Miracle-dz/UniFAR-FTUs huggingface dataset:
Immersive virtual reality and affective computing for gaming, fear and anxiety management

Video game developers are enthusiastic about creating novel interaction approaches that yield a better gaming experience; such interactions are usually built with physical and emotional immersion in mind. Technologies such as Wii®, Kinect®, and Playstation Move® focus on the physical movement of play to encourage seamless and natural behaviors during gameplay. On the other hand, technologies such as biofeedback are not yet being utilized to any large degree in the commercial industry and could be used to gain further knowledge of player's behavior and emotions. Biofeedback refers to technologies that provide awareness of human physiological functions through signals in order to control a system or improve those functions. This technology was primarily developed for clinical purposes to treat diseases such as headaches, high blood pressure, and epilepsy. The patients obtain the skill to control functions associated with aforementioned diseases by being exposed to equipment that measures and displays their bodily functions such as brain waves, heart rate, and galvanic skin response (GSR). This enables them to observe those senses through visualization and exert control over their physiological response over time.
Economic Liberalization in India : Productivity and Learning-by-Export

In this paper, we examine whether firms become productive by learning through exporting. To this end, we estimate the production function by using micro data of Indian manufacturing firms operating in the period 1991-2001. In contrast to previous studies which focused more towards developed countries, we find strong evidence that Indian manufacturing firms are experiencing a rise in productivity in entering export markets, and thus the evidence of the learning effect. We also find that there is a productivity rise prior to exporting, which reinforces the evidence of learning effect. Our results also support the self-selection mechanism. JEL Classification:
Static Hedging of Standard Options

We consider the hedging of options when the price of the underlying asset is always exposed to the possibility of jumps of random size. Working in a single factor Markovian setting, we derive a new spanning relation between a given option and a continuum of shorter-term options written on the same asset. In this portfolio of shorter-term options, the portfolio weights do not vary with the underlying asset price or calendar time. We then implement this static relation using a finite set of shorter-term options and use Monte Carlo simulation to determine the hedging error thereby introduced. We compare this hedging error to that of a delta hedging strategy based on daily rebalancing in the underlying futures. The simulation results indicate that the two types of hedging strategies exhibit comparable performance in the classic Black-Scholes environment, but that our static hedge strongly outperforms delta hedging when the underlying asset price is governed by Merton (1976)'s jump-diffusion model. The conclusions are unchanged when we switch to ad hoc static and dynamic hedging practices necessitated by a lack of knowledge of the driving process. Further simulations indicate that the inferior performance of the delta hedge in the presence of jumps cannot be improved upon by increasing the rebalancing frequency. In contrast, the superior performance of the static hedging strategy can be further enhanced by using more strikes or by optimizing on the common maturity in the hedge portfolio. We also compare the hedging effectiveness of the two types of strategies using more than six years of data on S&P 500 index options. We find that in all cases considered, a static hedge using just five call options outperforms daily delta hedging with the underlying futures. The consistency of this result with our jump model simulations lends empirical support for the existence of jumps of random size in the movement of the S&P 500 index. We also find that the performance of our static hedge deteriorates moderately as we increase the gap between the maturity of the target call option and the common maturity of the call options in the hedge portfolio. We interpret this result as evidence of additional random factors such as stochastic volatility.
Examining the annealing schedules for RNA design algorithm

RNA structures are important for many biological processes in the cell. One important function of RNA are as catalytic elements. Ribozymes are RNA sequences that fold to form active structures that catalyze important chemical reactions. The folded structure for these RNA are very important; only specific conformations maintain these active structures, so it is very important for RNA to fold in a specific way. The RNA design problem describes the prediction of an RNA sequence that will fold into a given RNA structure. Solving this problem allows researchers to design RNA; they can decide on what folded secondary structure is required to accomplish a task, and the algorithm will give them a primary sequence to assemble. However, there are far too many possible primary sequence combinations to test sequentially to see if they would fold into the structure. Therefore we must employ heuristics algorithms to attempt to solve this problem. This paper introduces SIMARD, an evolutionary algorithm that uses an optimization technique called simulated annealing to solve the RNA design problem. We analyzes three different cooling schedules for the annealing process: 1) An adaptive cooling schedule, 2) a geometric cooling schedule, and 3) a geometric cooling schedule with warm up. Our results show that an adaptive annealing schedule may not be more effective at minimizing the Hamming distance between the target structure and our folded sequence's structure when compared with geometric schedules. The results also show that warming up in a geometric cooling schedule may be useful for optimizing SIMARD.
Probabilistic plane fitting in 3D and an application to robotic mapping

This work presents a method for probabilistic plane fitting and an application to robotic 3D mapping. The plane is fitted in an orthogonal least-square sense and the output complies with the conventions of the symmetries and perturbation model (SPmodel). In the second part of the paper, the presented plane fitting method is used within a 3D mapping application. It is shown that by using probabilistic information, high precision 3D maps can be generated.
Coefficient-Free Blood Pressure Estimation Based on Pulse Transit Time–Cuff Pressure Dependence

Oscillometry is a popular technique for automatic estimation of blood pressure (BP). However, most of the oscillometric algorithms rely on empirical coefficients for systolic and diastolic pressure evaluation that may differ in various patient populations, rendering the technique unreliable. A promising complementary technique for automatic estimation of BP, based on the dependence of pulse transit time (PTT) on cuff pressure (CP) (PTT-CP mapping), has been proposed in the literature. However, a theoretical grounding for this technique and a nonparametric BP estimation approach are still missing. In this paper, we propose a novel coefficient-free BP estimation method based on PTT-CP dependence. PTT is mathematically modeled as a function of arterial lumen area under the cuff. It is then analytically shown that PTT-CP mappings computed from various points on the arterial pulses can be used to directly estimate systolic, diastolic, and mean arterial pressure without empirical coefficients. Analytical results are cross-validated with a pilot investigation on ten healthy subjects where 150 simultaneous electrocardiogram and oscillometric BP recordings are analyzed. The results are encouraging whereby the mean absolute errors of the proposed method in estimating systolic and diastolic pressures are 5.31 and 4.51 mmHg, respectively, relative to the Food and Drug Administration approved Omron monitor. Our work thus shows promise toward providing robust and objective BP estimation in a variety of patients and monitoring situations.
Recognizing Speech with Anthropomorphic Models For Voice Synthesis Application to Humanoid Robotics

In order to emulate in robots the speech production and learning capabilities of human infants, exploratory strategies in articulatory synthesizers have been proposed for the creation of acoustic to motor associations. However, commonly used articulatory speech synthesis models are based on an unconstrained modeling of the physiology of the human vocal tract which contain many redundant parameters. In this thesis we show that vocalic speech requires, in fact, a very reduced number of parameters and, based on well-established linguistic knowledge, propose a two-dimensional articulatory space for VTCalcs articulatory synthesizer.The proposed space is generated through the convex combination of prototype vowels representing vocal tract extremal configurations. We also propose a speech learning architecture that can integrate unsupervised classification for vocalic speech sounds of a given language. This provides a low-dimensional and intuitive vowel production space, suited for automatic production, recognition and learning of speech in articulatory models.
Adjuvant and mitogenic properties of a supernatant fraction of sonically treated Myobacterium bovis (BCG)

A water-soluble, oil-free supernatant fraction of sonically treated BCG (BCG-SS) was shown to be an immunological adjuvant and a mitogen. When BCG-SS and sheep erythrocytes (SRBC) were injected intravenously into mice, the plaque-forming cell (PFC) response was 10 times greater than that induced by injection of SRBC alone. Circulating antibody responses to SRBC and to bovine serum albumin were also enhanced by BCG-SS. The in vitro enhancement of PFC and circulating antibody responses did not require mineral oil or exogenous lipids. In vitro PFC responses by normal mouse spleen cells were also greatly increased in the presence of BCG-SS. Anti-theta serum-treated spleen cells from mice that had been lethally irradiated and reconstituted with normal bone marrow cells also gave a higher PFC response to SRBC in the presence of BCG-SS. This suggests that BCG-SS can stimulate B lymphocytes to develop an immune response when T lymphocytes are severely depleted or absent. BCG-SS also stimulated the uptake of 125IUdR by normal spleen cell cultures, indicating that it is a mitogen.
Characterizing Stiffness and Strength Properties of Glass-Fiber Reinforced, Hollow-Cored Recycled Plastic Extrusions

Hollow-cored structural profiles have been processed using blends of polyethylene with continuous glass-fiber roving reinforcement through an offset extrusion process. These profiles have been produced with and without coupling agents. The intent has been to achieve high bending stiffness (El) by locating the reinforcing fibers in the mid-plane of the extrusion walls. However, the process was not optimally tuned which results in the fibers being nearer the inner surfaces of extrusion walls. Four-point bending and compression tests have been performed on the hollow-cored beams according to ASTM D6109 and ASTM D61 12. The nonlinear elastic nature of the unreinforced plastic blends has been quantified by performing tensile tests according to ASTM D638. A finite element-based computational model has been developed through the assembly of "repeating cells" representing individual fiber rovings and neighboring plastic. Comparing experimental and computational results demonstrates the validity of the model in predicting bending stiffness (El). Furthermore, simple multi-material beam models are shown to over-predict bending stiffness. The experiments also provide evidence suggesting shear failure at fiber-matrix interfaces.
Scale-based adaptive smoothing

A novel adaptive smoothing approach is proposed for noise removal and feature preservation where the local object scale is adopted to detect the size of the local structure. Its main idea is: the variance and mask size of the Gaussian filter are controlled by the local object scale. There are two main contributions in this paper. First one, we change a crucial parameter in the definition of object scale in previous reference in order to reflect the real size of object in visible light image very well. Second one, we introduce the median filter for removing the isolated large gradient noise points which interfere with the calculation of object scale greatly. Both subjective and objective comparisons demonstrate that our method can preserve the fine details while suppress noise, and is superior to the several traditional image smoothing algorithms.
A Smartphone-based Medication Self-management System with Real-time Medication Monitoring

Summary Background: Most patients cannot remember their entire medication regimen and occasionally forget to take their medication. Objectives: The objective of the study was to design, develop, and demonstrate the feasibility of a new type of medication self-management system using smartphones with real-time medication monitoring. Methods: We designed and developed a smartphone-based medication self-management system (SMSS) based on interviews of 116 patients. The system offered patients two main functions by means of smartphones: (1) storage and provision of an accurate, portable medication history and medication-taking records of patients; and (2) provision of a reminder to take medication only when the patient has forgotten to take his/her medication. These functions were realized by two data input methods: (a) reading of prescription data represented in two-dimensional barcodes using the smartphone camera and getting the photographic images of the pills; and (b) real-time medication monitoring by novel user-friendly wireless pillboxes. Results: Interviews suggested that a pocket-sized pillbox was demanded to support patient’s medication-taking outside the home and pillboxes for home use should be adaptable to the different means of pillbox storage. In accordance with the result, we designed and developed SMSS. Ten patients participated in the feasibility study. In 17 out of 47 cases (36.2%), patients took their medication upon being presented with reminders by the system. Correct medication-taking occur-rence was improved using this system. Conclusions: The SMSS is acceptable to patients and has the advantage of supporting ubiquitous medication self-management using a smartphone. We believe that the proposed system is feasible and provides an innovative solution to encourage medication self-management. Citation: Hayakawa M, Uchimura Y, Omae K, Waki K, Fujita H, Ohe K. A smartphone-based medication selfmanagement system with real-time medication monitoring. Appl Clin Inf 2013; 4: 37–52 http://dx.doi. org/10.4338/ACI-2012-10-RA-0045
Similarity Search in High Dimensions via Hashing

The nearestor near-neighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search/index structures for performing similarity search over high-dimensional data, e.g., image databases, document collections, time-series databases, and genome databases. Unfortunately, all known techniques for solving this problem fall prey to the \curse of dimensionality. " That is, the data structures scale poorly with data dimensionality; in fact, if the number of dimensions exceeds 10 to 20, searching in k-d trees and related structures involves the inspection of a large fraction of the database, thereby doing no better than brute-force linear search. It has been suggested that since the selection of features and the choice of a distance metric in typical applications is rather heuristic, determining an approximate nearest neighbor should su ce for most practical purposes. In this paper, we examine a novel scheme for approximate similarity search based on hashing. The basic idea is to hash the points Supported by NAVY N00014-96-1-1221 grant and NSF Grant IIS-9811904. Supported by Stanford Graduate Fellowship and NSF NYI Award CCR-9357849. Supported by ARO MURI Grant DAAH04-96-1-0007, NSF Grant IIS-9811904, and NSF Young Investigator Award CCR9357849, with matching funds from IBM, Mitsubishi, Schlumberger Foundation, Shell Foundation, and Xerox Corporation. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 25th VLDB Conference, Edinburgh, Scotland, 1999. from the database so as to ensure that the probability of collision is much higher for objects that are close to each other than for those that are far apart. We provide experimental evidence that our method gives signi cant improvement in running time over other methods for searching in highdimensional spaces based on hierarchical tree decomposition. Experimental results also indicate that our scheme scales well even for a relatively large number of dimensions (more than 50).
Increased DNA Damage Sensitivity and Apoptosis in Cells Lacking the Snf5/Ini1 Subunit of the SWI/SNF Chromatin Remodeling Complex

ABSTRACT The gene encoding the SNF5/Ini1 core subunit of the SWI/SNF chromatin remodeling complex is a tumor suppressor in humans and mice, with an essential role in early embryonic development. To investigate further the function of this gene, we have generated a Cre/lox-conditional mouse line. We demonstrate that Snf5 deletion in primary fibroblasts impairs cell proliferation and survival without the expected derepression of most retinoblastoma protein-controlled, E2F-responsive genes. Furthermore, Snf5-deficient cells are hypersensitive to genotoxic stress, display increased aberrant mitotic features, and accumulate phosphorylated p53, leading to elevated expression of a specific subset of p53 target genes, suggesting a role for Snf5 in the DNA damage response. p53 inactivation does not rescue the proliferation defect caused by Snf5 deficiency but reduces apoptosis and strongly accelerates tumor formation in Snf5-heterozygous mice.
Less frequent eating predicts greater BMI and waist circumference in female adolescents.

BACKGROUND Little is known about the effect of eating frequency on adiposity. OBJECTIVE The study aim was to assess the prospective relation of an objective measure of eating frequency with adiposity in girls from ages 9-10 to 19-20 y. DESIGN By using data from 3-d diet records collected from 2372 girls in the National Heart, Lung, and Blood Institute Growth and Health Study, meal, snack, and total eating frequencies aggregated over the first 2 study years were examined in relation to 10-y change in BMI and waist circumference (WC). RESULTS Eating frequency was lower in black and older girls than in white and younger girls (P < 0.0001). In whites, lower initial snack and total eating frequencies were related to greater 10-y increases in BMI (P = 0.023 and 0.012, respectively) and WC (P = 0.030 and 0.015, respectively). In blacks, lower initial meal and snack frequencies were related to greater increases in BMI (P = 0.004 and 0.022, respectively) and WC (P = 0.052 and 0.005, respectively). Also, in blacks, lower initial total eating frequency was related to greater increases in WC (P = 0.010). After adjustment for baseline adiposity measure, race, parental education, physical activity, television and video viewing, total energy intake, and dieting for weight loss, lower initial total eating frequency remained related to greater 10-y increases in BMI (P = 0.013) and WC (P = 0.036). CONCLUSIONS A lower eating frequency predicts a greater gain in adiposity in adolescent females. Intervention trials are needed to test if changing the frequency of eating can affect obesity risk.
Using Magpie for Request Extraction and Workload Modelling

Tools to understand complex system behaviour are essential for many performance analysis and debugging tasks, yet there are many open research problems in their development. Magpie is a toolchain for automatically extracting a system's workload under realistic operating conditions. Using low-overhead instrumentation, we monitor the system to record fine-grained events generated by kernel, middleware and application components. The Magpie request extraction tool uses an application-specific event schema to correlate these events, and hence precisely capture the control flow and resource consumption of each and every request. By removing scheduling artefacts, whilst preserving causal dependencies, we obtain canonical request descriptions from which we can construct concise workload models suitable for performance prediction and change detection. In this paper we describe and evaluate the capability of Magpie to accurately extract requests and construct representative models of system behaviour.
Managing and Visualizing Citation Network Using Graph Database and LDA Model

In this paper, a solution of storing and inferring on citation network using graph database and topic model is proposed. Citation network is a very large directed graph containing nodes and edges. Each node is a paper and each directed edge is a link between paper and its citing papers. In citation network analysis, each node usually contains basic properties of paper such as paper ID, publication year, paper title, authors. In this research, we propose one more property called "topic vector". This property contains topic distribution of a specific paper which is gained by LDA algorithm. After that, we propose a new approach to store and manage the citation network using graph database. Finally, we use the graph query language to develop some functions of citation network analysis and visualize topic propagation through the network. We also compare our approach with the traditional method in which the relational database is used to store and manage citation network. Experimental results show that the performance of our approach is higher than the traditional one and a different view of citation network analysis is also discussed.
Event Detection and Analysis from Video Streams

We present a system which takes as input a video stream obtained from an airborne moving platform and produces an analysis of the behavior of the moving objects in the scene. To achieve this functionality, our system relies on two modular blocks. The first one detects and tracks moving regions in the sequence. It uses a set of features at multiple scales to stabilize the image sequence, that is, to compensate for the motion of the observer, then extracts regions with residual motion and uses an attribute graph representation to infer their trajectories. The second module takes as input these trajectories, together with user-provided information in the form of geospatial context and goal context to instantiate likely scenarios. We present details of the system, together with results on a number of real video sequences and also provide a quantitative analysis of the results.
Balancing Training Data for Automated Annotation of Keywords: a Case Study

There has been an increasing interest in tools for automating the annotation of databases. Machine learning techniques are promising candidates to help curators to, at least, guide the process of annotation which is mostly done manually. Following previous works on automated annotation using symbolic machine learning techniques, the present work deals with a common problem in machine learning: that classes usually have skewed class prior probabilities, i.e., there is a large number of examples of one class compared with just few examples of the other class. This happens due to the fact that a large number of proteins is not annotated for every feature. Thus, we analyze and employ some techniques aiming at balancing the training data. Our experiments show that the classifiers induced from balanced data sampled with our method are more accurate than those induced from the original data.