distance cost) for different datasets. I. INTRODUCTION Entity Resolution (ER), also known as Record Linkage or Purging/Merging problems, refers to finding records that store the same entity within a single database or across different databases [1]. ER is an important kernel of many information-integration applications. For example, Social Networks and Archival Context (SNAC) collects records from databases all over the world to provide an integrated platform for searching historical collections [2]. In such applications , the records of the same person may be stored with slight differences, because documents come from different sources, with different naming conventions, transliteration conventions, etc. SNAC needs to find the records referring to the same entity despite different representations and merge these records. The intuitive method of solving ER is to compare all possible pair records and check whether a pair represents the same entity. Determining whether two records represent the same entity is usually computationally expensive. For example, the time complexity of the intuitive method is O(N
A cross-layer design exploration of charge-recycled power-delivery in many-layer 3D-IC

3D-IC technology brings both the opportunities to continue the historical trend of integration-level scaling and the challenges to deliver power reliably and efficiently. Voltage-stacking (V-S), a charge-recycled power delivery scheme that connects the different layers' supply/ground nets into a series stack, provides a scalable solution to the 3D-IC power delivery wall. While prior work has extensively discussed the implementations of V-S at circuit-level, a cross-layer study that examines its system-level implications is missing. In this paper, we start with a circuit implementation of a charge-recycled voltage regulator and build an architecture-level model to study the costs and benefits of utilizing V-S in 3D-IC. Our study shows that by significantly improving the EM-lifetime of C4 and TSV array (e.g., up to 5x) while only marginally increasing the average-case voltage noise (e.g., 0.75% Vdd IR drop), V-S provides a scalable solution for many-layer 3D-IC's power delivery challenge.
Towards Textbook Efficiency for Parallel Multigrid

In this work, we extend Achi Brandt’s notion of textbook multigrid efficiency (TME) to massively parallel algorithms. Using a finite element based geometric multigrid implementation, we recall the classical view on TME with experiments for scalar linear equations with constant and varying coefficients as well as linear systems with saddle-point structure. To extend the idea of TME to the parallel setting, we give a new characterization of a work unit (WU) in an architecture-aware fashion by taking into account performance modeling techniques. We illustrate our newly introduced parallel TME measure by large-scale computations, solving problems with up to 200 billion unknowns on a TOP-10 supercomputer. AMS subject classifications: 65N55, 68W10
Hydraulic system modeling through memory-based learning

Hydraulic machines used in a number of applications are highly non-linear systems. Besides the dynamic coupling between the different links, there are significant actuator nonlinearities due to the inherent properties of the hydraulic system. Automation of such machines requires the robotic machine to be at least as productive as a manually operated machine, which in turn makes the case for performing tasks optimally with respect to an objective function (say) composed of a combination of time and fuel usage. Optimal path computation requires fast machine models in order to be practically usable. This work examines the use of memory-based learning in constructing the model of a 25-ton hydraulic excavator. The learned actuator model is used in conjunction with a linkage dynamic model to construct a complete excavator model which is much faster than a complete analytical model. Test results show that the approach effectively captures the interactions between the different actuators.