Miracle-dz/UniFAR-FTUs huggingface dataset:
The role played by public enterprises: how much does it differ across countries?

This article studies the extent to which governments produce goods for the market (that is, the extent of public enterprise production). It concludes that the current literature dramatically understates the role of public enterprises in many low-productivity countries. The current literature focuses on the total value of goods produced by public enterprises. This article focuses on the types of goods they produce. While the total value of goods produced by public enterprises (as a share of total output) differs a bit across countries, the types of goods they produce differ much more dramatically. In many low-productivity countries, the government produces a large share of the country's manufactured goods. In nearly all high-productivity countries, the government stays out of the manufacturing sector altogether. Therefore—and because the manufacturing sector plays a special role in economies—this article concludes that public enterprises play a very large role in many low-productivity countries.
AniMatrix: A Matrix-Based Visualization of Software Evolution

Software designs are ever changing to adapt to new environments and requirements. Tracking and understanding changes in modules and relationships in a software project is difficult, but even more so when the software goes through several types of changes. The typical complexity and size of software also makes it harder to grasp software evolution patterns. In this paper, we present an interactive matrix-based visualization technique that, combined with animation, depicts how software designs evolve. For example, it shows which new modules and couplings are added and removed over time. Our generic visualization supports dynamic and weighted digraphs and is applied in the context of software evolution. Analyzing source code changes is important to determine the software's structural organization and identify quality issues over time. To demonstrate our approach, we explore open-source repositories and discuss some of our findings regarding these evolving software designs.
Dependency Parsing as Head Selection

Conventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently selecting the head of each word in a sentence. Our model which we call DENSE (as shorthand for Dependency Neural Selection) produces a distribution over possible heads for each word using features obtained from a bidirectional recurrent neural network. Without enforcing structural constraints during training, DeNSe generates (at inference time) trees for the overwhelming majority of sentences, while non-tree outputs can be adjusted with a maximum spanning tree algorithm. We evaluate DeNSe on four languages (English, Chinese, Czech, and German) with varying degrees of non-projectivity. Despite the simplicity of the approach, our parsers are on par with the state of the art.
Quantitative modeling of transcription factor binding specificities using DNA shape

Significance Genomes provide an abundance of putative binding sites for each transcription factor (TF). However, only small subsets of these potential targets are functional. TFs of the same protein family bind to target sites that are very similar but not identical. This distinction allows closely related TFs to regulate different genes and thus execute distinct functions. Because the nucleotide sequence of the core motif is often not sufficient for identifying a genomic target, we refined the description of TF binding sites by introducing a combination of DNA sequence and shape features, which consistently improved the modeling of in vitro TF−DNA binding specificities. Although additional factors affect TF binding in vivo, shape-augmented models reveal binding specificity mechanisms that are not apparent from sequence alone. DNA binding specificities of transcription factors (TFs) are a key component of gene regulatory processes. Underlying mechanisms that explain the highly specific binding of TFs to their genomic target sites are poorly understood. A better understanding of TF−DNA binding requires the ability to quantitatively model TF binding to accessible DNA as its basic step, before additional in vivo components can be considered. Traditionally, these models were built based on nucleotide sequence. Here, we integrated 3D DNA shape information derived with a high-throughput approach into the modeling of TF binding specificities. Using support vector regression, we trained quantitative models of TF binding specificity based on protein binding microarray (PBM) data for 68 mammalian TFs. The evaluation of our models included cross-validation on specific PBM array designs, testing across different PBM array designs, and using PBM-trained models to predict relative binding affinities derived from in vitro selection combined with deep sequencing (SELEX-seq). Our results showed that shape-augmented models compared favorably to sequence-based models. Although both k-mer and DNA shape features can encode interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space. In addition, analyzing the feature weights of DNA shape-augmented models uncovered TF family-specific structural readout mechanisms that were not revealed by the DNA sequence. As such, this work combines knowledge from structural biology and genomics, and suggests a new path toward understanding TF binding and genome function.
An asymptotically unbiased estimator for bearings-only and Doppler-bearing target motion analysis

Bearings-only (BO) and Doppler-bearing (DB) target motion analysis (TMA) attempt to obtain a target trajectory based on bearings and on Doppler and bearing measurements, respectively, from an observer to the target. The BO-TMA and DB-TMA problems are nontrivial because the measurement equations are nonlinearly related to the target location parameters. The pseudolinear formulation provides a linear estimator solution, but the resulting location estimate is biased. The instrumental variable method and the numerical maximum likelihood approach can eliminate the bias. Their convergence behavior, however, is not easy to control. This paper proposes an asymptotically unbiased estimator of the tracking problem. The proposed method applies least squares minimization on the pseudolinear equations with a quadratic constraint on the unknown parameters. The resulting estimator is shown to be solving the generalized eigenvalue problem. The proposed solution does not require initial guesses and does not have convergence problems. Sequential forms of the proposed algorithms for both BO-TMA and DB-TMA are derived. The sequential algorithms improve the estimation accuracy as a new measurement arrives and do not require generalized eigenvalue decomposition for solution update. The proposed estimator achieves the Cramer-Rao Lower Bound (CRLB) asymptotically for Gaussian noise before the thresholding effect occurs.
This Paper Is Included in the Proceedings of the 11th Usenix Symposium on Networked Systems Design and Implementation (nsdi '14). Farm: Fast Remote Memory Farm: Fast Remote Memory

We describe the design and implementation of FaRM, a new main memory distributed computing platform that exploits RDMA to improve both latency and through-put by an order of magnitude relative to state of the art main memory systems that use TCP/IP. FaRM exposes the memory of machines in the cluster as a shared address space. Applications can use transactions to allocate , read, write, and free objects in the address space with location transparency. We expect this simple programming model to be sufficient for most application code. FaRM provides two mechanisms to improve performance where required: lock-free reads over RDMA, and support for collocating objects and function shipping to enable the use of efficient single machine transactions. FaRM uses RDMA both to directly access data in the shared address space and for fast messaging and is carefully tuned for the best RDMA performance. We used FaRM to build a key-value store and a graph store similar to Facebook's. They both perform well, for example, a 20-machine cluster can perform 167 million key-value lookups per second with a latency of 31µs.
Active online confidence boosting for efficient object classification

We present a novel efficient algorithm for object classification. Our method is based on the active learning framework, in which training and classification are performed in loops, and new ground truth labels are queried from the supervisor in each loop. Our underlying classifier is from the family of boosting methods, but in contrast to earlier methods, our Confidence Boosting particularly focusses on misclassified samples that have a high classification confidence associated. We show that weighting these samples more than others leads to a decrease of overconfidence, for which we give a formal definition. As a result, our classifier is better suited for active learning, leading to steeper learning curves and less required label queries. We show the benefits of our approach on standard data sets from machine learning and robotics.
Activation and Inhibition of Transglutaminase 2 in Mice

Transglutaminase 2 (TG2) is an allosterically regulated enzyme with transamidating, deamidating and cell signaling activities. It is thought to catalyze sequence-specific deamidation of dietary gluten peptides in the small intestines of celiac disease patients. Because this modification has profound consequences for disease pathogenesis, there is considerable interest in the design of small molecule TG2 inhibitors. Although many classes of TG2 inhibitors have been reported, thus far an animal model for screening them to identify promising celiac drug candidates has remained elusive. Using intraperitoneal administration of the toll-like receptor 3 (TLR3) ligand, polyinosinic-polycytidylic acid (poly(I∶C)), we induced rapid TG2 activation in the mouse small intestine. Dose dependence was observed in the activation of TG2 as well as the associated villous atrophy, gross clinical response, and rise in serum concentration of the IL-15/IL-15R complex. TG2 activity was most pronounced in the upper small intestine. No evidence of TG2 activation was observed in the lung mucosa, nor were TLR7/8 ligands able to elicit an analogous response. Introduction of ERW1041E, a small molecule TG2 inhibitor, in this mouse model resulted in TG2 inhibition in the small intestine. TG2 inhibition had no effect on villous atrophy, suggesting that activation of this enzyme is a consequence, rather than a cause, of poly(I∶C) induced enteropathy. Consistent with this finding, administration of poly(I∶C) to TG2 knockout mice also induced villous atrophy. Our findings pave the way for pharmacological evaluation of small molecule TG2 inhibitors as drug candidates for celiac disease.
Visual Instance Retrieval with Deep Convolutional Networks

This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval. Besides the choice of convolutional layers, we present an efficient pipeline exploiting multi-scale schemes to extract local features, in particular, by taking geometric invariance into explicit account, i.e. positions, scales and spatial consistency. In our experiments using five standard image retrieval datasets, we demonstrate that generic ConvNet image representations can outperform other state-of-the-art methods if they are extracted appropriately.
The Flickering Wheel Illusion: When α Rhythms Make a Static Wheel Flicker

α oscillations (8–14 Hz) greatly influence brain activity, yet we generally do not experience them consciously: the world does not appear to oscillate. Dedicated strategies must exist in the brain to prevent these oscillations from disrupting normal processing. Could suitable stimuli fool these strategies and lead to the conscious experience of our own brain oscillations? We describe and explore a novel illusion in which the center of a static wheel stimulus (with 30–40 spokes) is experienced as flickering when viewed in the visual periphery. The key feature of this illusion is that the stimulus fluctuations are experienced as a regular and consistent flicker, which our human observers estimated at ∼9 Hz during a psychophysical matching task. Correspondingly, the occipital α rhythm of the EEG was the only oscillation that showed a time course compatible with the reported illusion: when α amplitude was strong, the probability of reporting illusory flicker increased. The peak oscillatory frequency for these flicker-induced modulations was significantly correlated, on a subject-by-subject basis, with the individual α frequency measured during rest, in the absence of visual stimulation. Finally, although the effect is strongest during eye movements, we showed that stimulus motion relative to the retina is not necessary to perceive the illusion: the flicker can also be perceived on the afterimage of the wheel, yet by definition this afterimage is stationary on the retina. We conclude that this new flickering illusion is a unique way to experience the α rhythms that constantly occur in the brain but normally remain unnoticed.
Unmanaged Multiversion STM

Multiversion STM systems allow a transaction to read old values of a recently updated object, after which the transaction may serialize before transactions that committed earlier in physical time. This ability to “commit in the past” is particularly appealing for long-running read-only transactions, which may otherwise starve in many STM systems, because short-running peers modify data out from under them before they have a chance to finish. Most previous work on multiversion STM assumed an object-oriented, garbage-collected language. In contrast, we present a multiversion STM system designed for unmanaged languages, in which ownership information is typically maintained on a “per-slice” (rather than a “per-object”) basis, and in which automatic garbage collection may not be available. We have implemented this UMV system as an extension to the TL2-like LLT back end of the RSTM suite. Experiments with microbenchmarks confirm that UMV can dramatically reduce the abort rate of long read-only transactions. Overall impacts range from 2× slowdown to 5× speedup, depending on the offered workload.
Allocation of Time , EEG-Engagement and EEG-Workload Resources as Scientific Problem Solving Skills Are Acquired in the Classroom

We have studied EEG-derived metrics of Workload (WL) and Engagement (E) as students developed and refined their problem solving approaches to determine the degree to which these are modulated as problem solving experience is gained. The problem solving tasks (IMMEXTM) used for these studies were a series of science and mathematics online simulations designed for middle school students. Comparison of WL and E levels on IMMEXTM tasks and baseline cognitive tasks indicated that the simulations recruited high levels of both WL and E. Detailed second-by second analysis of these metrics during problem solving indicated they were dynamic with cycles of high and low values. Aggregated comparisons of WL and E across students as they gained experience in problem solving showed rapid decreases in time on task while E and particularly WL showed little change. Performances where the solution was missed were significantly lower in WL than when the problem was solved. Analysis of WL and E of individual students showed fluctuations of E with practice with some students showing decreased levels with time and others showing increases. When the levels of WL and E were compared across strategies accounted to be novice or proficient by probabilistic modeling there were no significant differences. These findings indicate that as students practice and refine their problem solving strategies the levels, and changes in the mental effort put into the process is not easily predicted by the changes in the speed of the task, by whether or not the problem was solved, or whether the resulting strategy is more novice or expert.
Selective depletion of tumor neovasculature by microbubble destruction with appropriate ultrasound pressure

Low‐intensity ultrasound‐microbubble (LIUS‐MB) treatment is a promising antivascular therapy for tumors. We sought to determine whether LIUS‐MB treatment with an appropriate ultrasound pressure could achieve substantial and persistent cessation of tumor perfusion without having significant effects on normal tissue. Further, we investigated the mechanisms underlying this treatment. Murine S‐180 sarcomas, thigh muscles, and skin tissue from 60 tumor‐bearing mice were subjected to sham therapy, an ultrasound application combined with microbubbles in four different ultrasound pressures (0.5, 1.5, 3.0, 5.0 MPa), or ultrasound at 5.0 MPa alone. Subsequently, contrast‐enhanced ultrasonic imaging and histological studies were performed. Tumor microvessels, tumor cell necrosis, apoptosis, tumor growth, and survival were evaluated in 85 mice after treatment with the selected ultrasound pressure. We found that twenty‐four hours after LIUS‐MB treatment at 3.0 MPa, blood perfusion and microvessel density of the tumor had substantially decreased by 84 ± 8% and 84%, respectively (p < 0.01). Similar reductions were not observed in the muscle or skin. Additionally, an extreme reduction in the number of immature vessels was observed in the tumor (reduced by 90%, p < 0.01), while the decrease in mature vessels was not significant. Further, LIUS‐MB treatment at 3.0 MPa promoted tumor cell necrosis and apoptosis, delayed tumor growth, and increased the survival rate of tumor‐bearing mice (p < 0.01). These findings indicate that LIUS‐MB treatment with an appropriate ultrasound pressure could selectively and persistently reduce tumor perfusion by depleting the neovasculature. Therefore, LIUS‐MB treatment offers great promise for clinical applications in antivascular therapy for solid tumors.
The relative contribution of gesture to communication in aphasia : a case study

Communication involves the use of many modalities. Communication modalities used for expressing information include speech, gesture, writing, and others. Although speech is usually considered the primary modality for expression of information, other modalities, such as gesture, are often integral parts of communicative exchanges. The purpose of this study was to examine the expressive communication ability, through different modalities, in an aphasic subject who demonstrated exceptional use of gesture in spontaneous conversation. Findings were discussed within the context of two hypotheses: (1) a central organizer exists which coordinates the output modalities; and (2) the modalities are differentially impaired for this subject, but symbolic functioning is relatively intact. This investigation involved a single subject. Videotapes were made of the subject describing sequences of pictures. Modality use (speech, gesture, or written) was restricted in different attempts at sequence description, so that the subject described each sequence in each individual modality and in all possible combinations of modalities. Judges observed the videotapes of the subject (and written material produced by the subject, when writing was allowed), and recorded the information they perceived. The information recorded by the judges was the data used for analysis of the subject's communicative ability through different modalities. Findings supported differential impairment of modalities for this subject. Communication through speech was more severely impaired than communication through gesture or writing. Symbolic functioning appeared to be relatively intact.
Factoring the adjoint and maximal Cohen-Macaulay modules over the generic determinant

A question of Bergman asks whether the adjoint of the generic square matrix over a field can be factored nontrivially as a product of square matrices. We show that such factorizations indeed exist over any coefficient ring when the matrix has even size. Establishing a correspondence between such factorizations and extensions of maximal Cohen–Macaulay modules over the generic determinant, we exhibit all factorizations where one of the factors has determinant equal to the generic determinant. The classification shows not only that the Cohen–Macaulay representation theory of the generic determinant is wild in the tame-wild dichotomy, but that it is quite wild: even in rank two, the isomorphism classes cannot be parametrized by a finite-dimensional variety over the coefficients. We further relate the factorization problem to the multiplicative structure of the Ext–algebra of the two nontrivial rank-one maximal Cohen–Macaulay modules and determine it completely.
Random gaps

Itisproved thatthereexistsan ( ! 1 ;! 1 )Souslin gap in theBoolean algebra ( L 0 ( (cid:23) ) = Fin ; (cid:18) (cid:3) ae )forevery nonseparablem easure (cid:23) . Thusa Souslin, also known asdestructible,( ! 1 ;! 1 )gap in P ( N ) = Fin can alwaysbe constructed from uncountably m any random reals.W eexplain how to obtain the corresponding conclusion from the hypothesisthatLebesgue m easure can be extended to allsubsetsofthe realline (RV M ).
AccFFT: A library for distributed-memory FFT on CPU and GPU architectures

We present a new library for parallel distributed Fast Fourier Transforms (FFT). The importance of FFT in science and engineering and the advances in high performance computing necessitate further improvements. AccFFT extends existing FFT libraries for CUDA-enabled Graphics Processing Units (GPUs) to distributed memory clusters. We use overlapping communication method to reduce the overhead of PCIe transfers from/to GPU. We present numerical results on the Maverick platform at the Texas Advanced Computing Center (TACC) and on the Titan system at the Oak Ridge National Laboratory (ORNL). We present the scaling of the library up to 4,096 K20 GPUs of Titan.
International Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA) 2013 A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing

The next-generation of cloud computing will thrive on how effectively the infrastructure are instantiated and available resources utilized dynamically. Load balancing which is one of the main challenges in Cloud computing, distributes the dynamic workload across multiple nodes to ensure that no single resource is either overwhelmed or underutilized. This can be considered as an optimization problem and a good load balancer should adapt its strategy to the changing environment and the types of tasks. This paper proposes a novel load balancing strategy using Genetic Algorithm (GA). The algorithm thrives to balance the load of the cloud infrastructure while trying minimizing the make span of a given tasks set. The proposed load balancing strategy has been simulated using the CloudAnalyst simulator. Simulation results for a typical sample application shows that the proposed algorithm outperformed the existing approaches like First Come First Serve (FCFS), Round Robing (RR) and a local search algorithm Stochastic Hill Climbing (SHC). c