Miracle-dz/UniFAR-FTUs huggingface dataset:
SINR-based coverage probability in cellular networks under multiple connections

We give numerically tractable, explicit integral expressions for the distribution of the signal-to-interference-and-noise-ratio (SINR) experienced by a typical user in the down-link channel from the k-th strongest bases stations of a cellular network modelled by Poisson point process on the plane. Our model comprises power-low path-loss model with arbitrarily distributed shadowing, with and without Rayleigh fading. Our results are valid in the whole domain of SINR, in particular for SINR<1, where one observes multiple coverage. In this latter aspect our paper complements previous studies reported in [Dhillon et al. JSAC 2012].
Application of Mean Shift Algorithm in Real-Time Facial Expression Recognition

In a dynamic real-time facial expression recognition, accurate and fast face tracking is a very important preparatory work that is in order to obtain the image sequence of facial expressions. For this problem, we proposed a mean shift algorithm for real-time tracking human faces, and using this method we can obtain the facial expressions image sequence. In order to obtain the initial target of the face image, we used an adaptive skin-color face detection method. Then we used the geometric model based on human face to locate the region of facial expression features, and can estimate the optical flow to calculate the Eigen-flow vectors. At last, hidden semi-Markov model is used for facial expression recognition. The experimental results show that the application of mean shift algorithm in realtime facial expression recognition is very effective for obtaining the facial expression image sequence quickly and accurately. Keywords-mean shift algorithm; face tracking; facial expression recognition; image sequence; HSMM
Biological Control of Apple Ring Rot on Fruit by Bacillus amyloliquefaciens 9001

Apple ring rot disease, caused by Botryosphaeria dothidea (Moug. ex. Fr) Ces. et de Not. , is one of the most important diseases on apple fruits. In this study, strain 9001 isolated from healthy apple fruits from an infested orchard was evaluated for its biocontrol activity against apple ring rot in vitro and in vivo. Strain 9001 showed obvious antagonistic activity to B. dothidea YL-1 when plated on potato dextrose agar. Soaking healthy apples in the bacterial suspensions of strain 9001 prior to artificial inoculation of fungal pathogen resulted in a dramatic decrease in disease incidence when compared to the control. Moreover, either field application in the growth season or postharvest treatment of apples from infected orchards with bacterial suspensions of strain 9001 resulted in significantly reduced disease incidence within the storage period for 4 months at room temperature. Based on the phylogenetic analysis of 16S rRNA and the gyrA gene, strain 9001 was identified as Bacillus amyloliquefaciens. These results indicated that B. amyloliquefaciens 9001 could be a promising agent in biocontrol of apple ring rot on fruit, which might help to minimize the yield loss of apple fruit during the long postharvest period.
Usenix Association 2015 Symposium on Usable Privacy and Security 123 " I Added '!' at the End to Make It Secure " : Observing Password Creation in the Lab

Users often make passwords that are easy for attackers to guess. Prior studies have documented features that lead to easily guessed passwords, but have not probed why users craft weak passwords. To understand the genesis of common password patterns and uncover average users' misconceptions about password strength, we conducted a qualitative interview study. In our lab, 49 participants each created passwords for fictitious banking, email, and news web-site accounts while thinking aloud. We then interviewed them about their general strategies and inspirations. Most participants had a well-defined process for creating passwords. In some cases, participants consciously made weak passwords. In other cases, however, weak passwords resulted from misconceptions, such as the belief that adding " ! " to the end of a password instantly makes it secure or that words that are difficult to spell are more secure than easy-to-spell words. Participants commonly anticipated only very targeted attacks, believing that using a birthday or name is secure if those data are not on Facebook. In contrast, some participants made secure passwords using unpredictable phrases or non-standard capitalization. Based on our data, we identify aspects of password creation ripe for improved guidance or automated intervention.
On the Control of Acute Rodent Malaria Infections by Innate Immunity

Does specific immunity, innate immunity or resource (red blood cell) limitation control the first peak of the blood-stage parasite in acute rodent malaria infections? Since mice deficient in specific immunity exhibit similar initial dynamics as wild-type mice it is generally viewed that the initial control of parasite is due to either limitation of resources (RBC) or innate immune responses. There are conflicting views on the roles of these two mechanisms as there is experimental evidence supporting both these hypotheses. While mathematical models based on RBC limitation are capable of describing the dynamics of primary infections, it was not clear whether a model incorporating the key features of innate immunity would be able to do the same. We examine the conditions under which a model incorporating parasite and innate immunity can describe data from acute Plasmodium chabaudi infections in mice. We find that innate immune response must decay slowly if the parasite density is to fall rather than equilibrate. Further, we show that within this framework the differences in the dynamics of two parasite strains are best ascribed to differences in susceptibility to innate immunity, rather than differences in the strains' growth rates or their propensity to elicit innate immunity. We suggest that further work is required to determine if innate immunity or resource limitation control acute malaria infections in mice.
Trajectory tracking and formation controls for a UAV model that incorporates dynamic effects

The paper solves the trajectory tracking control problem for unmanned aerial vehicles (UAVs) when both the kinematics and dynamics effects are intertwined in the mathematical model. Using the backstepping method we establish a controller for the considered model that ensures exponential convergence to a given reference trajectory. Then, using the concept of virtual vehicles we consider the application of the proposed controller for a group of UAVs in formation flights.
Relatedness influences signal reliability in evolving robots

Communication is an indispensable component of animal societies, yet many open questions remain regarding the factors affecting the evolution and reliability of signalling systems. A potentially important factor is the level of genetic relatedness between signallers and receivers. To quantitatively explore the role of relatedness in the evolution of reliable signals, we conducted artificial evolution over 500 generations in a system of foraging robots that can emit and perceive light signals. By devising a quantitative measure of signal reliability, and comparing independently evolving populations differing in within-group relatedness, we show a strong positive correlation between relatedness and reliability. Unrelated robots produced unreliable signals, whereas highly related robots produced signals that reliably indicated the location of the food source and thereby increased performance. Comparisons across populations also revealed that the frequency for signal production—which is often used as a proxy of signal reliability in empirical studies on animal communication—is a poor predictor of signal reliability and, accordingly, is not consistently correlated with group performance. This has important implications for our understanding of signal evolution and the empirical tools that are used to investigate communication.
The Effect of Categorisation on Sensitivity to Second-Order Relations in Novel Objects

Adults appear to be more sensitive to configural information, including second-order relations (the spacing of features), in faces than in other objects. Superior processing of second-order relations in faces may arise from our experience of identifying faces at the individual level of categorisation (eg Bob versus John) but other objects at the basic level of categorisation (eg table versus chair; Gauthier and Tarr, 1997 Vision Research 37 1673–1682). We simulated this learning difference with novel stimuli (comprised of blobs) by having two groups view the same stimuli but learn to identify the objects only at the basic level (based on the number of constituent blobs) or at both the basic level and individual level (based on the spacing, or second-order relations, of the blobs) of categorisation. Results from two experiments showed that, after training, observers in the individual-level training group were more sensitive to the second-order relations in novel exemplars of the learned category than observers in the basic-level training group. This is the first demonstration of specific improvement in sensitivity to second-order relations after training with non-face stimuli. The findings are consistent with the hypothesis that adults are more sensitive to second-order relations in faces than in other objects, at least in part, because they have more experience identifying faces at the individual level of categorisation.
Inferring Noncoding RNA Families and Classes by Means of Genome-Scale Structure-Based Clustering

The RFAM database defines families of ncRNAs by means of sequence similarities that are sufficient to establish homology. In some cases, such as microRNAs and box H/ACA snoRNAs, functional commonalities define classes of RNAs that are characterized by structural similarities, and typically consist of multiple RNA families. Recent advances in high-throughput transcriptomics and comparative genomics have produced very large sets of putative noncoding RNAs and regulatory RNA signals. For many of them, evidence for stabilizing selection acting on their secondary structures has been derived, and at least approximate models of their structures have been computed. The overwhelming majority of these hypothetical RNAs cannot be assigned to established families or classes. We present here a structure-based clustering approach that is capable of extracting putative RNA classes from genome-wide surveys for structured RNAs. The LocARNA (local alignment of RNA) tool implements a novel variant of the Sankoff algorithm that is sufficiently fast to deal with several thousand candidate sequences. The method is also robust against false positive predictions, i.e., a contamination of the input data with unstructured or nonconserved sequences. We have successfully tested the LocARNA-based clustering approach on the sequences of the RFAM-seed alignments. Furthermore, we have applied it to a previously published set of 3,332 predicted structured elements in the Ciona intestinalis genome (Missal K, Rose D, Stadler PF (2005) Noncoding RNAs in Ciona intestinalis. Bioinformatics 21 (Supplement 2): i77–i78). In addition to recovering, e.g., tRNAs as a structure-based class, the method identifies several RNA families, including microRNA and snoRNA candidates, and suggests several novel classes of ncRNAs for which to date no representative has been experimentally characterized.
Mining Hubble Space Telescope images

Astrophysicists rely on crowd sourced initiatives to classify galaxies in large surveys. The next generation of telescopes will lead to a revolutionary increase in the amount of unlabelled data available to astrophysicists making crowd sourcing infeasible. To cope with this significant increase in data astrophysicists will need unsupervised techniques. In this paper we show that a model using unsupervised learning can automatically categorise galaxies in Hubble Space Telescope survey images. We discover the optimum parameter combination using a grid search like approach. We also present a detailed analysis of the effect of varying parameters. We analyse Hubble Space Telescope images and evaluate the model's performance for identifying major categories of galaxies and for identifying a type of lensed galaxy. We find that the pixel intensity power spectrum is the most effective at identifying elliptical, spiral and background galaxies whereas the rotationally invariant feature transform is more effective at detecting a type of lensed galaxy that occurs as a result of strong lensing.
Learning from incomplete data with infinite imputations

We address the problem of learning decision functions from training data in which some attribute values are unobserved. This problem can arise, for instance, when training data is aggregated from multiple sources, and some sources record only a subset of attributes. We derive a generic joint optimization problem in which the distribution governing the missing values is a free parameter. We show that the optimal solution concentrates the density mass on finitely many imputations, and provide a corresponding algorithm for learning from incomplete data. We report on empirical results on benchmark data, and on the email spam application that motivates our work.
Mammalian Smaug Is a Translational Repressor That Forms Cytoplasmic Foci Similar to Stress Granules*

Cytoplasmic events depending on RNA-binding proteins contribute to the fine-tuning of gene expression. Sterile α motif-containing RNA-binding proteins constitute a novel family of post-transcriptional regulators that recognize a specific RNA sequence motif known as Smaug recognition element (SRE). The Drosophila member of this family, dSmaug, triggers the translational repression and deadenylation of maternal mRNAs by independent mechanisms, and the yeast homologue Vts1 stimulates degradation of SRE-containing messengers. Two homologous genes are present in the mammalian genome. Here we showed that hSmaug 1, encoded in human chromosome 14, represses the translation of reporter transcripts carrying SRE motifs. When expressed in fibroblasts, hSmaug 1 forms cytoplasmic granules that contain polyadenylated mRNA and the RNA-binding proteins Staufen, TIAR, TIA-1, and HuR. Smaug 1 foci are distinct from degradation foci. The murine protein mSmaug 1 is expressed in the central nervous system and is abundant in post-synaptic densities, a subcellular region where translation is tightly regulated by synaptic stimulation. Biochemical analysis indicated that mSmaug 1 is present in synaptoneurosomal 20 S particles. These results suggest a role for mammalian Smaug 1 in RNA granule formation and translation regulation in neurons.
Electrophysiological Evidence for Endogenous Control of Attention in Switching between Languages in Overt Picture Naming

Language switching in bilingual speakers requires attentional control to select the appropriate language, for example, in picture naming. Previous language-switch studies used the color of pictures to indicate the required language thereby confounding endogenous and exogenous control. To investigate endogenous language control, our language cues preceded picture stimuli by 750 msec. Cue-locked event-related potentials (ERPs) were measured while Dutch–English bilingual speakers overtly named pictures. The response language on consecutive trials could be the same (repeat trials) or different (switch trials). Naming latencies were longer on switch than on repeat trials, independent of the response language. Cue-locked ERPs showed an early posterior negativity for switch compared to repeat trials for L2 but not for L1, and a late anterior negativity for switch compared to repeat trials for both languages. The early switch–repeat effect might reflect disengaging from the nontarget native language, whereas the late switch–repeat effect reflects engaging in the target language. Implications for models of bilingual word production are discussed.
Multiple scattering among vias in planar waveguides using preconditioned SMCG method

Full-wave modeling for cylindrical vias in planar waveguides is formulated using Foldy-Lax multiple scattering equations. Recently, a sparse-matrix canonical-grid method based on fast Fourier transform and an iterative algorithm was proposed to solve a large-scale via problem. In this paper, we further improve computational efficiency by a preconditioning scheme based on the dominant information contained in the near field. We also discuss two methods-the sparse-matrix LU decomposition and sparse matrix iterative methods-for constructing the preconditioner, providing the tradeoffs between CPU time and memory. Results show an order of magnitude improvement over the nonpreconditioned case on the convergence rate. As an example, for 20 000 vias simulation, the solution time (per excitation and per frequency) is approximately 13 s per iteration, and 75 min to reach convergence in 320 iterations with memory requirements of 560 MB on a single Pentium 2.4-GHz processor machine. Numerical simulations are illustrated for physical problems such as ball-grid array and large-scale randomly distributed vias, where we have observed the shielding effects of the terminated vias that reduce the coupling between vias.
Creation of a Bacterial Cell Controlled by a Chemically Synthesized Genome

Let There Be Life The DNA sequence information from thousands of genomes is stored digitally as ones and zeros in computer memory. Now, Gibson et al. (p. 52, published online 20 May; see the cover; see the Policy Forum by Cho and Relman) have brought together technologies from the past 15 years to start from digital information on the genome of Mycoplasma mycoides to chemically synthesize the genomic DNA as segments that could then be assembled in yeast and transplanted into the cytoplasm of another organism. A number of methods were also incorporated to facilitate testing and error correction of the synthetic genome segments. The transplanted genome became established in the recipient cell, replacing the recipient genome, which was lost from the cell. The reconstituted cells were able to replicate and form colonies, providing a proof-of-principle for future developments in synthetic biology. A synthetic Mycoplasma mycoides genome transplanted into M. capricolum was able to control the host cell. We report the design, synthesis, and assembly of the 1.08–mega–base pair Mycoplasma mycoides JCVI-syn1.0 genome starting from digitized genome sequence information and its transplantation into a M. capricolum recipient cell to create new M. mycoides cells that are controlled only by the synthetic chromosome. The only DNA in the cells is the designed synthetic DNA sequence, including “watermark” sequences and other designed gene deletions and polymorphisms, and mutations acquired during the building process. The new cells have expected phenotypic properties and are capable of continuous self-replication.
Some Advances in Role Discovery in Graphs

Role discovery in graphs is an emerging area that allows analysis of complex graphs in an intuitive way. In contrast to other graph prob- lems such as community discovery, which finds groups of highly connected nodes, the role discovery problem finds groups of nodes that share similar graph topological structure. However, existing work so far has two severe limitations that prevent its use in some domains. Firstly, it is completely unsupervised which is undesirable for a number of reasons. Secondly, most work is limited to a single relational graph. We address both these lim- itations in an intuitive and easy to implement alternating least squares framework. Our framework allows convex constraints to be placed on the role discovery problem which can provide useful supervision. In par- ticular we explore supervision to enforce i) sparsity, ii) diversity and iii) alternativeness. We then show how to lift this work for multi-relational graphs. A natural representation of a multi-relational graph is an order 3 tensor (rather than a matrix) and that a Tucker decomposition allows us to find complex interactions between collections of entities (E-groups) and the roles they play for a combination of relations (R-groups). Existing Tucker decomposition methods in tensor toolboxes are not suited for our purpose, so we create our own algorithm that we demonstrate is pragmatically useful.
An Emulator for Executing IA-32 Applications on ARM-Based Systems

Virtual Machine (VM) can not only release the processor designers from the burden of ensuring crossplatform compatibility but also provide new opportunities for innovation. Instruction Set Architecture (ISA) emulation is the key aspect of a VM. This paper describes the design and implementation of TransARM-IU, a use-level ISA emulator that supports IA-32 applications on ARMbased systems. This paper also discusses several dedicated solutions to several crucial issues related to the development of TransARM-IU, such as the Executable and Linking Format resolution and hybrid threaded interpretation. In particular, conventional interpretation is implemented in a centralized style which consumes much more execution time. A hybrid threaded interpretation method is proposed to improve the efficiency of the emulator by appending a portion of the simple decoding and dispatching code to the end of each of the instruction interpreter routines. For performance evaluation, we selected 6 benchmarks from MiBench and carried out the experiments on two real ARM-based systems. Experimental results demonstrate the correctness of TransARM-IU in terms of ISA emulation and indicate that TransARM-IU is competitive to other ISA emulators.
To build a synapse: signaling pathways in neuromuscular junction assembly

Synapses, as fundamental units of the neural circuitry, enable complex behaviors. The neuromuscular junction (NMJ) is a synapse type that forms between motoneurons and skeletal muscle fibers and that exhibits a high degree of subcellular specialization. Aided by genetic techniques and suitable animal models, studies in the past decade have brought significant progress in identifying NMJ components and assembly mechanisms. This review highlights recent advances in the study of NMJ development, focusing on signaling pathways that are activated by diffusible cues, which shed light on synaptogenesis in the brain and contribute to a better understanding of muscular dystrophy.
Role of ABC and Solute Carrier Transporters in the Placental Transport of Lamivudine

ABSTRACT Lamivudine is one of the antiretroviral drugs of choice for the prevention of mother-to-child transmission (MTCT) in HIV-positive women. In this study, we investigated the relevance of drug efflux transporters P-glycoprotein (P-gp) (MDR1 [ABCB1]), BCRP (ABCG2), MRP2 (ABCC2), and MATE1 (SLC47A1) for the transmembrane transport and transplacental transfer of lamivudine. We employed in vitro accumulation and transport experiments on MDCK cells overexpressing drug efflux transporters, in situ-perfused rat term placenta, and vesicular uptake in microvillous plasma membrane (MVM) vesicles isolated from human term placenta. MATE1 significantly accelerated lamivudine transport in MATE1-expressing MDCK cells, whereas no transporter-driven efflux of lamivudine was observed in MDCK-MDR1, MDCK-MRP2, and MDCK-BCRP monolayers. MATE1-mediated efflux of lamivudine appeared to be a low-affinity process (apparent Km of 4.21 mM and Vmax of 5.18 nmol/mg protein/min in MDCK-MATE1 cells). Consistent with in vitro transport studies, the transplacental clearance of lamivudine was not affected by P-gp, BCRP, or MRP2. However, lamivudine transfer across dually perfused rat placenta and the uptake of lamivudine into human placental MVM vesicles revealed pH dependency, indicating possible involvement of MATE1 in the fetal-to-maternal efflux of the drug. To conclude, placental transport of lamivudine does not seem to be affected by P-gp, MRP2, or BCRP, but a pH-dependent mechanism mediates transport of lamivudine in the fetal-to-maternal direction. We suggest that MATE1 might be, at least partly, responsible for this transport.