Miracle-dz/UniFAR-FTUs huggingface dataset:
Multilingual Word Embeddings using Multigraphs

We present a family of neural-network--inspired models for computing continuous word representations, specifically designed to exploit both monolingual and multilingual text. This framework allows us to perform unsupervised training of embeddings that exhibit higher accuracy on syntactic and semantic compositionality, as well as multilingual semantic similarity, compared to previous models trained in an unsupervised fashion. We also show that such multilingual embeddings, optimized for semantic similarity, can improve the performance of statistical machine translation with respect to how it handles words not present in the parallel data.
Employment Networks and Mutual Fund Performance

This paper explores the role of social networks in the portfolio allocation decisions and performance of mutual fund managers. Using a novel dataset, I examine the past employment networks of both mutual fund managers and firm executives. I find that fund managers place larger bets and perform significantly better on firms when they are connected to a senior executive of that firm through overlapping employment networks. A portfolio of connected stocks outperforms a portfolio of unconnected stocks by over 10% per year. The results suggest that fund managers may be benefiting from improved information secured as a result of their networks. *I would like to thank my adviser Lauren Cohen, along with John Beshears, Richard Townsend, Jeremy Hoon, Zach Frankel, and an anonymous private wealth management adviser.
Prevalence of Fatigue Among Cancer Patients Receiving Various Anticancer Therapies and its Impact on Quality of Life: A Cross-Sectional Study

Background: Fatigue is disabling and continuous phenomenon in cancer patients during and after various anticancer treatments which can continue for many years after treatment and definitely it has profound effect on Quality of Life (QOL). However, determining its severity is still underestimated among the cancer patients and also very few studies in the literature exist reporting on Cancer-Related Fatigue (CRF) among Indian population. Aims: To find out the prevalence of rate of fatigue in cancer patient receiving various anti cancer therapies. To find out the relative impact of fatigue on QOL. Materials and Methods: This cross-sectional observational study included a total 121 cancer patients receiving radiotherapy, chemotherapy, and concurrent chemo-radiation with the age group of above 15 years who fulfilled the inclusion and exclusion criteria. All the patients were assessed for severity of fatigue using Brief Fatigue Inventory (BFI) and for QOL using FACT-G scale while they were receiving the anticancer therapies as an in-patient in the regional cancer centers in Madhya Pradesh, India. Results: The severe fatigue was more prevalent in chemotherapy [58/59 (98.30%)], and concurrent chemo-radiation (33/42 (78.57%)) as compared to radiotherapy (Moderate-9/20 (45%) and Severe-9/20 (45%)). Moderate correlations were exhibited between fatigue due to radiotherapy and QOL (r = -0.71, P < 0.01), whereas weak correlation was found between fatigue due to chemotherapy and concurrent chemo-radiation (r = -0.361, P < 0.01 and r = -0.453, P < 0.01, respectively). Conclusion: Severity of fatigue was found more after chemotherapy and concurrent chemo-radiation therapy while impact on QOL was more after the radiotherapy.
Experimental study and empirical modelling of magnetic abrasive finishing on ferromagnetic and non–ferromagnetic materials

Magnetic abrasive finishing is one of the advanced finishing processes where in the iron (Fe) particles form a flexible matrix in which the abrasives are trapped when magnetic field is applied. In the present paper, the magnetic flux densities (MFD) are measured at various distances from the outer surface to centre on ferromagnetic and non–ferromagnetic materials with approximately same hardness. MFD is about three times higher on ferromagnetic compared to non–ferromagnetic workpiece. Complete experimental study is carried out and empirically modelled. Best final Ra value about 20 nm and 60 nm is achieved on ferromagnetic and non–ferromagnetic stainless steel, respectively. With increase in abrasive mesh size and decrease in working gap, percentage change in Ra (%ΔRa) increases. Online measurement of forces is done using indigenously developed ring type dynamometer. Normal magnetic force is much higher in case of ferromagnetic than non–ferromagnetic workpiece material but no significant change is found in tangential cutting force.
Computing Convex Coverage Sets for Faster Multi-objective Coordination

In this article, we propose new algorithms for multi-objective coordination graphs (MO-CoGs). Key to the efficiency of these algorithms is that they compute a convex coverage set (CCS) instead of a Pareto coverage set (PCS). Not only is a CCS a sufficient solution set for a large class of problems, it also has important characteristics that facilitate more efficient solutions. We propose two main algorithms for computing a CCS in MO-CoGs. Convex multi-objective variable elimination (CMOVE) computes a CCS by performing a series of agent eliminations, which can be seen as solving a series of local multi-objective subproblems. Variable elimination linear support (VELS) iteratively identifies the single weight vector w that can lead to the maximal possible improvement on a partial CCS and calls variable elimination to solve a scalarized instance of the problem for w. VELS is faster than CMOVE for small and medium numbers of objectives and can compute an e-approximate CCS in a fraction of the runtime. In addition, we propose variants of these methods that employ AND/OR tree search instead of variable elimination to achieve memory efficiency. We analyze the runtime and space complexities of these methods, prove their correctness, and compare them empirically against a naive baseline and an existing PCS method, both in terms of memory-usage and runtime. Our results show that, by focusing on the CCS, these methods achieve much better scalability in the number of agents than the current state of the art.
Prediction of response to tapentadol in chronic low back pain

Many chronic low back pain (cLBP) patients do not satisfactorily respond to treatment. The knowledge of responders and non‐responders before initiating treatment would improve decision making and reduce health care costs. The aims of this exploratory prediction study in cLBP patients treated with tapentadol were to identify predictors of treatment outcome based on baseline characteristics, to evaluate quality‐of‐life and functionality as alternative outcome parameters and to develop nomograms to calculate the individual probability of response.
Workload Analysis of Blue Waters

Blue Waters is a Petascale-level supercomputer whose mission is to enable the national scientific and research community to solve "grand challenge" problems that are orders of magnitude more complex than can be carried out on other high performance computing systems. Given the important and unique role that Blue Waters plays in the U.S. research portfolio, it is important to have a detailed understanding of its workload in order to guide performance optimization both at the software and system configuration level as well as inform architectural balance tradeoffs. Furthermore, understanding the computing requirements of the Blue Water's workload (memory access, IO, communication, etc.), which is comprised of some of the most computationally demanding scientific problems, will help drive changes in future computing architectures, especially at the leading edge. With this objective in mind, the project team carried out a detailed workload analysis of Blue Waters.
Device and Circuit Performance of the Future Hybrid III–V and Ge-Based CMOS Technology

The device and circuit performance of a 20-nm gate length InGaAs and Ge hybrid CMOS based on an implant free quantum well (QW) device architecture is studied using a multiscale approach combining ensemble Monte Carlo simulation, drift-diffusion simulation, compact modeling, and TCAD mixed-mode circuit simulation. We have found that the QW and doped substrate, used in the hybrid CMOS, help to reduce shortchannel effects by enhancing carrier confinement. The QW also reduces the destructive impact of a low density of states in III-V materials. In addition, the calculated access resistance is found to be a much lower than in Si counterparts thanks to a heavily doped overgrowth source/drain contact. We predict an overall low gate capacitance and a large drive current when compared with Si-CMOS that leads to a significant reduction in a circuit propagation time delay (~5.5 ps).
Adolescent tobacco menace: Attitudes, norms, and parental influence

Background: Adolescence is a very delicate and vulnerable age when children are exposed to the harmful and damaging culture of the society. Tobacco habits are increasingly becoming an annoying menace to the Indian society since the past few years. The teenage group is fast catching up the tobacco habits because of its easy availability in the local Indian markets. Thus, this study was envisaged to analyze the factors responsible for this adverse habit and to obtain an overview of the trends in tobacco habits in young children of North India. Methods: Eight hundred and sixteen schoolchildren in the age group of 14–19 years of different schools of Meerut city were instructed to fill the prepared questionnaires. Results were formulated and statistical analysis was done. Results: Chi-square analysis revealed significant difference between tobacco users and nonusers. Smokeless habit was more prevalent among adolescent boys. Peer pressure was the most cited reason for initiating the tobacco habit while parental influence helped the most in abstaining from this adverse addiction. Conclusion: Despite the existence of anti-tobacco regulations in India, tobacco dependence in adolescents raises an alarm for the Indian community and stringent steps are required to remove this menace.
Statin Therapy Prescribing for Patients with Type 2 Diabetes Mellitus: A Review of Current Evidence and Challenges

Use of statin therapy in patients with type 2 diabetes mellitus (T2DM) has been recommended by most clinical guidelines. Cardiovascular disease (CVD) is a leading cause of morbidity and mortality among T2DM patients. It has been proved that statins are effective for primary or secondary CVD prophylaxis. Reports have highlighted the underutilization of statins in clinical practice and the suboptimal adherence to guideline recommendations. This review article points to summarize the current evidence confirming the role of statins in T2DM patients and to provide an overview of factors that may affect statins' prescribing patterns and compliance in clinical practice. Initiatives to enhance statin therapy prescribing should recognize the comprehensive nature of the prescribing process. Attempts to assure proper statin prescribing and utilization can help in achieving better clinical outcomes of statin therapy.
MARCKS is a major PKC-dependent regulator of calmodulin targeting in smooth muscle

Calmodulin (CaM) is a ubiquitous transducer of intracellular Ca2+ signals and plays a key role in the regulation of the function of all cells. The interaction of CaM with a specific target is determined not only by the Ca2+-dependent affinity of calmodulin but also by the proximity to that target in the cellular environment. Although a few reports of stimulus-dependent nuclear targeting of CaM have appeared, the mechanisms by which CaM is targeted to non-nuclear sites are less clear. Here, we investigate the hypothesis that MARCKS is a regulator of the spatial distribution of CaM within the cytoplasm of differentiated smooth-muscle cells. In overlay assays with portal-vein homogenates, CaM binds predominantly to the MARCKS-containing band. MARCKS is abundant in portal-vein smooth muscle (∼16 μM) in comparison to total CaM (∼40 μM). Confocal images indicate that calmodulin and MARCKS co-distribute in unstimulated freshly dissociated smooth-muscle cells and are co-targeted simultaneously to the cell interior upon depolarization. Protein-kinase-C (PKC) activation triggers a translocation of CaM that precedes that of MARCKS and causes multisite, sequential MARCKS phosphorylation. MARCKS immunoprecipitates with CaM in a stimulus-dependent manner. A synthetic MARCKS effector domain (ED) peptide labelled with a photoaffinity probe cross-links CaM in smooth-muscle tissue in a stimulus-dependent manner. Both cross-linking and immunoprecipitation increase with increased Ca2+ concentration, but decrease with PKC activation. Introduction of a nonphosphorylatable MARCKS decoy peptide blocks the PKC-mediated targeting of CaM. These results indicate that MARCKS is a significant, PKC-releasable reservoir of CaM in differentiated smooth muscle and that it contributes to CaM signalling by modulating the intracellular distribution of CaM.
Cysteine-3635 is responsible for skeletal muscle ryanodine receptor modulation by NO

We have shown previously that at physiologically relevant oxygen tension (pO2 ≈ 10 mmHg), NO S-nitrosylates 1 of ≈50 free cysteines per ryanodine receptor 1 (RyR1) subunit and transduces a calcium-sensitizing effect on the channel by means of calmodulin (CaM). It has been suggested that cysteine-3635 is part of a CaM-binding domain, and its reactivity is attenuated by CaM [Porter Moore, C. , Zhang, J. Z. , Hamilton, S. L. (1999) J. Biol. Chem. 274, 36831–36834]. Therefore, we tested the hypothesis that the effect of NO was mediated by C3635. The full-length RyR1 single-site C3635A mutant was generated and expressed in HEK293 cells. The mutation resulted in the loss of CaM-dependent NO modulation of channel activity and reduced S-nitrosylation by NO to background levels but did not affect NO-independent channel modulation by CaM or the redox sensitivity of the channel to O2 and glutathione. Our results reveal that different cysteines within the channel have been adapted to serve in nitrosative and oxidative responses, and that S-nitrosylation of the cysteine-containing CaM-binding domain underlies the mechanism of CaM-dependent regulation of RyR1 by NO.
Valproic acid potentiates curcumin-mediated neuroprotection in lipopolysaccharide induced rats

The etiology of neuroinflammation is complex and comprises multifactorial, involving both genetic and environmental factors during which diverse genetic and epigenetic modulations are implicated. Curcumin (Cur) and valproic acid (VPA), histone deacetylase 1 inhibitor, have neuroprotective effects. The present study was designed with an aim to investigate the ability of co-treatment of both compounds (Cur or VPA, 200 mg/kg) for 4 weeks to augment neuroprotection and enhance brain recovery from intra-peritoneal injection of (250 μg/kg) lipopolysaccharide-stimulated neuroinflammatory condition on rat brain cortex. Cortex activation and the effects of combined treatment and production of proinflammatory mediators, cyclooxygenase-2 (COX-2), APE1, and nitric oxide/inducible nitric oxide synthase (iNOS) were investigated. Neuroinflammation development was assessed by histological analyses and by investigating associated indices [β-secretase (BACE1), amyloid protein precursor (APP), presenilin (PSEN-1), and PSEN-2)]. Furthermore we measured the expression profile of lethal-7 (let-7) miRNAs members a, b, c, e, and f in all groups, a highly abundant regulator of gene expression in the CNS. Protein and mRNA levels of neuroinflammation markers COX-2, BACE1, APP, and iNOS were also attenuated by combined therapy. On the other hand, assessment of the indicated five let-7 members, showed distinct expression profile pattern in the different groups. Let-7 a, b, and c disappeared in the induced group, an effect that was partially suppressed by co-addition of either Cur or VPA. These data suggest that the combined treatment induced significantly the expression of the five members when compared to rats treated with Cur or VPA only as well as to self-recovery group, which indicates a possible benefit from the synergistic effect of Cur-VPA combination as therapeutic agents for neuroinflammation and its associated disorders. The mechanism elucidated here highlights the particular drug-induced expression profile of let-7 family as new targets for future pharmacological development.
The role of rpoS, hmp, and ssrAB in Salmonella enterica Gallinarum and evaluation of a triple-deletion mutant as a live vaccine candidate in Lohmann layer chickens

Salmonella enterica Gallinarum (SG) causes fowl typhoid (FT), a septicemic disease in avian species. We constructed deletion mutants lacking the stress sigma factor RpoS, the nitric oxide (NO)-detoxifying flavohemoglobin Hmp, and the SsrA/SsrB regulator to confirm the functions of these factors in SG. All gene products were fully functional in wild-type (WT) SG whereas mutants harboring single mutations or a combination of rpoS, hmp, and ssrAB mutations showed hypersusceptibility to H2O2, loss of NO metabolism, and absence of Salmonella pathogenicity island (SPI)-2 expression, respectively. A triple-deletion mutant, SGΔ3 (SGΔrpoSΔhmpΔssrAB), was evaluated for attenuated virulence and protection efficacy in two-week-old Lohmann layer chickens. The SGΔ3 mutant did not cause any mortality after inoculation with either 1 × 106 or 1 × 108 colony-forming units (CFUs) of bacteria. Significantly lower numbers of salmonellae were recovered from the liver and spleen of chickens inoculated with the SGΔ3 mutant compared to chickens inoculated with WT SG. Vaccination with the SGΔ3 mutant conferred complete protection against challenge with virulent SG on the chickens comparable to the group vaccinated with a conventional vaccine strain, SG9R. Overall, these results indicate that SGΔ3 could be a promising candidate for a live Salmonella vaccine against FT.
Length-bounded cuts and flows

For a given number <i>L</i>, an <i>L</i>-length-bounded edge-cut (node-cut, respectively) in a graph <i>G</i> with source <i>s</i> and sink <i>t</i> is a set <i>C</i> of edges (nodes, respectively) such that no <i>s</i>-<i>t</i>-path of length at most <i>L</i> remains in the graph after removing the edges (nodes, respectively) in <i>C</i>. An <i>L</i>-length-bounded flow is a flow that can be decomposed into flow paths of length at most <i>L</i>. In contrast to classical flow theory, we describe instances for which the minimum <i>L</i>-length-bounded edge-cut (node-cut, respectively) is Θ(<i>n</i><sup>2/3</sup>)-times (Θ(&sqrt;<i>n</i>)-times, respectively) larger than the maximum <i>L</i>-length-bounded flow, where <i>n</i> denotes the number of nodes; this is the worst case. We show that the minimum length-bounded cut problem is <i>NP</i>-hard to approximate within a factor of 1.1377 for <i>L</i>≥ 5 in the case of node-cuts and for <i>L</i>≥ 4 in the case of edge-cuts. We also describe algorithms with approximation ratio <i>O</i>(min{<i>L</i>,<i>n/L</i>}) ⊆ <i>O</i>&sqrt;<i>n</i> in the node case and <i>O</i>(min {<i>L</i>,<i>n</i><sup>2</sup>/<i>L</i><sup>2</sup>,&sqrt;<i>m</i>} ⊆ <i>O</i><sup>2/3</sup> in the edge case, where <i>m</i> denotes the number of edges. Concerning <i>L</i>-length-bounded flows, we show that in graphs with unit-capacities and general edge lengths it is <i>NP</i>-complete to decide whether there is a fractional length-bounded flow of a given value. We analyze the structure of optimal solutions and present further complexity results.
The force field of tourism 1

The literature on tourism development has focused on a one dimensional relationship between tourism development and quality of life. The impact of shock events on tourism development and quality of life seems ignored. Rather less attention has been paid to the multi-dimensional aspects of the relationship between tourism development and quality of life, and the potential impact of shock events on shaping this relationship. This study proposes a conceptual framework describing a triad of relations between tourism development (TD), quality of life (QoL) and shock events, and advocates that a bilateral relation exists between these three constructs. The framework also integrates three types of theories, each of which with the potential to explain tourism growth from a different perspective. The study analyzes a number of challenges facing tourism and discusses how these challenges interact and affect the interconnectedness between TD, QoL and shock events. The dynamic interplay of all these forces shapes tourism development patterns and will determine the nature of tourism development. The present study contributes to the tourism literature by identifying and structuring core elements that are responsible for the dynamics of tourism. In essence, it advances the understanding of this complex phenomenon, while providing building blocks for an anticipated view on the future of tourism.
STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation

Recently, significant improvement has been made on semantic object segmentation due to the development of deep convolutional neural networks (DCNNs). Training such a DCNN usually relies on a large number of images with pixel-level segmentation masks, and annotating these images is very costly in terms of both finance and human effort. In this paper, we propose a simple to complex (STC) framework in which only image-level annotations are utilized to learn DCNNs for semantic segmentation. Specifically, we first train an initial segmentation network called Initial-DCNN with the saliency maps of simple images (i.e., those with a single category of major object(s) and clean background). These saliency maps can be automatically obtained by existing bottom-up salient object detection techniques, where no supervision information is needed. Then, a better network called Enhanced-DCNN is learned with supervision from the predicted segmentation masks of simple images based on the Initial-DCNN as well as the image-level annotations. Finally, more pixel-level segmentation masks of complex images (two or more categories of objects with cluttered background), which are inferred by using Enhanced-DCNN and image-level annotations, are utilized as the supervision information to learn the Powerful-DCNN for semantic segmentation. Our method utilizes 40K simple images from Flickr.com and 10K complex images from PASCAL VOC for step-wisely boosting the segmentation network. Extensive experimental results on PASCAL VOC 2012 segmentation benchmark well demonstrate the superiority of the proposed STC framework compared with other state-of-the-arts.
Kernel Latent SVM for Visual Recognition

Latent SVMs (LSVMs) are a class of powerful tools that have been successfully applied to many applications in computer vision. However, a limitation of LSVMs is that they rely on linear models. For many computer vision tasks, linear models are suboptimal and nonlinear models learned with kernels typically perform much better. Therefore it is desirable to develop the kernel version of LSVM. In this paper, we propose kernel latent SVM (KLSVM) – a new learning framework that combines latent SVMs and kernel methods. We develop an iterative training algorithm to learn the model parameters. We demonstrate the effectiveness of KLSVM using three different applications in visual recognition. Our KLSVM formulation is very general and can be applied to solve a wide range of applications in computer vision and machine learning.