Miracle-dz/UniFAR-FTUs huggingface dataset:
Surface identification using simple contact dynamics for mobile robots

This paper describes an approach to surface identification in the context of mobile robotics, applicable to supervised and unsupervised learning. The identification is based on analyzing the tip acceleration patterns induced in a metallic rod, dragged along a surface that is to be identified. Eight features in time and frequency domains are used for classification. Results show that for ten type of indoor and outdoor surfaces, reliable identification can be achieved (90.0 and 94.6 percent for a 1 and 4 seconds time-window, respectively), using a non-sophisticated classifier (artificial neural network). Demonstration is done on how such a sensor and a simple control strategy can be used to guide a blind robot, using a simulation and a real differential drive robot.
Empirical Analysis of Rate Limiting Mechanisms (CMU-PDL-05-103)

One class of worm defense techniques that received attention of late is to “rate limit” outbound traffic to contain fast spreading worms. Several proposals of rate limiting techniques have appeared in the literature, each with a different take on the impetus behind rate limiting. This paper presents an empirical analysis on different rate limiting schemes using real traffic and attack traces from a sizable network. In the analysis we isolate and investigate the impact of the critical parameters for each scheme and seek to understand how these parameters might be set in realistic network settings. Analysis shows that using DNS-based rate limiting has substantially lower error rates than schemes based on other traffic statistics. The analysis additionally brings to light a number of issues with respect to rate limiting at large. We explore the impact of these issues in the context of general worm containment.
Formulation and Analysis of LMS Adaptive Networks for Distributed Estimation in the Presence of Transmission Errors

Wireless sensor network (WSN) technologies and distributed processing are essential to develop ubiquitous sensing in the Internet of Things (IoT) paradigm, wherein sensors pervasively collect data and perform information processing and communication tasks to achieve a common objective. This paper presents the formulation and analysis of distributed estimation algorithms based on the diffusion cooperation scheme in the presence of errors due to the unreliable data transfer among nodes. In particular, we highlight the impact of transmission errors on the least-mean squares (LMS) adaptive networks. We develop the closed-form expressions for the steady-state mean-square deviation (MSD), which is helpful to assess the effects of the imperfect information flow on the behavior of diffusion LMS algorithms in terms of steady-state error. The model is then validated by performing Monte Carlo simulations. It is shown that local and global steady-state MSD values are not necessarily monotonic increasing functions of the error probability. We also assess sufficient conditions that ensure mean and mean-square stability of diffusion LMS strategies in the presence of transmission errors. We examine a practical scenario where errors occur at the medium access control (MAC) level. To overcome the problem of unreliable data exchange, we implement a random pairwise strategy that improves the performance of the estimation algorithm in the presence of high transmission error rates. Moreover, issues such as scalability in the sense of network size and regressor size, convergence behavior during the transient phase, spatially correlated observations, as well as the effect of the distribution of the noise variance are studied.
Power spectrum estimation method for image denoising by frequency domain Wiener filter

In this paper, a new denoising method for images degraded by white noise in blind condition using the Wiener filter has been proposed. The Wiener filter is a well-known denoising technique, but the performance comparison of the Wiener filter in different domains is not clear. This paper investigates and evaluates the Wiener filter implemented in the frequency, time and wavelet domains in an ideal case. It is shown that the frequency domain Wiener filter outperformes the Wiener filter implemented in the other domains. In addition, this paper proposes a power spectrum estimation method? to be employed with the frequency domain Wiener filter. The performance is studied and found that the proposed method is capable to denoise noisy images in a wide range of noise levels and provides improved or equivalent performance relative to conventional methods.
Comparative Investigation of Human Amniotic Epithelial Cells and Mesenchymal Stem Cells for Application in Bone Tissue Engineering

Emerging evidence suggests amniotic epithelial cells (AECs) as a promising source of progenitor cells in regenerative medicine and bone tissue engineering. However, investigations comparing the regenerative properties of AECs with other sources of stem cells are particularly needed before the feasibility of AECs in bone tissue engineering can be determined. This study aimed to compare human amniotic epithelial cells (hAECs), human bone marrow mesenchymal stem cells (hBMSCs), and human amniotic fluid derived mesenchymal stem cells (hAFMSCs) in terms of their morphology, proliferation, immunophenotype profile, and osteogenic capacity in vitro and in vivo. Not only greatly distinguished by cell morphology and proliferation, hAECs, hAFMSCs, and hBMSCs exhibited remarkably different signature regarding immunophenotypical profile. Microarray analysis revealed a different expression profile of genes involved in ossification along the three cell sources, highlighting the impact of different anatomical origin and molecular response to osteogenic induction on the final tissue-forming potential. Furthermore, our data indicated a potential role of FOXC2 in early osteogenic commitment.
Supplemental Data Comprehensive Molecular Structure of the Eukaryotic Ribosome

rpS19e point mutations affiliated with DBA Several point mutations to rpS19e that are associated with DBA inhibit maturation and thus, nuclear export, causing a significant reduction in the level of cytosolic pre-40S subunits in yeast (Leger-Silvestre et al.) and human cells (Idol et al., 2007). Several of these missense mutations have been mapped to the atomic structure of rpS19e and categorized into two classes based on interspecies conservation and conserved basic patches formed within the specific areas of the crystal structure (Gregory et al., 2007). Class I mutations (V15F, L18P, A57P, A61S/E, L64P, G127E, and L131R in humans) are restricted to residues within the core of rpS19e, mainly within the buried, amphipathic α-helix 3 (Figure S2b). Analyses of mutant rpS19e expressed in mammalian cells suggest that the class I mutations inhibit proper folding of rpS19e, leading to rapid degradation of the unstable protein by the proteasome (Angelini et al., 2007). Conversely, class II mutations (P47L, W52R, R56Q, S59F, R62W/Q, R101H, and G120S in humans) are more resistant to proteasome degradation when expressed in mammalian cells. While these data suggest that class II mutations result in proper folding of rpS19e, incorporation of rpS19e with class II mutations into pre-ribosomes is abrogated (Angelini et al., 2007).
Evidence for a Domain-Swapped CD4 Dimer as the Coreceptor for Binding to Class II MHC1

CD4 is a coreceptor for binding of T cells to APC and the primary receptor for HIV. The disulfide bond in the second extracellular domain (D2) of CD4 is reduced on the cell surface, which leads to formation of disulfide-linked homodimers. A large conformational change must take place in D2 to allow for formation of the disulfide-linked dimer. Domain swapping of D2 is the most likely candidate for the conformational change leading to formation of two disulfide-bonds between Cys130 in one monomer and Cys159 in the other one. Mild reduction of the extracellular part of CD4 resulted in formation of disulfide-linked dimers, which supports the domain-swapped model. The functional significance of dimer formation for coreceptor function was tested using cells expressing wild-type or disulfide-bond mutant CD4. Eliminating the D2 disulfide bond markedly impaired CD4’s coreceptor function. Modeling of the complex of the TCR and domain-swapped CD4 dimer bound to class II MHC and Ag supports the domain-swapped dimer as the immune coreceptor. The known involvement of D4 residues Lys318 and Gln344 in dimer formation is also accommodated by this model. These findings imply that disulfide-linked dimeric CD4 is the preferred coreceptor for binding to APC.
Open Source-Style Collaborative Development Practices in Commercial Projects Using GitHub

Researchers are currently drawn to study projects hosted on GitHub due to its popularity, ease of obtaining data, and its distinctive built-in social features. GitHub has been found to create a transparent development environment, which together with a pull request-based workflow, provides a lightweight mechanism for committing, reviewing and managing code changes. These features impact how GitHub is used and the benefits it provides to teams' development and collaboration. While most of the evidence we have is from GitHub's use in open source software (OSS) projects, GitHub is also used in an increasing number of commercial projects. It is unknown how GitHub supports these projects given that GitHub's workflow model does not intuitively fit the commercial development way of working. In this paper, we report findings from an online survey and interviews with GitHub users on how GitHub is used for collaboration in commercial projects. We found that many commercial projects adopted practices that are more typical of OSS projects including reduced communication, more independent work, and self-organization. We discuss how GitHub's transparency and popular workflow can promote open collaboration, allowing organizations to increase code reuse and promote knowledge sharing across their teams.
Does social contact matter?: modelling the hidden web of trust underlying twitter

Social recommender systems aim to alleviate the information overload problem on social network sites. The social network structure is often an important input to these recommender systems. Typically, this structure cannot be inferred directly from declared relationships among users. The goal of our work is to extract an underlying hidden and sparse network which more strongly represents the actual interactions among users. We study how to leverage Twitter activities like micro-blogging and the network structure to find a simple, efficient, but accurate method to infer and expand this hidden network. We measure and compare the performance of several different modeling strategies using a crawled data set from Twitter. Our results reveal that the structural similarity in the network generated by users' retweeting behavior outweighs the other discussed methods.
Evolutionary hardware-in-the-loop optimization of a controller for cascaded hydraulic valves

This paper presents a method for evolutionary optimization of a cascaded control structure for an industrial hydraulic valve. It proposes an iterative optimization scheme, in which the controller optimization alters between inner and outer loop parameters. The evolutionary optimization is a combination of hardware-in-the-loop evaluation of the pilot valve and a dynamic simulation of the main valve behavior. The experimental results demonstrate that the intertwined scheme results in faster convergence as it breaks down the overall optimization of the control structure into the less complex subproblems of separated optimization of inner and outer loop.
The Utility Metric: A Novel Method to Assess the Overall Performance of Discrete Brain–Computer Interfaces

A relevant issue in a brain-computer interface (BCI) is the capability to efficiently convert user intentions into correct actions, and how to properly measure this efficiency. Usually, the evaluation of a BCI system is approached through the quantification of the classifier performance, which is often measured by means of the information transfer rate (ITR). A shortcoming of this approach is that the control interface design is neglected, and hence a poor description of the overall performance is obtained for real systems. To overcome this limitation, we propose a novel metric based on the computation of BCI Utility. The new metric can accurately predict the overall performance of a BCI system, as it takes into account both the classifier and the control interface characteristics. It is therefore suitable for design purposes, where we have to select the best options among different components and different parameters setup. In the paper, we compute Utility in two scenarios, a P300 speller and a P300 speller with an error correction system (ECS), for different values of accuracy of the classifier and recall of the ECS. Monte Carlo simulations confirm that Utility predicts the performance of a BCI better than ITR.
Effect of Neuroglobin Genetically Modified Bone Marrow Mesenchymal Stem Cells Transplantation on Spinal Cord Injury in Rabbits

Objective This study aims to investigate the potentially protective effect of neuroglobin (Ngb) gene-modified bone marrow mesenchymal stem cells (BMSCs) on traumatic spinal cord injury (SCI) in rabbits. Methods A lentiviral vector containing an Ngb gene was constructed and used to deliver Ngb to BMSCs. Ngb gene-modified BMSCs were then injected at the SCI sites 24 hours after SCI. The motor functions of the rabbits were evaluated by the Basso–Beattie–Bresnahan rating scale. Fluorescence microscopy, quantitative real-time PCRs, Western blots, malondialdehyde (MDA) tests, and terminal deoxynucleotidyltransferase-mediated UTP end labeling assays were also performed. Results Ngb expression in the Ngb-BMSC group increased significantly. A more significant functional improvement was observed in the Ngb-BMSC group compared with those in the other groups. Traumatic SCI seemingly led to an increase in MDA level and number of apoptotic cells, which can be prevented by Ngb-BMSC treatment. Conclusion This study demonstrates that Ngb gene-modified BMSCs can strengthen the therapeutic benefits of BMSCs in reducing secondary damage and improving the neurological outcome after traumatic SCI. Therefore, the combined strategy of BMSC transplantation and Ngb gene therapy can be used to treat traumatic SCI.
Generation of educational content through gameplay

Language learning games require large quantities of content to be educational. This paper presents a boot-strapping method to generate game content using a combination of existing digital resources and the human computation of game players themselves. It describes a baseline method for hint generation to be used in a two-player game in which players select hints to assist each other. It then shows that the data gathered from player actions contains significant agreement in evaluation of automatically generated content. The quality of this data is verified against data from tasked-based crowdsourcing. Results demonstrate that this type of data may be used to extend and improve the hint generation method.
Evolutionary mechanisms of habitat invasions, using the copepod Eurytemora affinis as a model system

The study of the copepod Eurytemora affinis has provided unprecedented insights into mechanisms of invasive success. In this invited review, I summarize a subset of work from my laboratory to highlight key insights gained from studying E. affinis as a model system. Invasive species with brackish origins are overrepresented in freshwater habitats. The copepod E. affinis is an example of such a brackish invader, and has invaded freshwater habitats multiple times independently in recent years. These invasions were accompanied by the evolution of physiological tolerance and plasticity, increased body fluid regulation, and evolutionary shifts in ion transporter (V‐type H+ ATPase, Na+, K+‐ATPase) activity and expression. These evolutionary changes occurred in parallel across independent invasions in nature and in laboratory selection experiments. Selection appears to act on standing genetic variation during invasions, and maintenance of this variation is likely facilitated through ‘beneficial reversal of dominance’ in salinity tolerance across habitats. Expression of critical ion transporters is localized in newly discovered Crusalis leg organs. Increased freshwater tolerance is accompanied by costs to development time and greater requirements for food. High‐food concentration increases low‐salinity tolerance, allowing saline populations to invade freshwater habitats. Mechanisms observed here likely have relevance for other taxa undergoing fundamental niche expansions.
Simple Principles of Metalearning

The goal of metalearning is to generate useful shifts of inductive bias by adapting the current learning strategy in a ``useful'''' way. Our learner leads a single life during which actions are continually executed according to the system''s internal state and current {\em policy} (a modifiable, probabilistic algorithm mapping environmental inputs and internal states to outputs and new internal states). An action is considered a learning algorithm if it can modify the policy. Effects of learning processes on later learning processes are measured using reward/time ratios. Occasional backtracking enforces success histories of still valid policy modifications corresponding to histories of lifelong reward accelerations. The principle allows for plugging in a wide variety of learning algorithms. In particular, it allows for embedding the learner''s policy modification strategy within the policy itself (self-reference). To demonstrate the principle''s feasibility in cases where conventional reinforcement learning fails, we test it in complex, non-Markovian, changing environments (``POMDPs''''). One of the tasks involves more than $10^{13}$ states, two learners that both cooperate and compete, and strongly delayed reinforcement signals (initially separated by more than 300,000 time steps).
Culex pipiens, an Experimental Efficient Vector of West Nile and Rift Valley Fever Viruses in the Maghreb Region

West Nile fever (WNF) and Rift Valley fever (RVF) are emerging diseases causing epidemics outside their natural range of distribution. West Nile virus (WNV) circulates widely and harmlessly in the old world among birds as amplifying hosts, and horses and humans as accidental dead-end hosts. Rift Valley fever virus (RVFV) re-emerges periodically in Africa causing massive outbreaks. In the Maghreb, eco-climatic and entomologic conditions are favourable for WNV and RVFV emergence. Both viruses are transmitted by mosquitoes belonging to the Culex pipiens complex. We evaluated the ability of different populations of Cx. pipiens from North Africa to transmit WNV and the avirulent RVFV Clone 13 strain. Mosquitoes collected in Algeria, Morocco, and Tunisia during the summer 2010 were experimentally infected with WNV and RVFV Clone 13 strain at titers of 107.8 and 108.5 plaque forming units/mL, respectively. Disseminated infection and transmission rates were estimated 14–21 days following the exposure to the infectious blood-meal. We show that 14 days after exposure to WNV, all mosquito st developed a high disseminated infection and were able to excrete infectious saliva. However, only 69.2% of mosquito strains developed a disseminated infection with RVFV Clone 13 strain, and among them, 77.8% were able to deliver virus through saliva. Thus, Cx. pipiens from the Maghreb are efficient experimental vectors to transmit WNV and to a lesser extent, RVFV Clone 13 strain. The epidemiologic importance of our findings should be considered in the light of other parameters related to mosquito ecology and biology.
A Connectionist Model of Language-Scene Interaction

Recent "visual worlds" studies, wherein researchers study language in context by monitoring eye-movements in a visual scene during sentence processing, have revealed much about the interaction of diverse information sources and the time course of their influence on comprehension. In this study, five experiments that trade off scene context with a variety of linguistic factors are modelled with a Simple Recurrent Network modified to integrate a scene representation with the standard incremental input of a sentence. The results show that the model captures the qualitative behavior observed during the experiments, while retaining the ability to develop the correct interpretation in the absence of visual input.
The Emergence of Online Community Leadership

Compared to traditional organizations, online community leadership processes and how leaders emerge are not well studied. Previous studies of online leadership have often identified leaders as those who administer forums or have high network centrality scores. Although communication in online communities occurs almost exclusively through written words, little research has addressed how the comparative use of language shapes community dynamics. Using participant surveys to identify leading online community members, this study analyzes a year of communication network history and message content to assess whether language use differentiates leaders from other core community participants. We contribute a novel use of textual analysis to develop a model of language use to evaluate the utterances of all participants in the community. We find that beyond communication network position-in terms of formal role, centrality, membership in the core, and boundary spanning-those viewed as leaders by other participants, post a large number of positive, concise posts with simple language familiar to other participants. This research provides a model to study online language use and points to the emergent and shared nature of online community leadership.
Uncertainty-aware localization solution for under-ice autonomous underwater vehicles

Localization underwater has been known to be challenging due to the limited accessibility of the Global Positioning System (GPS) to obtain absolute positions. This becomes more severe in the under-ice environment since the ocean surface is covered with ice, making it more difficult to access GPS or to deploy localization infrastructure. In this paper, a novel solution that minimizes localization uncertainty and communication overhead of under-ice Autonomous Underwater Vehicles (AUVs) is proposed. Existing underwater localization solutions generally rely on reference nodes at ocean surface or on localization infrastructure to calculate positions, and they are not able to estimate the localization uncertainty, which may lead to the increase of localization error in under-ice environments. In contrast, using the notion of external uncertainty (i.e., the position uncertainty as seen by others), our solution can characterize an AUV's position with a probability model. This model is further used to estimate the uncertainty associated with our proposed Doppler-based localization technique - a novel one that can exploit ongoing communications for localization, as well as that associated with the standard distance-based localization. Based on this uncertainty estimate, we further propose algorithms to minimize localization uncertainty and communication overhead. Our solution is emulated and compared against existing solutions, showing improved performance.