Miracle-dz/UniFAR-FTUs huggingface dataset:
User-Centric Coordinates for Applications Leveraging 3-Axis Accelerometer Data

Mobile devices are becoming ubiquitous and, sometimes, even extensions of ourselves. These devices are growing fast in terms of delivered computational power, storage capacity, battery duration, and built-in sensors. Time and again, we see headlines advertising new unforeseen applications leveraging this power, especially the sensors, for solving diverse problems, including fall detection, user’s activity recognition, location identification, or even user authentication based on the way of walking (gait). In this paper, we focus on motion sensors and discuss how the provided data can be interpreted and transformed to better serve different purposes. We propose a method to process the data from such sensors that reduces the acquisition noise and possible artifacts, and turns the data invariant to the device’s position and the user’s movement direction. We introduce a new coordinate system referred to as user-centric, as opposed to the two most common coordinate systems used—the device and world-coordinate systems. For testing, we design and develop a user recognition system based on gait, in which the three coordinates systems are compared and discussed considering a one-versus-rest authentication setup. The results show the importance of properly pre-processing the acquired data to enable more reliable applications underpinned by mobile sensors.
Sensor placement tool for rapid development of video sensor layouts

The arrangement of video sensors -- in closed-circuit television (CCTV) systems, for instance -- can have drastic effects on the efficiency and cost of the final system. In the present work, I describe a tool designed for rapid construction of simulated video sensor layouts that allows quantification of sensor coverage and cost estimation to be determined prior to installation; thus, avoiding costly changes during or after the installation. Most previous work in this area either considers sensor coverage only in a 2D space or requires significant preparation to achieve accurate results in 3D. In the present work, I describe an implementation of a novel coverage-analysis algorithm that uses the geometry of image formation to cast rays from simulated video sensors through the monitored area to estimate sensor coverage at every 3D location. Visualization techniques of the acquired sensor coverage data are additionally presented.
Analysis of a sequential Monte Carlo optimization methodology

We investigate a family of stochastic exploration methods that has been recently proposed to carry out estimation and prediction in discrete-time random dynamical systems. The key of the novel approach is to identify a cost function whose minima provide valid estimates of the system state at successive time instants. This function is recursively optimized using a sequential Monte Carlo minimization (SMCM) procedure which is similar to standard particle filtering algorithms but does not require a explicit probabilistic model to be imposed on the system. In this paper, we analyze the asymptotic convergence of SMCM methods and show that a properly designed algorithm produces a sequence of system-state estimates with individually minimal contributions to the cost function. We apply the SMCM method to a target tracking problem in order to illustrate how convergence is achieved in the way predicted by the theory.
History, Dynamics, and Public Health Importance of Malaria Parasite Resistance

SUMMARY Despite considerable efforts, malaria is still one of the most devastating infectious diseases in the tropics. The rapid spread of antimalarial drug resistance currently compounds this grim picture. In this paper, we review the history of antimalarial drug resistance and the methods for monitoring it and assess the current magnitude and burden of parasite resistance to two commonly used drugs: chloroquine and sulfadoxine-pyrimethamine. Furthermore, we review the factors involved in the emergence and spread of drug resistance and highlight its public health importance. Finally, we discuss ways of dealing with such a problem by using combination therapy and suggest some of the research themes needing urgent answers.
Attribution requirements for next generation Internets

The notion of attribution is usually tied to identity: the ability to associate the originator of something with that data. This notion is both a simplification of the myriad aspects of attribution, and a masking of many different types of attribution under the rubric of “identity. ” Current efforts at attribution of data in the network focus either on traceback of packets or signing data at various network layers, and management of the keys involved. As the Internet evolves into the next generation network, and people implement testbeds to facilitate that evolution, they can design support mechanisms for other forms of attribution. What types of attribution should those mechanisms support? This paper presents requirements for attribution that will be useful both in the next generation infrastructure and in the data it manages.
Effects of BRCA1- and BRCA2-Related Mutations on Ovarian and Breast Cancer Survival: A Meta-analysis

Purpose: To estimate the effects of BRCA1 and BRCA2 mutations on ovarian cancer and breast cancer survival. Experimental Design: We searched PubMed and EMBASE for studies that evaluated the associations between BRCA mutations and ovarian or breast cancer survival. Meta-analysis was conducted to generate combined HRs with 95% confidence intervals (CI) for overall survival (OS) and progression-free survival (PFS). Results: From 1,201 unique citations, we identified 27 articles that compared prognosis between BRCA mutation carriers and noncarriers in patients with ovarian or breast cancer. Fourteen studies examined ovarian cancer survival and 13 studies examined breast cancer survival. For ovarian cancer, meta-analysis demonstrated that both BRCA1 and BRCA2 mutation carriers had better OS (HR, 0.76; 95% CI, 0.70–0.83 for BRCA1 mutation carriers; HR, 0.58; 95% CI, 0.50–0.66 for BRCA2 mutation carriers) and PFS (HR, 0.65; 95% CI, 0.52–0.81 for BRCA1 mutation carriers; HR, 0.61; 95% CI, 0.47–0.80 for BRCA2 mutation carriers) than noncarriers, regardless of tumor stage, grade, or histologic subtype. Among patients with breast cancer, BRCA1 mutation carriers had worse OS (HR, 1.50; 95% CI, 1.11–2.04) than noncarriers but were not significantly different from noncarriers in PFS. BRCA2 mutation was not associated with breast cancer prognosis. Conclusions: Our analyses suggest that BRCA mutations are robust predictors of outcomes in both ovarian and breast cancers and these mutations should be taken into account when devising appropriate therapeutic strategies. Clin Cancer Res; 21(1); 211–20. ©2014 AACR.
Adaptive Genetic Algorithms Based on Coevolution with Fuzzy Behaviors Adaptive Genetic Algorithms Based on Coevolution with Fuzzy Behaviors

Adaptive genetic algorithms dynamically adjust the genetic algorithm connguration during the course of evolving a problem solution in order to ooer an appropriate balance between exploration (overall search in the solution space) and exploitation (localized search in the promising regions discovered in that space). One promising way followed for building adaptive genetic algorithms involves the application of fuzzy logic controllers for tuning genetic algorithm control parameters. In this paper, a general model based on fuzzy logic controllers is presented for adapting parameters that control the application of any genetic operator. Our proposal is called coevolution with fuzzy behaviors. A fuzzy behavior is a vector with the linguistic values of the fuzzy rule consequent of a fuzzy logic controller, that encodes its fuzzy rule base. Control parameter values are computed for each set of parents that undergo the genetic operator, using a fuzzy logic controller that considers particular features associated with the parents as inputs. On the other hand, fuzzy behaviors (fuzzy rule bases) are implicitly learnt by means of an additional genetic algorithm that coevolves with the main one (coevolution). The goal of coevolution with fuzzy behaviors is to obtain fuzzy rule bases producing suitable control parameter values for allowing the genetic operator to show an adequate performance. In order to analyze the eeectiveness of the model, an instance is implemented for the adaptation of the fuzzy recombination, a crossover operator proposed for real-coded genetic algorithms. An empirical study of the instance is made from two diierent points of view, one of performance and one of adaptation itself (based on the distributions of fuzzy behaviors appearing during the runs). The results show that the instance has an adaptation ability which allows signiicant performance to be achieved for test functions with diierent diiculties.
CD 4 and CD 8 : modulators of T-cell receptor recognition of antigen and of immune responses ?

The response of T cells to antigen involves the participation of a number of distinct receptor-ligand engagements. The major players in the recognition of complexes of major histocompatibility complex molecules and peptide antigens are the T-cell receptors and the co-receptors CD4 and CDS. Progress in understanding the physical structures of these molecules, and how complexes between them are formed, is helping our understanding of how they participate in regulating the signals transduced to T cells.
A light-weighted software-controlled cache for PCM-based main memory systems

The replacement of DRAM with non-volatile memory relies on solutions to resolve the wear leveling and slow write problems. Different from the past work in compiler-assisted optimization or joint DRAM-PCM management strategies, we explore a light-weighted software-controlled DRAM cache design for the non-volatile-memory-based main memory. The run-time overheads in the management of the DRAM cache is minimized by utilizing the information from a miss of the translation lookaside buffer (TLB) or the cache. Experiments were conducted based on a series of the well-known benchmarks to evaluate the effectiveness of the proposed design, for which the results are very encouraging.
Adaptive internal model based formation control of a class of multi-agent systems with switched exosystems

In this article we consider a class of multi-agent dynamic systems with uncertainties and the problem of moving in a predefined formation along a reference trajectory. The reference trajectory is generated by switched exosystems each of which are neutrally stable and have unknown parameters. The problem is formulated as a nonlinear servomechanism problem and an adaptive internal model based controller is used to solve this problem. Various formation maneuvers such as expansion/contraction, rotation, and reconfiguration (change of shape) are also considered. The performance of the system is verified with simple numerical simulations.
Adaptive Neighborhood Selection Method Based on Silhouette Index for Locally Linear Embedding

Abstract Locally linear embedding (LLE) is a highly popular manifold learning and nonlinear dimensionality reduction technique. However, the neighborhood parameters of the algorithm are sensitive to the mapping results and difficult to choose. In this paper, we propose an adaptive neighborhood selection method based on Silhouette index for LLE algorithm. From the point of the cluster quality of feature extraction, Silhouette index is applied to evaluate the cluster effect of the low-dimensional embedding structure, in order to determine the optimal neighborhood parameters of LLE algorithm. Applied to IRIS and compressor failure data, the new method can effectively extract the inherent characteristics of low-dimensional manifolds, and obviously improve the classification performance of pattern recognition. The experimental results validate the feasibility and effectiveness of the proposed method.
Nanoparticle-based One-dimensional Photonic Crystals

Herein we present a fast, reliable method for building nanoparticle-based 1D photonic crystals in which a periodic modulation of the refractive index is built by alternating different types of nanoparticles and by controlling the level of porosity of each layer. The versatility of the method is further confirmed by building up optically doped photonic crystals in which the opening of transmission windows due to the creation of defect states in the gap is demonstrated. The potential of this new type of structure as a sensing material is illustrated by analyzing the specific color changes induced by the infiltration of solvents of different refractive indexes. Optical coatings made of several alternate layers of two different dielectric materials present brightly colored reflections arising from the interference of coherently scattered light waves. The frequency and width of the reflection is determined by the refractive index and the thickness of the layers in the film. Such structures, also known as Bragg reflectors or 1D photonic crystals, are commonly used in all branches of optics as precise frequency-selective filters or as antireflective coatings. The materials that the layers are usually made of are metal oxides with a large refractive index contrast between them, so an intense, wide reflectivity peak is attained just with a few alternate layers in the stack. SiO 2 and TiO 2 are two of the preferred materials for building these structures because they present very different dielectric constants and can be grown as highly uniform thin films using a wide variety of techniques such as pulsed laser deposition, reactive sputtering, or different types of chemical vapor deposition techniques. 1 Also, sol-gel methods combined with dip coating 2-4 or spin coating 5-7 have been employed because they allow one to obtain thin films with a large variety of compositions over wide area substrates in a simple and inexpensive way. In all of these examples, dense non-hygroscopic phases of the deposited coatings are sought after because performance stability under variable ambient conditions is usually desired. However, building multilayer structures whose optical response depends on the type of compound present in the environment might give rise to novel, interesting applications of such Bragg reflectors in the field of chemical sensing and detection. Unfortunately, the controlled response of a bulk material is difficult to attain unless precise control over both the composition and the mesostructure is achieved. The first realization of such structures has recently …
Early changes in muscle fiber size and gene expression in response to spinal cord transection and exercise

Dupont-Versteegden, Esther E. , John D. Houlé, Cathy M. Gurley, and Charlotte A. Peterson. Early changes in muscle fiber size and gene expression in response to spinal cord transection and exercise. Am. J. Physiol. 275 (Cell Physiol. 44): C1124–C1133, 1998. —Muscles of spinal cordtransected rats exhibit severe atrophy and a shift toward a faster phenotype. Exercise can partially prevent these changes. The goal of this study was to investigate early events involved in regulating the muscle response to spinal transection and passive hindlimb exercise. Adult female Sprague-Dawley rats were anesthetized, and a complete spinal cord transection lesion (T10) was created in all rats except controls. Rats were killed 5 or 10 days after transection or they were exercised daily on motor-driven bicycles starting at 5 days after transection and were killed 0.5, 1, or 5 days after the first bout of exercise. Structural and biochemical features of soleus and extensor digitorum longus (EDL) muscles were studied. Atrophy was decreased in all fiber types of soleus and in type 2a and type 2x fibers of EDL after 5 days of exercise. However, exercise did not appear to affect fiber type that was altered within 5 days of spinal cord transection: fibers expressing myosin heavy chain 2x increased in soleus and EDL, and extensive coexpression of myosin heavy chain in soleus was apparent. Activation of satellite cells was observed in both muscles of transected rats regardless of exercise status, evidenced by increased accumulation of MyoD and myogenin. Increased expression was transient, except for MyoD, which remained elevated in soleus. MyoD and myogenin were detected both in myofiber and in satellite cell nuclei in both muscles, but in soleus, MyoD was preferentially expressed in satellite cell nuclei, and in EDL, MyoD was more readily detectable in myofiber nuclei, suggesting that MyoD and myogenin have different functions in different muscles. Exercise did not affect the level or localization of MyoD and myogenin expression. Similarly, Id-1 expression was transiently increased in soleus and EDL upon spinal cord transection, and no effect of exercise was observed. These results indicate that passive exercise can ameliorate muscle atrophy after spinal cord transection and that satellite cell activation may play a role in muscle plasticity in response to spinal cord transection and exercise. Finally, the mechanisms underlying maintenance of muscle mass are likely distinct from those controlling myosin heavy chain expression.
Dual‐primal FETI methods for linear elasticity

Dual‐primal FETI methods are nonoverlapping domain decomposition methods where some of the continuity constraints across subdomain boundaries are required to hold throughout the iterations, as in primal iterative substructuring methods, while most of the constraints are enforced by Lagrange multipliers, as in one‐level FETI methods. These methods are used to solve the large algebraic systems of equations that arise in elliptic finite element problems. The purpose of this article is to develop strategies for selecting these constraints, which are enforced throughout the iterations, such that good convergence bounds are obtained that are independent of even large changes in the stiffness of the subdomains across the interface between them. The algorithms are described in terms of a change of basis that has proven to be quite robust in practice. A theoretical analysis is provided for the case of linear elasticity, and condition number bounds are established that are uniform with respect to arbitrarily large jumps in the Young's modulus of the material and otherwise depend only polylogarithmically on the number of unknowns of a single subdomain. The strategies have already proven quite successful in large‐scale implementations of these iterative methods. © 2006 Wiley Periodicals, Inc.
Joint Control to Improve Spatial Reuse for Mobile Ad Hoc Networks

Spatial reuse in a wireless network can allow multiple communications to proceed concurrently. Usually it seems that the more concurrent transmissions are allowed, the more the aggregate throughput can be sought. Unfortunately, to increase the level of spatial reuse, medium access control (MAC) protocols should either reduce the transmit power or increase the carrier sense (CS) threshold dynamically, which will make the SINR at the receiver decrease. With the decrease of SINR, the data rate sustained by each transmitter will also decrease, which counteracts the gain of simultaneously transmissions and leads to the degradation of network performance. The conflict among SINR and spatial reuse of the data rate suggests that there should be a tradeoff between them. In this paper, on the basis of theoretical analysis, we propose a dynamic joint control (JC) mechanism to adjust transmit power, sense threshold and data rate, based on the former transmit status, to increase the level of spatial reuse and improve performance in terms of aggregate throughput and energy efficiency.
Tight Variational Bounds via Random Projections and I-Projections

Information projections are the key building block of variational inference algorithms and are used to approximate a target probabilistic model by projecting it onto a family of tractable distributions. In general, there is no guarantee on the quality of the approximation obtained. To overcome this issue, we introduce a new class of random projections to reduce the dimensionality and hence the complexity of the original model. In the spirit of random projections, the projection preserves (with high probability) key properties of the target distribution. We show that information projections can be combined with random projections to obtain provable guarantees on the quality of the approximation obtained, regardless of the complexity of the original model. We demonstrate empirically that augmenting mean field with a random projection step dramatically improves partition function and marginal probability estimates, both on synthetic and real world data.
Impact of intensive lifestyle and metformin therapy on cardiovascular disease risk factors in the diabetes prevention program.

OBJECTIVE The Diabetes Prevention Program demonstrated the ability to delay or prevent type 2 diabetes in participants with impaired glucose tolerance (IGT). Participants with IGT are at high risk for cardiovascular disease (CVD), with a marked increase in the number and severity of CVD risk factors. We prospectively assessed the impact of our interventions on hypertension, dyslipidemia, and CVD events. RESEARCH DESIGN AND METHODS The study group consisted of 3,234 individuals with IGT randomly assigned to receive intensive lifestyle intervention, metformin, or placebo. Annual assessment of blood pressure, lipids, electrocardiogram, and CVD events was undertaken. RESULTS Hypertension was present in 30% of participants at study entry and then increased in the placebo and metformin groups, although it significantly decreased with intensive lifestyle intervention. Triglyceride levels fell in all treatment groups, but fell significantly more with intensive lifestyle intervention. Total cholesterol and LDL cholesterol levels were similar among treatment groups. Intensive lifestyle intervention significantly increased the HDL cholesterol level and reduced the cumulative incidence of the proatherogenic LDL phenotype B. At 3 years of follow-up, the use for pharmacologic therapy to achieve established goals in the intensive lifestyle group was 27-28% less for hypertension and 25% less for hyperlipidemia compared with placebo and metformin groups. Over an average of 3 years, 89 CVD events from 64 participants were positively adjudicated studywide, with no differences among treatment groups. CONCLUSIONS Lifestyle intervention improves CVD risk factor status compared with placebo and metformin therapy. Although no differences in CVD events were noted after 3 years, achieved risk factor modifications suggest that longer intervention may reduce CVD event rates.