Miracle-dz/UniFAR-FTUs huggingface dataset:
Oxytocin improves behavioral and electrophysiological deficits in a novel Shank3-deficient rat

Mutations in the synaptic gene SHANK3 lead to a neurodevelopmental disorder known as Phelan-McDermid syndrome (PMS). PMS is a relatively common monogenic and highly penetrant cause of autism spectrum disorder (ASD) and intellectual disability (ID), and frequently presents with attention deficits. The underlying neurobiology of PMS is not fully known and pharmacological treatments for core symptoms do not exist. Here, we report the production and characterization of a Shank3-deficient rat model of PMS, with a genetic alteration similar to a human SHANK3 mutation. We show that Shank3-deficient rats exhibit impaired long-term social recognition memory and attention, and reduced synaptic plasticity in the hippocampal-medial prefrontal cortex pathway. These deficits were attenuated with oxytocin treatment. The effect of oxytocin on reversing non-social attention deficits is a particularly novel finding, and the results implicate an oxytocinergic contribution in this genetically defined subtype of ASD and ID, suggesting an individualized therapeutic approach for PMS. DOI: http://dx.doi. org/10.7554/eLife.18904. 001
PERA: Ad-Hoc Routing Protocol for Mobile Robots

Mobile robots need to be able to communicate among them, and with other hosts participating in a given task. Traditional wired networks are obviously not suitable for mobile robots. Current wireless networks are usually based on a ﬁxed network infrastructure (base stations) to route packets. The best alternative for mobile robots are Ad-Hoc networks, which are wireless networks that do not need a ﬁxed infrastructure. This paper describes PERA, an adaptation of an ad-hoc routing protocol that runs on Eyebot mobile robots. By using PERA, a robot can send messages to other robots or hosts that are not directly reachable through its radio antenna, by routing messages through intermediate mobile robots. The design, implementation and lessons learned in the initial tests of PERA are presented in this paper.
Range Counting over Multidimensional Data Streams

AbstractWe consider the problem of approximate range counting over a stream of d-dimensional points. In the data stream model the algorithm makes a single scan of the data, which is presented in an arbitrary order, and computes a compact summary data structure. The summary, whose size depends on the approximation parameter ε, can be used to count the number of points inside a query range within additive error εn, where n is the size of the stream seen so far. We present several results, deterministic and randomized, for both rectangle and halfspace ranges.
Analysis of a class of degenerate reaction-diffusion systems and the bidomain model of cardiac tissue

We prove well-posedness (existence and uniqueness) results for a class of degenerate reaction-diffusion systems. A prototype system belonging to this class is provided by the bidomain model, which is frequently used to study and simulate electrophysiological waves in cardiac tissue. The existence result, which constitutes the main thrust of this paper, is proved by means of a nondegenerate approximation system, the Faedo-Galerkin method, and the compactness method.
An Approach for Classifying Program Failures

In this work, we leverage hardware performance counters-collected data to automatically group program failures that stem from closely related causes into clusters, which can in turn help developers prioritize failures as well as diagnose their causes. Hardware counters have been used for performance analysis of software systems in the past. By contrast, in this paper they are used as abstraction mechanisms for program executions. The results of our feasibility studies conducted on two widely-used applications suggest that hardware counters-collected data can be used to reliably classify failures.
Implementation of dust emission and chemistry into the Community Multiscale Air Quality modeling system and initial application to an Asian dust storm episode

Abstract. The US Environmental Protection Agency's (EPA) Community Multiscale Air Quality (CMAQ) modeling system version 4.7 is further developed to enhance its capability in simulating the photochemical cycles in the presence of dust particles. The new model treatments implemented in CMAQ v4.7 in this work include two online dust emission schemes (i.e., the Zender and Westphal schemes), nine dust-related heterogeneous reactions, an updated aerosol inorganic thermodynamic module ISORROPIA II with an explicit treatment of crustal species, and the interface between ISORROPIA II and the new dust treatments. The resulting improved CMAQ (referred to as CMAQ-Dust), offline-coupled with the Weather Research and Forecast model (WRF), is applied to the April 2001 dust storm episode over the trans-Pacific domain to examine the impact of new model treatments and understand associated uncertainties. WRF/CMAQ-Dust produces reasonable spatial distribution of dust emissions and captures the dust outbreak events, with the total dust emissions of ~111 and 223 Tg when using the Zender scheme with an erodible fraction of 0.5 and 1.0, respectively. The model system can reproduce well observed meteorological and chemical concentrations, with significant improvements for suspended particulate matter (PM), PM with aerodynamic diameter of 10 μm, and aerosol optical depth than the default CMAQ v4.7. The sensitivity studies show that the inclusion of crustal species reduces the concentration of PM with aerodynamic diameter of 2.5 μm (PM2.5) over polluted areas. The heterogeneous chemistry occurring on dust particles acts as a sink for some species (e.g., as a lower limit estimate, reducing O3 by up to 3.8 ppb (~9%) and SO2 by up to 0.3 ppb (~27%)) and as a source for some others (e.g., increasing fine-mode SO42− by up to 1.1 μg m−3 (~12%) and PM2.5 by up to 1.4 μg m−3 (~3%)) over the domain. The long-range transport of Asian pollutants can enhance the surface concentrations of gases by up to 3% and aerosol species by up to 20% in the Western US.
Fitting and comparing models of phyletic evolution: random walks and beyond

Abstract For almost 30 years, paleontologists have analyzed evolutionary sequences in terms of simple null models, most commonly random walks. Despite this long history, there has been little discussion of how model parameters may be estimated from real paleontological data. In this paper, I outline a likelihood-based framework for fitting and comparing models of phyletic evolution. Because of its usefulness and historical importance, I focus on a general form of the random walk model. The long-term dynamics of this model depend on just two parameters: the mean (μstep) and variance (σ2step) of the distribution of evolutionary transitions (or “steps”). The value of μstep determines the directionality of a sequence, and σ2step governs its volatility. Simulations show that these two parameters can be inferred reliably from paleontological data regardless of how completely the evolving lineage is sampled. In addition to random walk models, suitable modification of the likelihood function permits consideration of a wide range of alternative evolutionary models. Candidate evolutionary models may be compared on equal footing using information statistics such as the Akaike Information Criterion (AIC). Two extensions to this method are developed: modeling stasis as an evolutionary mode, and assessing the homogeneity of dynamics across multiple evolutionary sequences. Within this framework, I reanalyze two well-known published data sets: tooth measurements from the Eocene mammal Cantius, and shell shape in the planktonic foraminifera Contusotruncana. These analyses support previous interpretations about evolutionary mode in size and shape variables in Cantius, and confirm the significantly directional nature of shell shape evolution in Contusotruncana. In addition, this model-fitting approach leads to a further insight about the geographic structure of evolutionary change in this foraminiferan lineage.
It Is All About Control: Understanding Reluctance to Register for Organ Donation

This article tests the theory that anxiety about death causes feelings of lack of control, which, with other factors, results in reluctance to donate organs, despite endorsement of the act. Using a survey of U.S. and U.K. registered organ donors and unregistered adults (N = 777), we tested the impact of trust in medical professionals, perceived importance of information on the transplant processes, anxiety regarding loss of control at one’s end-of-life, and a prosocial view of organ donation on willingness to register as donor. Structural analyses show that control mediated the associations of trust and information with willingness, while prosocial did not predict willingness. The findings explain the gap between stated attitudes and actual behavior, and suggest that framing donor card registration and organ donation as a way to gain control over the loss of agency in death and dying may encourage organ donation.
Investigating preprocessor-based syntax errors

The C preprocessor is commonly used to implement variability in program families. Despite the widespread usage, some studies indicate that the C preprocessor makes variability implementation difficult and error-prone. However, we still lack studies to investigate preprocessor-based syntax errors and quantify to what extent they occur in practice. In this paper, we define a technique based on a variability-aware parser to find syntax errors in releases and commits of program families. To investigate these errors, we perform an empirical study where we use our technique in 41 program family releases, and more than 51 thousand commits of 8 program families. We find 7 and 20 syntax errors in releases and commits of program families, respectively. They are related not only to incomplete annotations, but also to complete ones. We submit 8 patches to fix errors that developers have not fixed yet, and they accept 75% of them. Our results reveal that the time developers need to fix the errors varies from days to years in family repositories. We detect errors even in releases of well-known and widely used program families, such as Bash, CVS and Vim. We also classify the syntax errors into 6 different categories. This classification may guide developers to avoid them during development.
A contextual personalized recommander system for mobile OLAP

The main drawbacks and handled devices (small storage space, small size of the display screen, discontinuance of the connection to the WLAN etc) are often incompatible with the need of querying and browsing information extracted from enormous amounts of data which are accessible through the network. A key characteristic of emerging OLAP database systems will be to use them with mobile device. To overcome this we investigate in this paper to propose a contextual recommender system (CARS) for mobile OLAP based on multi-agent architecture called (MASCARS). This system extracts first the user profile from the log data file of OLAP server. For this we use a variant of prediction and explanation algorithms. These profiles then form a knowledge base. This knowledge base will be used to generate automatically a rule base (ACE), for assigning weights to the attributes of data cubes by type of query and user preferences. The best sequence of requests will be deduced for using in CARS.
A 2bcgskew predictor fused by a redundant history skewed perceptron predictor

The presented predictor fuses multiple predictions to obtain a more accurate prediction. The various predictions are delivered by a 2bcgskew predictor and include the 2bcgskew prediction itself as well as the bias and hysteresis bits of its component predictors. Together with global and local history, these predictions are used in the fusion predictor, which is an alloyed redundant history skewed perceptron predictor (RHSP).
ARBUD : A Reusable Architecture for Building User Models from Massive Datasets

In many situations, it is common that a large single source of data serves as input to multiple applications, each of which may use a different user model. It is often the case that each user model is created using a different process; however, in many cases it would more efficient to use a common architecture for building different user models in different application areas. In this paper, we propose a distributed-computing architecture based on MapReduce that allows for the efficient processing of massive datasets using reusable components that compute different features of the final user model. A metamodel is used for specifying the characteristics of the desired user model – which can include both shortterm and long-term user models – and the architecture is responsible for building the user model from the specified data and reusable components. We present an instantiation of the architecture in the context of telecommunications applications and empirically evaluate the scalability of the proposed architecture with a real dataset. Our results indicate that complex user models for millions of users can be obtained in just a few hours on a small computer cluster.
Integrated approach to designing growth factor delivery systems

Growth factors have been widely used in strategies to regenerate and repair diseased tissues, but current therapies that go directly from bench to bedside have had limited clinical success. We hypothesize that engineering successful therapies with recombinant proteins will often require specific quantitative information of the spatiotemporal role of the factors and the development of sophisticated delivery approaches that provide appropriate tissue exposures. This hypothesis was tested in the context of therapeutic angiogenesis. An in vitro model of angiogenesis was adapted to quantify the role of the concentration/gradient of vascular endothelial growth factor [VEGF (165)] on microvascular endothelial cells, and a delivery system was then designed, based on a mathematical model, to provide the desired profile in ischemic mice hindlimbs. This system significantly enhanced blood vessel formation, and perfusion and recovery from severe ischemia. This general approach may be broadly applicable to growth factor therapies. — Chen R. R. , Silva, E. A. , Yuen, W. W. , Brock, A. A. , Fischbach, C. , Lin, A. S. , Guldberg, R. E. , Mooney D. J. Integrated approach to designing growth factor delivery systems. FASEB J. 21, 3896–3903 (2007)
CloudArmor : Protecting Cloud Instances by Validating Client Commands

IaaS clouds offer on-demand computing resources to clients to offload the burden of IT infrastructure management. However, in order to do so, cloud clients must place full trust in the cloud infrastructure, from various cloud services to the administrators managing the infrastructure. Such trust is often unjustified given the recent evidence of vulnerabilities found in cloud services and threats from accidental and intentional mismanagement by cloud administrators. In this paper, we introduce CloudArmor, a method that validates that client commands are executed as expected on cloud nodes, even if many cloud services are under the control of adversaries. Our insight is that cloud services act as a proxy that transforms commands to sequences of operations performed in cloud. Such transformations, however, do not transform argument values, so we can construct models that limit each command’s execution to legal sequences of system calls and predict their argument values. We implemented a prototype CloudArmor for the OpenStack cloud, converting the compute node to act as a proxy for client to validate how their commands are executed in cloud. Results show that CloudArmor can defend against a variety of attacks from cloud services, enabling a reduction of trust in cloud services by over 90% with no impact on the cloud function. Moreover, as CloudArmor only mediates cloud instance operations, it imposes less than 1.2% overhead for client instances. As a result, OpenStack clients can leverage CloudArmor to manage their instances safely without sacrificing performance or cloud function.
A Coefficient of Agreement for Nominal Scales

CONSIDER Table 1. It represents in its formal characteristics a situation which arises in the clinical-social-personality areas of psychology, where it frequently occurs that the only useful level of measurement obtainable is nominal scaling (Stevens, 1951, pp. 2526), i.e. placement in a set of k unordered categories. Because the categorizing of the units is a consequence of some complex judgment process performed by a &dquo;two-legged meter&dquo; (Stevens, 1958), it becomes important to determine the extent to which these judgments are reproducible, i.e., reliable. The procedure which suggests itself is that of having two (or more) judges independently categorize a sample of units and determine the degree, significance, and
Affective homogeneity in the Spanish general election debate. A comparative analysis of social networks political agents

ABSTRACT Many experts in the social sciences are studying the extent to which the agents of democratic political systems tend to strengthen their points of view to such an extent that it reduces their capacity to engage and debate with those who hold different points of view. This phenomenon, called polarization, is also present in public debate on social networks and has generated a significant number of studies and empirical research. In this context, a few noteworthy factors in the study of polarization are the concepts of ‘homophily’ and ‘homogeneity’. These terms refer to the fragmenting effect of social networks and are the consequence of the common characteristics and attributes of the members that comprise them. In this work, we analyze this phenomenon in relation with the General Elections for the Presidency of Spain and, particularly, in the case of the candidature of the political party UnidosPodemos. We used data from the Twitter social network to analyze the subjects of debate, and the affective positions in relation with each of these. We found that the most active political agents had postures that were clearly homogenized in affective terms. Finally, we discuss the polarizing effects of this homogenization.
Acoustic analysis of czech expressive recordings from a single speaker in terms of various communicative functions

This paper presents an extensive acoustic analysis of utterances which were recorded by a single Czech female speaker using various expressive speaking styles. The recording of the expressive utterances was performed as a dialogue between a human and a computer on a given topic. Speech of the human speaker was captured and later carefully transcribed by human annotators. It was also annotated using a listening test. The aim of the annotations was to label each utterance with a corresponding speaking style (referred to as a communicative function). Based on such a labeling, the expressive recordings were classified into various groups and acoustically analyzed. In particular, we placed emphasis on some features which are supposed to influence the perception of speech, such as F0, phoneme duration, formant frequencies or energy. We made an effort to reveal some acoustic differences between the various speaking styles that could help us to improve expressive speech synthesis in a given limited domain.
Weighted sum-rate maximization using weighted MMSE for MIMO-BC beamforming design

This paper studies linear transmit filter design for Weighted Sum-Rate (WSR) maximization in the multiple input multiple output broadcast channel (MIMO-BC). The problem of finding the optimal transmit filter is non-convex and intractable to solve using low complexity methods. Motivated by recent results highlighting the relationship between mutual information and minimum mean square error (MMSE), this paper establishes a relationship between weighted sum-rate and weighted MMSE in the MIMO-BC. The relationship is used to propose two low complexity algorithms for finding a local weighted sum-rate optimum based on alternating optimization. Numerical results studying sum-rate show that the proposed algorithms achieve high performance with few iterations.
Transcriptional co-factor Transducin beta-like (TBL) 1 acts as a checkpoint in pancreatic cancer malignancy

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer fatalities in Western societies, characterized by high metastatic potential and resistance to chemotherapy. Critical molecular mechanisms of these phenotypical features still remain unknown, thus hampering the development of effective prognostic and therapeutic measures in PDAC. Here, we show that transcriptional co‐factor Transducin beta‐like (TBL) 1 was over‐expressed in both human and murine PDAC. Inactivation of TBL1 in human and mouse pancreatic cancer cells reduced cellular proliferation and invasiveness, correlating with diminished glucose uptake, glycolytic flux, and oncogenic PI3 kinase signaling which in turn could rescue TBL1 deficiency‐dependent phenotypes. TBL1 deficiency both prevented and reversed pancreatic tumor growth, mediated transcriptional PI3 kinase inhibition, and increased chemosensitivity of PDAC cells in vivo. As TBL1 mRNA levels were also found to correlate with PI3 kinase levels and overall survival in a cohort of human PDAC patients, TBL1 was identified as a checkpoint in the malignant behavior of pancreatic cancer and its expression may serve as a novel molecular target in the treatment of human PDAC.
Analytical Model of Energy Consumption in Cluster based Wireless Sensor Networks with Data Aggregation using M/M/1 Queuing Model

Wireless Sensor Networks (WSNs) consist of several sensor nodes equipped with limited power sources that sense environments event and transmit data to the sink node. WSNs have many constraints including energy, redundant data, and many-to-one flows. Data aggregation is one of the most important issues for achieving energy-efficiency in wireless sensor networks. Sensor nodes in the surrounding region of an event may generate redundant sensed data. A data aggregation technique in WSNs focuses on decreasing the energy consumption by reducing the amount of data that needed to be sent to the sink node. In this paper, we propose an M/M/1 queuing model for evaluating energy consumption in WSNs with dada transmission. The proposed model can help designers to evaluate the energy consumption of cluster based wireless sensor network with data aggregation. We compare the energy consumption results for networks with and without data aggregation technique. General Terms Energy consumption in cluster based wireless sensor networks with data aggregation.
Comparison of eye movement filters used in HCI

We compared various real-time filters designed to denoise eye movements from low-sampling devices. Most of the filters found in literature were implemented and tested on data gathered in a previous study. An improvement was proposed for one of the filters. Parameters of each filter were adjusted to ensure their best performance. Four estimation parameters were proposed as criteria for comparison. The output from the filters was compared against two idealized signals (the signals denoised offline). The study revealed that FIR filters with triangular or Gaussian kernel (weighting) functions and parameters dependent on signal state show the best performance.