Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Virtual Reality in the Treatment of Gambling Disorder: The Development of a New Tool for Cognitive Behavior Therapy

Virtual reality (VR) can be used in the treatment of gambling disorder to provide emotionally charged contexts (e.g., induce cravings) where patients can practice cognitive behavior therapy (CBT) techniques in the safety of the therapist’s office. This raises practical questions, such as whether the cravings are sufficient to be clinically useful but also manageable enough to remain clinically safe. Pilot data are also needed to test the development of a treatment manual and prepare large randomized control trials. This paper reports on three studies describing (a) cravings induced in VR compared to real gambling and a control game of skill with no money involved (N = 28 frequent gamblers and 36 infrequent gamblers); (b) the usefulness of a treatment protocol with only two CBT sessions using VR (N = 34 pathological gamblers); and (c) the safety of a four-session treatment program of CBT in VR (N = 25 pathological gamblers). Study 1 reveals that immersions in VR can elicit desire and a positive anticipation to gamble in frequent gamblers that are (a) significantly stronger than for infrequent gamblers and for playing a control game of skill and (b) as strong as for gambling on a real video lottery terminal. Study 2 documents the feasibility of integrating VR in CBT, its usefulness in identifying more high-risk situations and dysfunctional thoughts, how inducing cravings during relapse prevention exercises significantly relates to treatment outcome, and the safety of the procedure in terms of cybersickness. Results from Study 3 confirm that, compared to inducing urges to gamble in imagination, using VR does not lead to urges that are stronger, last longer, or feel more out of control. Outcome data and effect sizes are reported for both randomized control pilot trials conducted in inpatient settings. Suggestions for future research are provided, including on increasing the number of VR sessions in the treatment program.
Optimized Memory-Disk Integrated System with DRAM and Nonvolatile Memory

New nonvolatile memory devices can overcome the high-energy consumption, volatility, and density scaling limit of dynamic RAM (DRAM). With these advantages, next-generation nonvolatile memory devices can use both working memory and persistent storage simultaneously. In this study, we horizontally arrange DRAM, phase change memory (PCM), and flash memories as a single compound layer of working and storage space for a memory-disk integrated system (MDIS). The MDIS architecture consists of a static data buffer, DRAM/PCM/Flash hybrid array, and its associated MDIS management module. The static data buffer is placed between the last-level cache and DRAM/PCM/Flash hybrid array to reduce the performance gap. In the DRAM/PCM/Flash hybrid array, DRAM space and a portion of PCM space are used for dynamic data, and the remaining portion of PCM space and flash memory are used for static data including program text and data segments. Based on our simulation results, dynamic access latency of a dynamic area with 128 MB DRAM of space is faster than the MDIS with a PCM-only array model by approximately 5.5 times. Furthermore, the results show that the total execution time of our proposed model with 128-MB DRAM space improves speed by 4.3 times compared to conventional memory-storage system, respectively.
Comparison of Two Overmodulation Strategies in an Indirect Matrix Converter

This paper discusses two overmodulation controls, namely, square wave and trapezoidal, in the indirect matrix converter (IMC) for motor drive application. The differences between these two modulations are analyzed based on the output-input voltage transfer ratio (VTR), the efficiency, and the current total harmonic distortions under two load conditions. Moreover, the performance is then improved by implementing four-step commutation methods in the primary side of the IMC. The validity of the proposed methods will be demonstrated through simulation and experimentally. The experimental results show that the VTR can be improved from 0.866 to 0.97 in the square wave modulation and 0.92 for the trapezoidal wave modulation in an induction motor drive system.
GPU-based Optimization of Tool Path Planning in 5-Axis Flank Milling

5-axis machining technology has received much attention since the late 90’s. It offers better machining efficiency and superior shaping capability compared with 3-axis machining. Machining error control is considered to be a challenging task in 5-axis flank milling of complex geometries. Previous studies have shown that optimized tool path planning is a feasible approach to reduction of the machining error. However, the error estimation is very time-consuming in the optimization process, thus limiting the practicality of the approach. In this work, we apply GPU computing technology to solve this problem. A PSO-based optimization scheme is developed to generate a series of cutter locations corresponding to a globally minimized machining error. The error induced by each cutter location is simultaneously calculated by the parallel processing units in GPU. This significantly accelerates the search process in the optimization scheme, while the optimal solution remains the same as that obtained by CPU. Our test result demonstrates the potential of improving the computational efficiency in CAD/CAM using GPU.
Network comparison using directed graphlets

With recent advances in high-throughput cell biology the amount of cellular biological data has grown drastically. Such data is often modeled as graphs (also called networks) and studying them can lead to new insights into molecule-level organization. A possible way to understand their structure is by analysing the smaller components that constitute them, namely network motifs and graphlets. Graphlets are particularly well suited to compare networks and to assess their level of similarity but are almost always used as small undirected graphs of up to five nodes, thus limiting their applicability in directed networks. However, a large set of interesting biological networks such as metabolic, cell signaling or transcriptional regulatory networks are intrinsically directional, and using metrics that ignore edge direction may gravely hinder information extraction. The applicability of graphlets is extended to directed networks by considering the edge direction of the graphlets. We tested our approach on a set of directed biological networks and verified that they were correctly grouped by type using directed graphlets. However, enumerating all graphlets in a large network is a computationally demanding task. Our implementation addresses this concern by using a state-of-the-art data structure, the g-trie, which is able to greatly reduce the necessary computation. We compared our tool, gtrieScanner, to other state-of-the art methods and verified that it is the fastest general tool for graphlet counting.
An event-log analysis and simulation-based approach for quantifying sustainability metrics in production facilities

This paper describes a discrete-event simulation and event-log analysis based approach for computing sustainability metrics in production environments to perform various types of comparative analysis and assessments. Event logs collected from the production environment are analyzed to compute current state sustainability metrics such as energy usage, carbon footprint and heating/cooling requirements. Bootstrapping based forecasting leveraging expert input is utilized to estimate future demand. The forecasted demand is then simulated to predict sustainability metrics. The discrete-event simulation results from the forecasted data and computation of heat produced is combined with thermodynamic models of heat transfer through the thermal envelope of the facility to provide more accurate estimates of true carbon footprint associated with the production operations while also enabling cross-comparative studies of setting operations in different geographical locations. The framework and software tool enables the integration of productivity metrics and sustainability metrics in decision-making process for designing and operating production environments.
CoMiner: An Effective Algorithm for Mining Competitors from the Web

This paper attempts to accomplish a novel task of mining competitive information with respect to an entity (such as a company, product, person) from the web. An algorithm called "CoMiner" is proposed, which first extracts a set of comparative candidates of the input entity and then ranks them according to the comparability, and finally extracts the competitive fields. The experimental results show that the proposed algorithm drafts a complete picture of competitive relation of a given entity effectively.
Long‐term potentiation of human visual evoked responses

Long‐term potentiation (LTP) is a candidate synaptic mechanism underlying learning and memory that has been studied extensively at the cellular and molecular level in laboratory animals. To date, LTP has only been directly demonstrated in humans in isolated cortical tissue obtained from patients undergoing surgery, where it displays properties identical to those seen in non‐human preparations. Inquiry into the functional significance of LTP has been hindered by the absence of a human model. Here we give the first demonstration that the rapid repetitive presentation of a visual checkerboard (a photic ‘tetanus’) leads to a persistent enhancement of one of the early components of the visual evoked potential in normal humans. The potentiated response is largest in the hemisphere contralateral to the tetanized visual hemifield and is limited to one component of the visual evoked response (the N1b). The selective potentiation of only the N1b component makes overall brain excitability changes unlikely and suggests that the effect is due instead to an LTP process. While LTP is known to exist in the human brain, the ability to elicit LTP from non‐surgical patients will provide a human model system allowing the detailed examination of synaptic plasticity in normal subjects and may have future clinical applications in the assessment of cognitive disorders.
IBM Research Report Extracting Enterprise Vocabulary Using Linked Open Data

A common vocabulary is vital to smooth business operation, yet codifying and maintaining an enterprise vocabulary is an arduous, manual task. We present a fully automated process for creating an enterprise vocabulary, by extracting terms from a domain-speciﬁc corpus, and extracting their types from LOD (Linked Open Data). We applied this process to create a vocabulary for the IT industry, using 58 Gartner analyst reports as a corpus, and the LOD subset consisting of DBpedia and Freebase. We present novel techniques for linking, cleansing, and extending the types in this LOD sub-set, resulting in an improvement of 55% for our IT domain results. We further improved our results through NER over the corpus. Our NER training is completely automated, exploiting Wikipedia and DBpedia. Altogether, we achieved 46.3% recall and 78.1% precision.
Computing approximate value of the pbm index for counting number of clusters using genetic algorithm

Determining number of clusters present in a data set is an important problem in clustering. There exist very few techniques that can solve this problem satisfactorily. Most of these techniques are expensive with regard to computation time. Recently VAT (Visual Assessment of Tendency for clustering) images of data sets are used for this purpose along with GA and a validity index. A series of diagonal dark blocks in the VAT image represents possible clusters present in the data set. We shall show an efficient way to compute an approximate value of PBM index directly from the VAT image. It is shown that the present approach is able to suitable index values for finding appropriate number of dark blocks (clusters), under a GA framework.
Authentic and chimeric full-length genomic cDNA clones of bovine viral diarrhea virus that yield infectious transcripts

Bovine viral diarrhea virus (BVDV) is the most insidious and devastating viral pathogen of cattle in the United States. Disease control approaches must be based on detailed knowledge of virus biology. To develop reverse-genetic systems to study the molecular biology of the virus, we first constructed a plasmid containing the entire genome of BVDV cloned as cDNA. Subsequently, we showed that infectious BVDV was produced by cells transfected with uncapped RNA transcribed in vitro from the cDNA clone. This result defined functional 5' and 3' termini in viral genomic RNA and established the biological importance of the proposed internal ribosome entry site element in the 5' untranslated region of the viral genome. BVDV rescued from the infectious cDNA clone has an in vitro phenotype similar to that of the wild-type parent, the National Animal Disease Laboratory strain of BVDV. A deletion of a single codon in the full-length genomic BVDV cDNA clone, encoding glutamic acid at position 1600, gave rise to sequence-tagged virus easily identified by restriction fragment length polymorphism analysis of reverse transcription-PCR amplicons. Suitability of the molecular clone of BVDV for genomic manipulations was shown by substitution of the major envelope glycoprotein E2/gp53 with that of the Singer strain, giving rise to a chimeric virus. The predicted change in antigenic structure of the chimeric virus could be readily identified with strain-specific monoclonal antibodies by neutralization and immunofluorescence assays. Immediate applications of this system include development of safe and effective live vaccine strains possessing predetermined defined attenuating mutations.
Osteoclastogenic Potential of Peripheral Blood Mononuclear Cells in Cleidocranial Dysplasia

Cleidocranial dysplasia (CCD) is an autosomal dominant skeletal dysplasia characterized by hypoplastic or aplastic clavicles, dental abnormalities, and delayed closure of the cranial sutures. In addition, mid-face hypoplasia, short stature, skeletal anomalies and osteoporosis are common. We aimed to evaluate osteoclastogenesis in a child (4 years old), who presented with clinical signs of CCD and who have been diagnosed as affected by deletion of RUNX2, master gene in osteoblast differentiation, but also affecting T cell development and indirectly osteoclastogenesis. The results of this study may help to understand whether in this disease is present an alteration in the bone-resorptive cells, the osteoclasts (OCs). Unfractionated and T cell-depleted Peripheral Blood Mononuclear Cells (PBMCs) from patient were cultured in presence/absence of recombinant human M-CSF and RANKL. At the end of the culture period, OCs only developed following the addition of M-CSF and RANKL. Moreover, real-time PCR experiment showed that freshly isolated T cells expressed the osteoclastogenic cytokines (RANKL and TNFα) at very low level, as in controls. This is in accordance with results arising from flow cytometry experiments demonstrating an high percentage of circulating CD4+CD28+ and CD4+CD27+ T cells, not able to produce osteoclastogenic cytokines. Also RANKL, OPG and CTX serum levels in CCD patient are similar to controls, whereas QUS measurements showed an osteoporotic status (BTT-Z score -3.09) in the patient. In conclusions, our findings suggest that the heterozygous deletion of RUNX2 in this CCD patient did not alter the osteoclastogenic potential of PBMCs in vitro.
AIM: An Auction Incentive Mechanism in Wireless Networks with Opportunistic Routing

Opportunistic routing in wireless networks has strong requirement with nodes¡¯ cooperation. But in most realistic networks, nodes perform in their own interests and refuse to forward packets for others when they belong to different organizations. The aim of this paper is to provide a fair incentive mechanism to encourage cooperation and achieve the energy efficient. We propose an auction incentive mechanism (AIM) using a fair pricing mechanism to incentive nodes and modeling the pricing process as an auction game. Furthermore, we proof that, this game has a Bayesian Nash equilibrium point, and the profit of each relay node can be maximized when the game reached Bayesian Nash equilibrium. Our simulation results show that the AIM can effectively incentive network nodes to relay data, so that the network throughput could be improved. And the energy consuming of nodes is balanced that enhances the stability of networks.
A lossless image coder with context classification, adaptive prediction and adaptive entropy coding

We combine a context classification scheme with adaptive prediction and entropy coding to produce an adaptive lossless image coder. In this coder, we maximize the benefits of adaptivity using both adaptive prediction and entropy coding. The adaptive prediction is closely tied with the classification of contexts within the image. These contexts are defined with respect to the local edge, texture or gradient characteristics as well as local activity within small blocks of the image. For each context an optimal predictor is found which is used for the prediction of all pixels belonging to that particular context. Once the predicted values have been removed from the original image, a clustering algorithm is used to design a separate, optimal entropy coding scheme for encoding the prediction residual. Blocks of residual pixels are classified into a finite number of classes and members of each class are encoded using the entropy coder designed for that particular class. The combination of these two powerful techniques produces some of the best lossless coding results reported so far.
U.S. Investment 1901-2005: Incumbents, Entrants, and Q ∗

Our paper shows that investment by new firms responds to Tobin's Q much more elastically than does investment by incumbent firms. To explain this fact we build a model in which the investment-supply curve of incumbent firms is highly elastic and positively related to Q. However, when variation in Q is caused by shifts in this supply curve, the equilibrium relation between Q and investment that it traces out is negative. That alone causes a negative equilibrium relation between Q and investment. At high levels of Q, however, the investment of incumbents is further reduced, or crowded out, by the positive response of the investment by entering firms to the rise in Q. We fit a composite-capital version of the model to data, and we then repeat the exercise for a physical-capital version.
A new core

This article discusses motors for compressors in domestic air conditioners, an area in which there is a strong demand for high efficiency and also reports the results of the efficiency improvement and productivity of a new core structure. An innovative core structure and manufacturing method boosts efficiency of permanent-magnet motors. This article presents the close relationship between the motor characteristics and the manufacturing method and indicates a way to improve both motor characteristics and the productivity. To pursue further improvement in motor performance by improving the coil space factor further using the joint-lapped core and manufacturing cores with smaller magnetic resistance.
A Algorithm for the Nonlinear Complementarity Problem in Contact Mechanics Modeling

In this paper, we consider nonlinear complementarity problem in contact mechanics modeling (NCP). To solve the problem, we first establish a global error bound for NCP, and then propose a new type of solution method to solve the NCP based on the error bound estimation. The global convergence is also established, and the given numerical experiments shows the efficiency of the method.
Limited Durability of Viral Control following Treated Acute HIV Infection

Background Early treatment of acute HIV infection with highly active antiretroviral therapy, followed by supervised treatment interruption (STI), has been associated with at least transient control of viremia. However, the durability of such control remains unclear. Here we present longitudinal follow-up of a single-arm, open-label study assessing the impact of STI in the setting of acute HIV-1 infection. Methods and Findings Fourteen patients were treated during acute HIV-1 infection and subsequently subjected to an STI protocol that required retreatment if viral load exceeded 50,000 RNA copies/ml plasma or remained above 5,000 copies/ml for more than three consecutive weeks. Eleven of 14 (79%) patients were able to achieve viral loads of less than 5,000 RNA copies/ml for at least 90 d following one, two, or three interruptions of treatment. However, a gradual increase in viremia and decline in CD4+ T cell counts was observed in most individuals. By an intention-to-treat analysis, eight (57%), six (43%), and three (21%) of 14 patients achieved a maximal period of control of 180, 360, and 720 d, respectively, despite augmentation of HIV-specific CD4+ and CD8+ T cell responses. The magnitude of HIV-1-specific cellular immune responses before treatment interruption did not predict duration of viremia control. The small sample size and lack of concurrent untreated controls preclude assessment of possible clinical benefit despite failure to control viremia by study criteria. Conclusions These data indicate that despite initial control of viremia, durable viral control to less than 5,000 RNA copies/ml plasma in patients following treated acute HIV-1 infection occurs infrequently. Determination of whether early treatment leads to overall clinical benefit will require a larger and randomized clinical trial. These data may be relevant to current efforts to develop an HIV-1 vaccine designed to retard disease progression rather than prevent infection since they indicate that durable maintenance of low-level viremia may be difficult to achieve.
Automatic recognition of melanoma using Support Vector Machines: A study based on Wavelet, Curvelet and color features

This paper proposes an automated non-invasive system for skin cancer (melanoma) detection based on Support Vector Machine classification. The proposed system uses a number of features extracted from the Wavelet or the Curvelet decomposition of the grayscale skin lesion images and color features obtained from the original color images. The dataset used include both digital images and Dermoscopy images for skin lesions that are either benign or malignant. The recognition accuracy obtained by the Support Vector Machine classifier used in this experiment is 87.7. 1% for the Wavelet based features and 83.6. 6% for the Curvelet based ones. The proposed system also resulted in a sensitivity of 86.4 % for the case of Wavelet and 76.9% for the case of Curvelet. It also resulted in a specificity of 88.1% for the case of Wavelet and 85.4% for the case of Curvelet. The obtained sensitivity and specificity results are comparable to those obtained by Dermatologists.
Tree Kernels for Machine Translation Quality Estimation

This paper describes Uppsala University's submissions to the Quality Estimation (QE) shared task at WMT 2012. We present a QE system based on Support Vector Machine regression, using a number of explicitly defined features extracted from the Machine Translation input, output and models in combination with tree kernels over constituency and dependency parse trees for the input and output sentences. We confirm earlier results suggesting that tree kernels can be a useful tool for QE system construction especially in the early stages of system design.