Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic channel assignment and reassignment for exploiting channel reuse opportunities in ad hoc wireless networks

In Ad Hoc networks, a channel that is occupied by a pair of hosts can be reused by another pair of hosts, only if their communication range is not overlapped. Exploiting the channel reuse opportunities may cause the co-channel interference problem. Two pairs of communicating hosts that use the same channel may be gradually close to each other. A channel reassignment operation should be applied to these hosts for maintaining their communication. This paper proposes a channel assignment protocol so that the channel resource could be highly utilized. A channel reassignment protocol is also proposed for preventing the communicating hosts from co-channel interference owing to mobility. The proposed reassignment protocol efficiently reassigns a new available channel for one pair of hosts that is suffering from co-channel interference. Experimental results show that the proposed protocols provide a larger amount of hosts with communication at the same time and prevent their communication from failure.
Enhancement of Calcium/Vitamin D Supplement Efficacy by Administering Concomitantly Three Key Nutrients Essential to Bone Collagen Matrix for the Treatment of Osteopenia in Middle-Aged Women: A One-Year Follow-Up

Two vitamins and proline (CB6Pro), three nutrients essential for bone collagen, were used in combination to a 1000 mg calcium/250 IU vitamin D (Ca/D) daily supplement to treat osteopenia as a preventive measure against osteoporosis later in life. Middle-aged women not using estrogen were screened for osteopenia using the WHO criteria and divided into three groups (n = 20 each): 1) placebo healthy controls with normal bone mineral density (BMD); 2) control Ca/D-treated osteopenic patients; and 3) Ca/D + CB6Pro-treated osteopenic patients. The three groups were comparable at baseline except for BMD. After one-year treatment, cortical diaphyseal BMD remained constant in each group, but trabecular bone loss persisted (at 5 lumbar sites) in osteopenic group 2. No further bone loss was detected in osteopenic group 3. A loss of 2% was evidenced in the placebo group at one lumbar site. Markers of bone formation (which increase in coupling to resorption) decreased significantly in both osteopenic groups. Although biomarkers of resorption did not change, hormone (PTH and 1,25(OH)2D3)-induced osteoclastic activity was significantly reduced. No decline in BMD occurred at any bone site in osteopenic group 3, highlighting the importance of improving the quality of bone matrix concomitantly to mineral replacement.
A study of motor-end EMI filter on output common-mode noise suppression in DC-fed motor drive system

High density electromagnetic interference (EMI) filter design, especially on conducted common-mode (CM) noise emission suppression, is essential for variable speed motor drive system in modern transportation applications. As an alternative of the conventional inverter-end (IE) EMI filter, this paper discusses the possibility of implementing an EMI filter on the motor end for the CM noise attenuation. A functional comparison between the inverter and the motor-end CM filter is firstly carried out, concerning the CM noise propagation path in the DC-fed motor drive system. According to their features in the noise propagation, the authors focus on two typical kinds of motor-end CM filters in detail, namely the motor front-end (MFE) filter and the motor chassis-end (MCE) filter. Their performances on improving the output CM impedance and suppressing the CM noise emission are analyzed, based on experimental results from a specified prototype system. Investigations show that the implementation of the MFE filter has similar functions with the IE CM choke, while attaching the MCE filter with the motor can help save the magnetic size and weight from the conventional IE filter. However, special attentions are needed for the safety and parasitic concerns.
Bio-molecular event extraction using Support Vector Machine

The main goal of Biomedical Natural Language Processing (BioNLP) is to capture biomedical phenomena from textual data by extracting relevant entities, information and relations between biomedical entities (i.e. proteins and genes). In general, in most of the published papers, only binary relations were extracted. In a recent past, the focus is shifted towards extracting more complex relations in the form of bio-molecular events that may include several entities or other relations. In this paper we propose an approach that enables event extraction (detection and classification) of relatively complex bio-molecular events. We approach this problem as a supervised classificat ion problem and use the well-known algorithm, namely Support Vector Machine (SVM) that makes use of statistical and linguistic features that represent various morphological, syntactic and contextual information of the candidate bio-molecular trigger words. Firstly, we consider the problem of event detection and classification as a two-step process, first step of which deals with the event detection task and the second step classifies these identified events to one of the nine predefined classes. Later on we tr eat this problem as one-step process, and perform event detection and classification together. Three-fold cross validation expe riments on the BioNLP 2009 shared task datasets yield the overall average recall, precision and F-measure values of 62.95%, 74.53%, and 68.25%, respectively, for the event detection. We observed the overall classification accuracy of 72.50%. Evaluation resu lts of the proposed approach when detection and classification are performed together showed the overall recall, precision and F-measure values of 57.66%, 55.87%, and 56.75%, respectively.
SmartEye: Assisting Instant Photo Taking via Integrating User Preference with Deep View Proposal Network

Instant photo taking and sharing has become one of the most popular forms of social networking. However, taking high-quality photos is difficult as it requires knowledge and skill in photography that most non-expert users lack. In this paper we present SmartEye, a novel mobile system to help users take photos with good compositions in-situ. The back-end of SmartEye integrates the View Proposal Network (VPN), a deep learning based model that outputs composition suggestions in real time, and a novel, interactively updated module (P-Module) that adjusts the VPN outputs to account for personalized composition preferences. We also design a novel interface with functions at the front-end to enable real-time and informative interactions for photo taking. We conduct two user studies to investigate SmartEye qualitatively and quantitatively. Results show that SmartEye effectively models and predicts personalized composition preferences, provides instant high-quality compositions in-situ, and outperforms the non-personalized systems significantly.
WormBase: a comprehensive data resource for Caenorhabditis biology and genomics

WormBase (http://www.wormbase.org), the model organism database for information about Caenorhabditis elegans and related nematodes, continues to expand in breadth and depth. Over the past year, WormBase has added multiple large-scale datasets including SAGE, interactome, 3D protein structure datasets and NCBI KOGs. To accommodate this growth, the International WormBase Consortium has improved the user interface by adding new features to aid in navigation, visualization of large-scale datasets, advanced searching and data mining. Internally, we have restructured the database models to rationalize the representation of genes and to prepare the system to accept the genome sequences of three additional Caenorhabditis species over the coming year.
Memory T Cells and Their Costimulators in Human Allograft Injury1

Both CD4+ and CD8+ human memory but not naive T cells respond to allogeneic human dermal microvascular endothelial cells (HDMEC) in vitro by secreting cytokines and by proliferating. Several recently identified costimulators, namely, 4-1BB ligand, ICOS ligand, and OX40 ligand, are up-regulated on cultured HDMEC in response to TNF or coculture with allogeneic T cells. Blockade of these costimulators each partially reduces IFN-γ and IL-2 secretion and proliferation of previously resting memory T cells. The effects of these costimulators are overlapping but not identical. Memory but not naive T cells are the principal effectors of microvascular injury in human skin allografts following adoptive transfer into immunodeficient mice. Furthermore, blocking 4-1BB ligand, ICOS ligand, or OX40 ligand in this model reduces human skin allograft injury and T cell effector molecule expression. These data demonstrate that human memory T cells respond to microvascular endothelial cells and can injure allografts in vivo without priming. Furthermore, several recently described costimulators contribute to these processes.
Mixed CSIT DL Channel: Gains with an Additional Receive Antenna

We study a practical version of the broadcast channel (BC) where the channel state information at the transmitter (CSIT) is available only for some of the users, dubbing the system as mixed CSIT. We start with the simplest instance of such a heterogeneous BC where a multi-antenna transmitter base-station (BS) is trying to communicate data to two single-antenna user equipments (UE), having the perfect CSIT about UE1 and no CSIT about UE2. We propose a very simple transmission strategy at the BS for such a system and compare its performance with the interference alignment solution proposed elsewhere which achieves full degrees-of-freedom (DOF) when channel coherence lengths and switching instants abide by certain restrictions. Then with an additional receive antenna granted to UE2 (whose CSIT is not available) and keeping the same CSIT perspective, achievable rates for two users are derived and compared with earlier schemes. Analytical and simulation results show that this additional receive antenna brings significant gains in the system with the same transmission strategy and without any extra requirement of channel training or feedback.
Joint Relay Selection and Power Allocation for Full-Duplex DF Co-Operative Networks With Outdated CSI

We investigate joint relay selection (RS) and power allocation (PA) for full-duplex (FD) decode-and-forward (DF) co-operative networks, considering the scenario where the available channel state information (CSI) is outdated, yet the correlation coefficient between the outdated CSI and the real one is known. We show that in the outdated CSI environment, the proposed scheme can provide a performance improvement over the conventional schemes, and if the residual loop interference (RLI) is sufficiently low, more performance gains can be offered in the high signal-to-noise-ratio (SNR) region. Simulations are provided to validate our results.
Analyzing Vulnerability of Power Systems with Continuous Optimization Formulations

Potential vulnerabilities in a power grid can be exposed by identifying those transmission lines on which attacks (in the form of interference with their transmission capabilities) causes maximum disruption to the grid. In this study, we model the grid by (nonlinear) AC power flow equations, and assume that attacks take the form of increased impedance along transmission lines. We quantify disruption in several different ways, including (a) overall deviation of the voltages at the buses from <inline-formula><tex-math notation="LaTeX">$1.0$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="kim-ieq1-2587484.gif"/></alternatives></inline-formula> per unit (p.u.), and (b) the minimal amount of load that must be shed in order to restore the grid to stable operation. We describe optimization formulations of the problem of finding the most disruptive attack, which are either nonlinear programing problems or nonlinear bilevel optimization problems, and describe customized algorithms for solving these problems. Experimental results on the IEEE 118-Bus system and a Polish 2383-Bus system are presented.
Inducing Compact but Accurate Tree-Substitution Grammars

Tree substitution grammars (TSGs) are a compelling alternative to context-free grammars for modelling syntax. However, many popular techniques for estimating weighted TSGs (under the moniker of Data Oriented Parsing) suffer from the problems of inconsistency and over-fitting. We present a theoretically principled model which solves these problems using a Bayesian non-parametric formulation. Our model learns compact and simple grammars, uncovering latent linguistic structures (e.g., verb subcategorisation), and in doing so far out-performs a standard PCFG.
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, there are only a few publicly available data sets, which often contain samples from subjects with too similar characteristics, and very often lack specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new dataset of acceleration samples acquired with an Android smartphone designed for human activity recognition and fall detection. The dataset includes 11,771 samples of both human activities and falls performed by 30 subjects of ages ranging from 18 to 60 years. Samples are divided in 17 fine grained classes grouped in two coarse grained classes: one containing samples of 9 types of activities of daily living (ADL) and the other containing samples of 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL performed, the age, the gender, and so on. Finally, the dataset has been benchmarked with four different classifiers and with two different feature vectors. We evaluated four different classification tasks: fall vs. no fall, 9 activities, 8 falls, 17 activities and falls. For each classification task, we performed a 5-fold cross-validation (i.e., including samples from all the subjects in both the training and the test dataset) and a leave-one-subject-out cross-validation (i.e., the test data include the samples of a subject only, and the training data, the samples of all the other subjects). Regarding the classification tasks, the major findings can be summarized as follows: (i) it is quite easy to distinguish between falls and ADLs, regardless of the classifier and the feature vector selected. Indeed, these classes of activities present quite different acceleration shapes that simplify the recognition task; (ii) on average, it is more difficult to distinguish between types of falls than between types of activities, regardless of the classifier and the feature vector selected. This is due to the similarity between the acceleration shapes of different kinds of falls. On the contrary, ADLs acceleration shapes present differences except for a small group. Finally, the evaluation shows that the presence of samples of the same subject both in the training and in the test datasets, increases the performance of the classifiers regardless of the feature vector used. This happens because each human subject differs from other subjects in performing activities even if she shares with them the same physical characteristics.
A “Hit and Run” Approach to Inducible Direct Reprogramming of Astrocytes to Neural Stem Cells

Temporal and spatial control of gene expression can be achieved using an inducible system as a fundamental tool for regulated transcription in basic, applied and eventually in clinical research. We describe a novel “hit and run” inducible direct reprogramming approach. In a single step, 2 days post-transfection, transiently transfected Sox2FLAG under the Leu3p-αIPM inducible control (iSox2) triggers the activation of endogenous Sox2, redirecting primary astrocytes into abundant distinct nestin-positive radial glia cells. This technique introduces a unique novel tool for safe, rapid and efficient reprogramming amendable to regenerative medicine.
Approximate data reuse-based processor: a case study on image compression

In most embedded systems, how to design accelerators of end applications under stringent design constraints has been a crucial issue. In this paper, we employ a new computation paradigm "approximate computing" to resolve this issue. More specifically, our work focuses on and reuses computations which have recently produced results that are expected to be similar enough to the current ones - "approximate data reuse. " This concept enables to reduce computations by skipping instructions. We develop accelerator designs with this concept holistically from both hardware (architecture) and software (compilation) to achieve sufficient speedup and energy saving while mitigating the area overhead at the cost of some error. This paper provides mainly three contributions: architectural extensions applicable to a variety of processors even under a stringent constraint on circuit area, parameterization of important features of our method so that the degree of approximate data reuse can be easily tuned for different applications, and exhaustive evaluations on combinations of key parameters through our case study. A case study was quantitatively conducted using a realistic application (image compression) to demonstrate the effectiveness of our method over conventional ones.
The study of morphological variation in the hominid fossil record: biology, landmarks and geometry

This review considers some recent advances in shape analysis based on landmark data, and focuses on the application of these methods to the study of skeletal evolution in primates. These advances have provoked some controversy. The major aims of this review are to put these debates in context and to provide an overview for the nonmathematician. The purpose of morphometric studies is considered, together with issues relating to the nature, significance and identification of landmarks before turning to a review of available technologies for the analysis of morphological variation. These are considered in terms of underlying models and assumptions in order to clarify when each is appropriate. To illustrate the application of these methods, 3 example studies are presented. The first examines differences amongst ancient and modern adult human crania using 2‐dimensional data. The second illustrates the extension of these methods into 3 dimensions in a study of facial growth in monkeys. The third presents an application to the analysis of the form of the hominoid talus. The review ends with an account of available software resources for shape analysis.
Using Visibility to Control Collective Attention in Crowdsourcing

Online crowdsourcing provides new opportunities for ordinary people to create original content. This has led to a rapidly growing volume of user-generated content, and consequently a challenge to readily identify high quality items. Due to people’s limited attention, the presentation of content strongly affects how people al- locate effort to the available content. We evaluate this effect experimentally using Amazon Mechanical Turk and show that it is possible to manipulate attention to accomplish desired goals.
The privacy analysis of battery control mechanisms in demand response: Revealing state approach and rate distortion bounds

Perfect knowledge of a user's power consumption profile by a utility is a violation of privacy and can be detrimental to the successful implementation of demand response systems. It has been shown that an in-home energy storage system which provides a viable means to achieve the cost savings of instantaneous electricity pricing without inconvenience can also be used to maintain the privacy of a user's power profile. The optimization of the tradeoff between privacy, as measured by Shannon entropy, and cost savings that can be provided by a finite capacity battery with zero tolerance for delay is known to be equivalent to a Partially Observable Markov Decision Process with non linear belief dependent rewards- solutions to such systems suffer from high computational complexity. In this paper, we propose a “revealing state” approach to enable computation of a class of battery control policies that aim to maximize the achievable privacy of in-home demands. In addition, a rate-distortion approach is presented to derive upper bounds on the privacy-cost savings tradeoff of battery control policies. These bounds are derived for a discrete model, where demand and price follow i.i. d uniform distributions. Numerical results show that the derived bounds are quite close to each other demonstrating the efficacy of the proposed class of strategies.
Feature consistency in compile-time-configurable system software: facing the linux 10,000 feature problem

Much system software can be configured at compile time to tailor it with respect to a broad range of supported hardware architectures and application domains. A good example is the Linux kernel, which provides more than 10,000 configurable features, growing rapidly. From the maintenance point of view, compile-time configurability imposes big challenges. The configuration model (the selectable features and their constraints as presented to the user) and the configurability that is actually implemented in the code have to be kept in sync, which, if performed manually, is a tedious and error-prone task. In the case of Linux, this has led to numerous defects in the source code, many of which are actual bugs. We suggest an approach to automatically check for configurability-related implementation defects in large-scale configurable system software. The configurability is extracted from its various implementation sources and examined for inconsistencies, which manifest in seemingly conditional code that is in fact unconditional. We evaluate our approach with the latest version of Linux, for which our tool detects 1,776 configurability defects, which manifest as dead/superfluous source code and bugs. Our findings have led to numerous source-code improvements and bug fixes in Linux: 123 patches (49 merged) fix 364 defects, 147 of which have been confirmed by the corresponding Linux developers and 20 as fixing a new bug.
Predicting the French Stock Market Using Social Media Analysis

In this article, we try to predict the next-day CAC40 index. We apply the idea of Johan Bollen et al. from [1] on the French stock market and we conduct our experiment using French tweets. Two analysis are applied on tweets: sentiment analysis and subjectivity analysis. Results of these analysis are then used to train a simple neural network. The input features are the sentiment, the subjectivity and the CAC40 closing values at day-1 and day-0. The single output value is the predicted CAC40 closing value at day+1. We propose an architecture using the JEE framework resulting in a better scalability and an easier industrialization. Experiments are conducted over 5 months of data. We train our neural network on 3/4 of the data and we test predictions on the remaining quarter. Our best run gives a direction accuracy of 80% and a mean absolute percentage error (MAPE) of 2.97%. Results are not as good as those reported in [1] but many modifications can be applied in order to improve the performance: classically by adding more features but we mainly aspire to a system where the user can integrate its own expertise.