Miracle-dz/UniFAR-FTUs huggingface dataset:
Attentional Bias for Reward and Punishment in Overweight and Obesity: The TRAILS Study

More than 80% of obese adolescents will become obese adults, and it is therefore important to enhance insight into characteristics that underlie the development and maintenance of overweight and obesity at a young age. The current study is the first to focus on attentional biases towards rewarding and punishing cues as potentially important factors. Participants were young adolescents (N = 607) who were followed from the age of 13 until the age of 19, and completed a motivational game indexing the attentional bias to general cues of reward and punishment. Additionally, self-reported reward and punishment sensitivity was measured. This study showed that attentional biases to cues that signal reward or punishment and self-reported reward and punishment sensitivity were not related to body mass index or the change in body mass index over six years in adolescents. Thus, attentional bias to cues of reward and cues of punishment, and self-reported reward and punishment sensitivity, do not seem to be crucial factors in the development and maintenance of overweight and obesity in adolescents. Exploratory analyses of the current study suggest that the amount of effort to gain reward and to avoid punishment may play a role in the development and maintenance of overweight and obesity. However, since the effort measure was a construct based on face validity and has not been properly validated, more studies are necessary before firm conclusions can be drawn.
Bayesian 3D independent motion segmentation with IMU-aided RBG-D sensor

In this paper we propose a two-tiered hierarchical Bayesian model to estimate the location of objects moving independently from the observer. Biological vision systems are very successful in motion segmentation, since they efficiently resort to flow analysis and accumulated prior knowledge of the 3D structure of the scene. Artificial perception systems may also build 3D structure maps and use optical flow to provide cues for ego- and independent motion segmentation. Using inertial and magnetic sensors and an image and depth sensor (RGB-D) we propose a method to obtain registered 3D maps, which are subsequently used in a probabilistic model (the bottom tier of the hierarchy) that performs background subtraction across several frames to provide a prior on moving objects. The egomotion of the RGB-D sensor is estimated starting with the angular pose obtained from the filtered accelerometers and magnetic data. The translation is derived from matched points across the images and corresponding 3D points in the rotation-compensated depth maps. A gyro-aided Lucas Kanade tracker is used to obtain matched points across the images. The tracked points can also used to refine the initial sensor based rotation estimation. Having determined the camera egomotion, the estimated optical flow assuming a static scene can be compared with the observed optical flow via a probabilistic model (the top tier of the hierarchy), using the results of the background subtraction process as a prior, in order to identify volumes with independent motion in the corresponding 3D point cloud. To deal with the computational load CUDA-based solutions on GPUs were used. Experimental results are presented showing the validity of the proposed approach.
Minimum-BER Power Allocation for Multicarrier Systems with Outdated Channel State Information

Multicarrier transmission systems can effectively exploit the frequency selectiveness of wireless multipath fading channels. With the perfect knowledge of the channel state information (CSI) at the transmitter, the spectral efficiency and/or link reliability of multicarrier systems can be further improved with proper power or bit loading. In this paper, we examine the effect of outdated CSI on the performance of power allocation schemes that target at minimizing the system BER. Based on a well-established correlation model between the feedbacked subchannel gains and the actual subchannel gains, we evaluate the performance loss of i) power allocation solely based on the outdated CSI and ii) power allocation based on outdated CSI as well as the outdatedness of CSI cases.
Self-reliance inventory versi bahasa Indonesia: Adaptasi alat ukur

Self-Reliance Inventory merupakan salah satu alat ukur rasa kemandirian yang banyak digunakan dalam penelitian mengenai stres kerja. Sejauh ini belum ditemukan adaptasi Self-Reliance Inventory dalam bahasa Indonesia, meskipun alat ukur tersebut telah dikembangkan menjadi 2 versi (2 indikator dan 3 indikator). Adaptasi Self-Reliance Inventory diperlukan karena selama kurun waktu 10 tahun terakhir penelitian yang mengukur konstruk tersebut menggunakan alat ukur yang kurang spesifik pada rasa kemandirian di tempat kerja. Self-Reliance Inventory versi 2 indikator (tidak ketergantungan dan ketergantungan) terdiri dari 20 item. Dalam penelitian ini, alat ukur tersebut diadaptasi melalui 6 tahap berdasarkan panduan adaptasi alat ukur lintas budaya. Berdasarkan uji coba terhadap 50 karyawan di Indonesia, diperoleh nilai koefisien reliabilitas sebesar 0,65 pada indikator tidak ketergantungan dan sebesar 0,75 pada indikator ketergantungan. Dengan demikian, hasil adaptasi alat ukur rasa kemandirian dapat digunakan untuk penelitian mendatang guna memprediksi berbagai variabel seperti stres kerja, intensi turn over, tekanan kerja, serta organizational citizenship behavior pada karyawan di Indonesia.
Sequence Mining Without Sequences: A New Way for Privacy Preserving

During the last decade, sequential pattern mining has been the core of numerous researches. It is now possible to efficiently discover users' behavior in various domains such as purchases in supermarkets, Web site visits, etc. Nevertheless, classical algorithms do not respect individual's privacy, exploiting personal information (name, IP address, etc.). We provide an original solution to privacy preserving by using a probabilistic automaton instead of the original data. An application in car flow modeling is presented, showing the ability of our algorithm to discover frequent routes without any individual information. A comparison with SPAM is done showing that even if we sample from the automaton, our approach is more efficient
Optimizing Operation Scheduling for In-Memory Databases

A Computer system is a sophisticated combination of software and hardware components that work closely together to solve today’s problems. While algorithms within a software component itself can be implemented and optimized by a small group of developers, algorithms for an entire software stack are more complex and their development gets expensive. To speed up the development of in-memory database specific scheduling algorithms for operating systems, we implemented a simulation model based on SAP’s TREX in-memory column database. This simulation is used to optimize query scheduling and execution algorithms without the necessity of modifying every component within the existing system. To quantify the advantages and drawbacks of the different scheduling algorithms, we use the Star Schema Benchmark to measure the cost reduction per user with regards to the implemented operating system scheduling
Personalized medicine in deep brain stimulation through utilization of neural oscillations

The neural underpinnings for normal and abnormal basal ganglia functions and functional connectivities remain unknown, although several important pieces to the puzzle have emerged over 3 decades. First was the identification of segregated basal ganglia circuits.1 This discovery was closely paralleled by the realization that these circuits communicated through the use of a special physiologic language,2 which proved to be more complex than initially thought, as it was not the simple firing rate of neurons, but rather specific patterns of oscillatory neuronal discharges that were …
Predicting the Minimal Translation Apparatus: Lessons from the Reductive Evolution of Mollicutes

Mollicutes is a class of parasitic bacteria that have evolved from a common Firmicutes ancestor mostly by massive genome reduction. With genomes under 1 Mbp in size, most Mollicutes species retain the capacity to replicate and grow autonomously. The major goal of this work was to identify the minimal set of proteins that can sustain ribosome biogenesis and translation of the genetic code in these bacteria. Using the experimentally validated genes from the model bacteria Escherichia coli and Bacillus subtilis as input, genes encoding proteins of the core translation machinery were predicted in 39 distinct Mollicutes species, 33 of which are culturable. The set of 260 input genes encodes proteins involved in ribosome biogenesis, tRNA maturation and aminoacylation, as well as proteins cofactors required for mRNA translation and RNA decay. A core set of 104 of these proteins is found in all species analyzed. Genes encoding proteins involved in post-translational modifications of ribosomal proteins and translation cofactors, post-transcriptional modifications of t+rRNA, in ribosome assembly and RNA degradation are the most frequently lost. As expected, genes coding for aminoacyl-tRNA synthetases, ribosomal proteins and initiation, elongation and termination factors are the most persistent (i.e. conserved in a majority of genomes). Enzymes introducing nucleotides modifications in the anticodon loop of tRNA, in helix 44 of 16S rRNA and in helices 69 and 80 of 23S rRNA, all essential for decoding and facilitating peptidyl transfer, are maintained in all species. Reconstruction of genome evolution in Mollicutes revealed that, beside many gene losses, occasional gains by horizontal gene transfer also occurred. This analysis not only showed that slightly different solutions for preserving a functional, albeit minimal, protein synthetizing machinery have emerged in these successive rounds of reductive evolution but also has broad implications in guiding the reconstruction of a minimal cell by synthetic biology approaches.
Evolutionary Algorithm With Experimental Design Technique

Major steps in evolutionary algorithms involve sampling point from a space. Most of the existing evolutionary algorithms adopt random sampling. Experimental design technique is a sophisticated sampling method. This paper introduces a crossover based on orthogonal array design. The proposed crossover can estimate the best point in the space determined by the parents. The experimental results show that the algorithm with this crossover is effective for optimization problems.
A pure probabilistic approach to range-only SLAM

Range-only SLAM (RO-SLAM) represents a difficult problem due to the inherent ambiguity of localizing either the robot or the beacons from distance measurements only. Most previous approaches to this problem employ non-probabilistic batch optimizations or delay the initialization of new beacons within a probabilistic filter until a good estimate is available. The contribution of this work is the formulation of RO-SLAM as an online Bayesian estimation process based on a Rao-Blackwellized particle filter. The conditional distribution for each beacon is initialized using an additional particle filter which, eventually, is transformed into an extended Kalman filter when the uncertainty becomes sufficiently small. This approach allows the introduction of new beacons without either delay or any special non-probabilistic processing. We validate our proposal with experiments for both simulated and real datasets.
Design of a hexapod robotic system

The main purpose of this work was to design a prototype of an autonomous hexapod robot. This paper reports on an initial phase, where the basic geometry of the system was specified and improved through a kinematics and dynamic study, using a motion analysis software. This also allowed the design of all mechanical components and the definition of motion generation needs. In this paper the importance of legged robots on mobile research is emphasised. The capabilities of the computational programs specially dedicated to the analysis of mechanical systems are demonstrated. The mobility of the geometric model presented in this paper is a trade-off between natural idea and technical feasibility. Some results of the virtual simulation of the movement of this hexapod robotic system are presented.
Multiclass Boosting for Weak Classifiers

AdaBoost.M2 is a boosting algorithm designed for multiclass problems with weak base classifiers. The algorithm is designed to minimize a very loose bound on the training error. We propose two alternative boosting algorithms which also minimize bounds on performance measures. These performance measures are not as strongly connected to the expected error as the training error, but the derived bounds are tighter than the bound on the training error of AdaBoost.M2. In experiments the methods have roughly the same performance in minimizing the training and test error rates. The new algorithms have the advantage that the base classifier should minimize the confidence-rated error, whereas for AdaBoost.M2 the base classifier should minimize the pseudo-loss. This makes them more easily applicable to already existing base classifiers. The new algorithms also tend to converge faster than AdaBoost.M2.
Xregion: A structure-based approach to Storing XML Data in Relational Databases

In this paper, we propose a new structure-based approach, called Xregion, to store XML data in relational databases. Our approach first partitions an XML document into several disjoint regions according to the cardinality of element nodes, and then maps these regions into separate relations. Our experimental results demonstrate that the proposed approach dramatically improves the performance of queries on the XML data over the existing approaches.
Telomere-dependent and telomere-independent origins of endogenous DNA damage in tumor cells

Human tumors and cultured cells contain elevated levels of endogenous DNA damage resulting from telomere dysfunction, replication and transcription errors, reactive oxygen species, and genome instability. However, the contribution of telomere-associated versus telomere-independent endogenous DNA lesions to this damage has never been examined. In this study, we characterized the relative amounts of these two types of DNA damage in five tumor cell lines by noting whether γ-H2AX foci, generally considered to mark DNA double-strand breaks (DSBs), were on chromosome arms or at chromosome ends. We found that while the numbers of non-telomeric DSBs were remarkably similar in these cultures, considerable variation was detected in the level of telomeric damage. The distinct heterogeneity in the numbers of γ-H2AX foci in these tumor cell lines was found to be due to foci associated with uncapped telomeres, and the amount of total telomeric damage also appeared to inversely correlate with the telomerase activity present in these cells. These results indicate that damaged telomeres are the major factor accounting for the variability in the amount of DNA DSB damage in tumor cells. This characterization of DNA damage in tumor cells helps clarify the contribution of non-telomeric DSBs and damaged telomeres to major genomic alterations.
Model Building in Two-sphere via Gauss-Weierstrass Kernel Smoothing and Its Application to Cortical Analysis , Part II

We present a new tensor-based morphometric framework that quantifies cortical shape variations via the concept of local area element. The local area element is obtained from the Riemannian metric tensors, which are, in turn, obtained from the smooth functional parametrization of a triangle cortical mesh. For the smooth parametrization, we have developed a novel weighted spherical harmonic (SPHARM) representation. The weighted-SPHARM differs from the classical SPHARM in a regularizing cost function. The classical SPHARM is the asymptotic limit of the weightedSPHARM. Further, for a specific choice of weights, the weighted-SPHARM is shown to be the finite least squares approximation to the solution of an isotropic heat diffusion on a unit sphere. The main aims of this paper are to present a theoretical framework for the weighted-SPHARM, and to show how it can be used in the tensor-based morphometry. As an illustration, the methodology has been applied in the problem of detecting abnormal cortical regions in a clinical population.
Fully Dynamic Connectivity in O(log n(log log n)2) Amortized Expected Time

Dynamic connectivity is one of the most fundamental problems in dynamic graph algorithms. We present a randomized Las Vegas dynamic connectivity data structure with $O(\log n(\log\log n)^2)$ amortized expected update time and $O(\log n/\log\log\log n)$ worst case query time, which comes very close to the cell probe lower bounds of Patrascu and Demaine (2006) and Patrascu and Thorup (2011).
Modeling Languages: Syntax, Semantics and All That Stuff, Part I: The Basic Stuff

The motivation for this paper, the first in a planned series of three parts, is the multitude of concepts surrounding the proper definition of complex modeling languages for systems and software, and the confusion that this often causes. Particularly relevant is the case of the recently standardized UML, which we refer to quite extensively as we proceed. Our intention is to discuss and clarify the notions involved in defining modeling languages. The main theme is the distinction between syntax and semantics, the nature and purpose of each, their usage and style, and the various means for defining and dealing with them. Underlying the exposition are the dichotomies of textual vs. visual languages, structural vs. behavioral specification, and requirements vs. system models. We hope that the paper will be useful to language designers, methodologists, tool vendors and educators.
Scheduling Wireless Links with Successive Interference Cancellation

In this paper we study the problem of scheduling wireless links in a model where successive interference cancellation is combined with the traditional physical interference model. Successive interference cancellation is based on the observation that interfering signals should not be treated as random noise, but as well-structured signals. By exploiting this structured nature, the strongest signal can be decoded and subtracted from a collision, thus enabling the decoding of weaker simultaneous signals. The procedure can be repeated iteratively as long as the collided signals differ in strength significantly. It has been shown that the problem of scheduling wireless links with successive interference cancellation is NP-hard. In this work, we propose a polynomial-time scheduling algorithm that uses successive interference cancellation to compute short schedules for network topologies formed by nodes arbitrarily distributed in the Euclidean plane. We prove that the proposed algorithm is correct in the physical interference model and provide simulation results demonstrating the performance of the algorithm in different network topologies. We compare the results to solutions without successive interference cancellation and observe that throughput gains of up to 20% are obtained in certain scenarios.
Hyperspectral Imagery Band Selection Based on Maximal Standard Deviation

A new Hyperspectral image band selection algorithm based on maximal standard deviation is proposed to reduce spectral redundancy of Hyperspectral remote sensing image and computational complexity. It first uses standard deviation to measure the band information. The correlation between band and selecting band is then used as a weight factor for standard deviation in the iterative calculation. The experimental results show that the informative band subsets with low correlation can be selected using the maximal standard deviation. In addation, when the obtained bands are combined with Hyperspectral image classification, better classification accuracy can be produced.
Latent Factors of Visual Popularity Prediction

Predicting the popularity of an image on social networks based solely on its visual content is a difficult problem. One image may become widely distributed and repeatedly shared, while another similar image may be totally overlooked. We aim to gain insight into how visual content affects image popularity. We propose a latent ranking approach that takes into account not only the distinctive visual cues in popular images, but also those in unpopular images. This method is evaluated on two existing datasets collected from photo-sharing websites, as well as a new proposed dataset of images from the microblogging website Twitter. Our experiments investigate factors of the ranking model, the level of user engagement in scoring popularity, and whether the discovered senses are meaningful. The proposed approach yields state of the art results, and allows for insight into the semantics of image popularity on social networks.
Elevated body mass index as a risk factor for chronic kidney disease: current perspectives

Chronic kidney disease (CKD) is defined by the National Kidney Foundation Kidney Disease Outcomes Quality Initiative as the presence of reduced kidney function or kidney damage for a period of 3 months or greater. Obesity is considered a risk factor for CKD development, but its precise role in contributing to CKD and end stage kidney disease is not fully elucidated. In this narrative review, the objectives are to describe the pathogenesis of CKD in obesity, including the impact of altered adipokine secretion in obesity and CKD, and to provide an overview of the clinical studies assessing the risk of obesity and CKD development.
Automatic Laughter Segmentation

Our goal in this work was to develop an accurate method to identify laughter segments, ultimately for the purpose of speaker recognition. Our previous work used MLPs to perform frame level detection of laughter using short-term features, including MFCCs and pitch, and achieved a 7.9% EER on the ICSI Meeting Recorder vocalized test set. We improved upon our previous results by including high-level and long-term features, median filtering, and performing segmentation via a hybrid MLP/HMM system with Viterbi decoding. Upon including the long-term features and median filtering, our results improved to 5.4% EER on the vocalized test set, which was a 32% relative improvement over our short-term MLP system, and 2.7% EER on an equal-prior test set used by others, which was a 67% improvement over previous best reported results on the equal-prior test set. After attaining segmentation results by incorporating the hybrid MLP/HMM system and Viterbi decoding, we had a 78.5% precision rate and 85.3% recall rate on the vocalized test set and a 99.5% precision rate and 88.0% recall rate on the equal-prior test set. To our knowledge these are the best known laughter segmentation results on the ICSI Meeting Recorder Corpus to date.
Problems and Opportunities of Applying Data- & Audio-Mining Techniques to Ethnic Music

Current research in music information retrieval focuses on Western music. In music from other cultures, both musical structures and thinking about music can be very different. This creates problems for both the analysis of musical features and the construction of databases. On the other hand, a well-documented digitization offers interesting opportunities for the study and spread of ‘endangered’ music. Here, some general problems regarding the digital indexation of ethnic music are given, illustrated with a method for describing pitch structure, comparing Western standards with African music found in the digitization of the archives of the Royal Museum of Central-Africa in Tervuren (Brussels).