Miracle-dz/UniFAR-FTUs huggingface dataset:
SAMPLE: Statistical Network Link Modelling in an On-Demand Probabilistic Routing Protocol for Ad Hoc Networks

Existing routing protocols for multi-hop wireless networks assume an idealised wireless network in which all links in the network are either on or off and where all functioning links are equally good. Such a model interprets the fraction of packets that are dropped due to contention or interference as broken links, which can in turn lead to increased routing traffic and radio contention. This paper introduces a statistical model of estimated link reliability in wireless networks based on sampling attempted and successful packet transmissions in the network. We present a path metric based on the link model to capture the cost of routes in the network. We investigate both the link model and the path metric in an on-demand probabilistic routing protocol called SAMPLE that is inspired by reinforcement learning techniques. Different scenario-based performance evaluations of the protocol in NS-2 are presented. In comparisons with AODV and DSR, SAMPLE exhibits improved performance in both lossy and congested wireless networks.
Mitigating Unfairness Due to Physical Layer Capture in Practical 802.11 Mesh Networks

In this paper, we describe <italic>FairMesh</italic>, which is the first attempt at mitigating the unfairness <italic>arising from physical layer capture (PLC)</italic> in 802.11 mesh networks. In the presence of PLC, which is surprisingly common in practical mesh networks, existing state-of-art solutions either fail to correctly identify the sender that needs to be throttled or are too aggressive in reducing the sending rate. FairMesh is able to accurately detect unfairness quickly and employs a simple <inline-formula><tex-math>$CW_{min}$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="wang-ieq1-2315796.gif"/></alternatives></inline-formula> adjustment algorithm to achieve approximate max-min fairness. Our key insight is that the nodes that cause an unfair situation to arise and can act to remedy it are often distinct from the ones that can accurately assess the degree of unfairness. To the best of our knowledge, we are the first to decouple the detection and assessment of unfairness from the remedial action. A key strength of our approach is its <italic>simplicity</italic>, which makes it amenable for deployment in practical 802.11 mesh networks to allow an arbitrary number of flows to operate concurrently without modifications to the 802.11 MAC. We show via simulation and with experiments on a 20-node outdoor 802.11 wireless mesh testbed that FairMesh has many desirable properties. First, it is fully distributed and has negligible control overhead. Second, it achieves approximate max-min fairness, and can be modified to support a different notion of fairness (e.g., proportional fairness). Third, it can handle multiple (more than two) competing links and can scale up to mesh networks with tens of nodes. Fourth, it remains efficient under high data rates and high loss rates. Finally, FairMesh interacts well with TCP and maintains good fairness when a multi-hop flow competes with a single-hop flow.
Efficient Data Compression Methods for Multidimensional Sparse Array Operations Based on the EKMR Scheme

We have proposed the extended Karnaugh map representation (EKMH) scheme for multidimensional array representation. We propose two data compression schemes, EKMR compressed row/column storage (ECRS/ECCS), for multidimensional sparse arrays based on the EKMR scheme. To evaluate the proposed schemes, we compare them to the CRS/CCS schemes. Both theoretical analysis and experimental tests were conducted. In the theoretical analysis, we analyze the CRS/CCS and the ECRS/ECCS schemes in terms of the time complexity, the space complexity, and the range of their usability for practical applications. In experimental tests, we compare the compressing time of sparse arrays and the execution time of matrix-matrix addition and matrix-matrix multiplication based on the CRS/CCS and the ECRS/ECCS schemes. The theoretical analysis and experimental results show that the ECRS/ECCS schemes are superior to the CRS/CCS schemes for all the evaluated criteria, except the space complexity in some case.
Numerical study of holographic electromagnetic induction technique for imaging arbitrarily shaped biological objects

Abstract This manuscript presents the working principle of holographic electromagnetic induction (HEMI) method for imaging small lesions in arbitrarily shaped biological objects. A computer model is developed to demonstrate a scattered magnetic field from optically small dielectric and arbitrarily shaped magnetic objects adsorbed onto a planar dielectric substrate. Wave propagation is included in the numerical model, particularly in modelling of biomedical applications. Results show that the HEMI image method has an ability to produce images of biological objects. Several potential medical imaging applications of the HEMI are available, including head imaging for brain stroke detection, breast imaging for breast cancer detection, diabetic ulcers and burns.
Comment on Iyengar, Peters, and Kinder (Vol. 76, December 1982, pp. 848-858)

Beck, P. A. , & Jennings, M. K. Political periods and political participation. American Political Science Review, 1979, 73, 737-750. Kinder, D. R. , & Abelson, R. P. Appraising presidential candidates: personality and affect in the 1980 campaign. Paper presented at the annual meetings of the American Political Science Association, New York, 1981. Markus, G. B. Political attitudes during an election year: a report on the NES panel study. American Political Science Review, 1982, 76, 538-560.
Efficient network level beamforming training for IEEE 802.11ad WLANs

Millimeter-wave (mmWave) communication is a promising technology that enables high rate (Giga-bit) multi-media applications. In this paper, we consider the directional multigigabit (DMG) transmission problem in IEEE 802.11ad wireless local area network (WLAN). We propose an amendment to the original MAC design of IEEE 802.11ad to eliminate the need to perform beamforming training during station (STA) to STA communication. During A-BFT access period, all STAs beamform with the Access Point (AP) using different time slots. Our mechanism makes STAs listen to other STAs beamform with the AP. We call this Intelligent Listening during A-BFT (ILA). Using this mechanism, each DMG STA can listen to other DMG STAs sector sweep with the AP and get their approximate beam direction which is later used during STA-STA beamforming to considerably reduce the beamforming overhead. We demonstrate via simulations that ILA mechanism considerably improves the network throughput at higher network size.
he corticotropin-releasing factor-like diuretic hormone 44 ( DH 44 ) nd kinin neuropeptides modulate desiccation and starvation olerance in Drosophila melanogaster

Malpighian tubules are critical organs for epithelial fluid transport and stress tolerance in insects, and are under neuroendocrine control by multiple neuropeptides secreted by identified neurons. Here, we demonstrate roles for CRF-like diuretic hormone 44 (DH44) and Drosophila melanogaster kinin (Dromekinin, DK) in desiccation and starvation tolerance. Gene expression and labelled DH44 ligand binding data, as well as highly selective knockdowns and/or neuronal ablations of DH44 in neurons of the pars intercerebralis and DH44 receptor (DH44-R2) in Malpighian tubule principal cells, indicate that suppression of DH44 signalling improves desiccation tolerance of the intact fly. Drome-kinin receptor, encoded by the leucokinin receptor gene, LKR, is expressed in DH44 neurons as well as in stellate cells of the Malpighian tubules. LKR knockdown in DH44-expressing neurons reduces europeptide receptor rosophila melanogaster Malpighian tubule-specific LKR, suggesting interactions between DH44 and LK signalling pathways. Finally, although a role for DK in desiccation tolerance was not defined, we demonstrate a novel role for Malpighian tubule cell-specific LKR in starvation tolerance. Starvation increases gene expression of epithelial LKR. Also, Malpighian tubule stellate cell-specific knockdown of LKR significantly reduced starvation tolerance, demonstrating a role for neuropeptide signalling during starvation stress. © 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license
MODELING FASHION Anonymous ICME submission

We propose a method to try to model fashionable dresses in this paper. We first discover common visual patterns that appear in dress images using a human-in-the-loop, active clustering approach. A fashionable dress is expected to contain certain visual patterns which make it fashionable. An approach is proposed to jointly identify fashionable visual patterns and learn a discriminative fashion classifier. The results show that interesting fashionable patterns can be discovered on a newly collected dress dataset. Furthermore, our model can also achieve high accuracy on distinguishing fashionable and unfashionable dresses.
Multilabel Tagging of Discourse Relations in Ambiguous Temporal Connectives

Many annotation schemes for discourse relations allow combinations such as temporal+cause (for events that are temporally and causally related to each other) and temporal+contrast (for contrasts between subsequent time spans, or between events that are temporally coextensive). However, current approaches for the automatic classification of discourse relations are limited to producing only one relation and disregard the others. We argue that the information contained in these ‘additional’ relations is indeed useful and present an approach to tag multiple fine-grained discourse relations in ambiguous connectives from the German T¨ uBa-D/Z corpus. Using a rich feature set, we show that good accuracy is possible even for inferred relations that are not part of the connective’s ‘core’ meaning.
Injecting Sentiment Information in Context-aware Convolutional Neural Networks

Deep learning models achieved remarkable results in Computer Vision, Speech recognition, Natural Language Processing and Information Retrieval. In this work, we extend a Convolutional Neural Networks (CNNs) for the Sentiment Analysis in Twitter task, as this architecture achieved state-of-the-art results in [3, 4]. In particular, this architecture has been shown effective when a proper pre-training step is adopted to perform the early estimation of the network parameters: in [4] it is suggested to generate pre-training data starting from a randomic selection of Twitter messages annotated with simple heuristics, e.g. the presence of specific emoticons in messages. We improve the quality of such CNN architecture in two ways. First, we propose to adopt a contextual model [5] to select pre-training material from the conversations to which training messages appear, as opposed to an arbitrary selection of messages. In this way, we aim at selecting pre-training messages that could better reflect the topics of the targeted data. Second, we promote the adoption of a multi-channel schema [3] for representing the input data to the CNN. A first channel is used to accommodate lexical information provided by Distributional Models of Lexical Semantics, i.e. a vector representation of words provided by a Word Embedding. The second channel is adopted to represent sentiment oriented information as it is provided by a polarity lexicon. In particular, the sentiment oriented vectors adopted in this study refer to the automatically acquired Distributional Polarity Lexicons, as proposed in [1]. The experimental evaluation shows that the proposed solutions are beneficial w.r. t the targeted task in two languages, i.e. English and Italian. The full version of this paper is provided in [2] and it is available in the SocialNLP@IJCAI 2016 proceedings.
Making the link between strategy and process model collections: a multi-layered approach

This paper proposes a multi-level approach that links strategic goals with an organization's collection processes, based on the Business Motivation Model (BMM) and the Business Process Management Notation (BPMN). A multi-layered metamodel is proposed together with a web-based support tool to manage the entire approach. The approach is illustrated and validated through a proof of concept application in a real process of a large company that provides IT services, where the advantages of the proposed model are made clear. Furthermore, the approach is evaluated by means of a systematic comparison with related work and through an empirical evaluation involving users in a real organization. KeywordsBusiness Process Management; BPMN; BMM; Multilayer; Process Collections; Strategic Alignment
Cyclic Changes of Dust-to-Gas Ratio

We discuss the time variation of dust-to-gas mass ratio in spiral galaxies using the multi-phase model of interstellar medium. The typical timescale of the phase change of an interstellar gas is ∼ 10 7 – 8 yr in spiral galaxies. Since the phase transition changes the ﬁlling factor of the cold gas where the dust growth occurs, the dust growth rate varies on that timescale. In order to examine the response of the dust-to-gas ratio to the phase transition, we construct a model of the time evolution of the dust-to-gas ratio. We adopt the three phase model for the interstellar gas and the Ikeuchi-Tomita model for the mass exchange among the phases. According to the model, three types of solutions are possible: [1] all the gas transformed to a hot gas; [2] a stable stationary state of three phases is realized; [3] the ﬁlling factor of each phase cyclically changes. For each of the three types of solutions, the dust-to-gas ratio behaves as follows:
QML : Quantum data and control

We introduce the language QML, a functional language for quantum computations on finite types. QML introduces quantum data and control structures, and integrates reversible and irreversible quantum computation. QML is based on strict linear logic, hence weakenings, which may lead to decoherence, have to be explicit. We present an operational semantics of QML programs using quantum circuits, and a denotational semantics using superoperators.
Supported employment for persons with traumatic brain injury: a preliminary investigation of long-term follow-up costs and program efficiency.

OBJECTIVE To investigate the long-term follow-up costs of supported employment as well as the wage and employment characteristics for individuals with moderate to severe traumatic brain injury (TBI) who participated in supported employment services over a 14-year time period. DESIGN Longitudinal design with prospectively collected data. SETTING A university-based supported employment program that uses the individual placement model of supported employment. PARTICIPANTS Fifty-nine individuals with moderate to severe TBI who were consecutively referred for supported employment services. The sample was restricted to individuals who were placed into a least 1 supported employment position during the study period. INTERVENTIONS Not applicable. MAIN OUTCOME MEASURES Data were collected on clients placed into at least 1 competitive supported employment position from 1985 to 1999. Analyses were performed to examine the costs of supported employment, employment characteristics (eg, wages, length of employment), and benefit-cost ratios of supported employment for individuals with TBI. RESULTS The average length of employment for the current sample was 42.58 months. Average gross earnings were US dollars 26,129.74 for individuals during their entire duration of employment. Billing charges accrued for employment services averaged US dollars 10,349.37. Individuals with TBI earned an average of US dollars 17,515 more than the costs associated with their supported employment. CONCLUSIONS Our investigation provides additional support for the conclusion that supported employment is cost effective for individuals with disabilities, including individuals with TBI, and that the costs of supported employment decrease over time.
Convolutive Blind Source Separation Using an Iterative Least-Squares Algorithm for Non-Orthogonal Approximate Joint Diagonalization

In this paper, we present an approach of recovering signal waveforms of speech sources from observed signals in noisy and reverberant environments. The approach is based on approximate joint diagonalization estimate to provide interference suppression of source signals and reduce echoes and distortions of separated signals. In the proposed approach, the mixing matrix is estimated by minimizing the constrained direct least-squares (LS) criterion in direct model. Exclusively under the condition where the estimated mixing matrix is not of full rank, it is replaced by a full-rank matrix. The unmixing matrix from the estimated mixing matrix is obtained by setting the frequency response of the composite mixing-unmixing filter to identity matrix. The cross-spectral density diagonal matrices of the source signals are precisely estimated by minimizing the indirect LS criterion in indirect model. These operations are fulfilled by using alternating least-squares algorithm. The correlation between the interfrequency power ratios is used to prevent a misalignment permutation of the unmixing matrix. Finally, we compare the proposed BSS with a number of conventional BSS methods in noisy and reverberant environments under both artificial and actual conditions.
Improved algorithms for reaction path following: Higher‐order implicit algorithms

Eight new algorithms for reaction path following are presented, ranging from third order to sixth order. Like the second‐order algorithm [J. Chem. Phys. 90, 2154 (1989)] these are implicit methods, i.e., they rely on the tangent (and in some cases the curvature) at the endpoint of the step. The tangent (and the curvature, if needed) are obtained by a constrained optimization using only the gradient. At most, only one Hessian calculation is needed per step along the path. The various methods are applied to the Muller–Brown surface and to a new surface whose reaction path is known analytically to test their ability to follow the reaction path and to reproduce the curvature along the path.
Towards Rich Sensor Data Representation - Functional Data Analysis Framework for Opportunistic Mobile Monitoring

The rise of new lightweight and cheap sensors has opened the door wide for new sensing applications. Mobile opportunistic sensing is one type of these applications which has been adopted in multiple citizen science projects including air pollution monitoring. However, the opportunistic nature of sensing along with campaigns being mobile and sensors being subjected to noise and missing values leads to asynchronous and unclean data. Analyzing this type of data requires cumbersome and time-consuming preprocessing. In this paper, we introduce a novel framework to treat such type of data by seeing data as functions rather than vectors. The framework introduces a new data representation model along with a high-level query language and an analysis module. Copyright
Vertrauen ist gut

Zusammenfassung. Vertrauen ist eine entscheidende Komponente innerhalb des Breiten- und Spitzensports mit kognitiver, emotionaler, und verhaltensbezogener Relevanz, die sich auf unterschiedlichen Ebenen (Mikro-, Meso-, Makro-Ebene) manifestiert. Zahlreiche sportrelevante Themen legen einen Zusammenhang zu Vertrauen nahe (z. B. Doping oder Wettmanipulation). Auch kann angenommen werden, dass die Vertrauenswurdigkeit eines Athleten, eines Trainers oder eines Sportverbands Einfluss in interpersonalen Beziehungen im Sport hat. Das psychologische Konzept des Vertrauens hat aber bislang im Sportkontext nur geringe Beachtung gefunden. Mit der vorliegenden Studie soll die Adaption und Validierung eines aus dem organisationalen Kontext stammenden Fragebogens zur Erfassung von Vertrauenswurdigkeit im Sport vorgestellt werden. Der 15 Items umfassende Fragebogen besteht aus drei Subskalen, welche die Antezedenzien von Vertrauen, Fahigkeit, Wohlwollen und Integritat abbilden. Eine konfirmatorische Faktorenanalyse best.
Human Papillomavirus Oncoprotein E6 Inactivates the Transcriptional Coactivator Human ADA3

ABSTRACT High-risk human papillomaviruses (HPVs) are associated with carcinomas of the cervix and other genital tumors. The HPV oncoprotein E6 is essential for oncogenic transformation. We identify here hADA3, human homologue of the yeast transcriptional coactivator yADA3, as a novel E6-interacting protein and a target of E6-induced degradation. hADA3 binds selectively to the high-risk HPV E6 proteins and only to immortalization-competent E6 mutants. hADA3 functions as a coactivator for p53-mediated transactivation by stabilizing p53 protein. Notably, three immortalizing E6 mutants that do not induce direct p53 degradation but do interact with hADA3 induced the abrogation of p53-mediated transactivation and G1 cell cycle arrest after DNA damage, comparable to wild-type E6. These findings reveal a novel strategy of HPV E6-induced loss of p53 function that is independent of direct p53 degradation. Given the likely role of the evolutionarily conserved hADA3 in multiple coactivator complexes, inactivation of its function may allow E6 to perturb numerous cellular pathways during HPV oncogenesis.
GSMPA : A Core Calculus With Generally Distributed Durations 1

We present a process algebra for the performance modeling and evaluation of concurrent systems whose activity durations are expressed through general probability distributions. We first determine the class of generalized semi-Markov processes (GSMPs) as being the class of stochastic processes on which we must rely for performance evaluation to be possible. Then we argue that in this context the right semantics for algebraic terms is a variant of the ST semantics which accounts for both functional and performance aspects. The GSMP based process algebra we propose is introduced together with its formal semantics, an example of performance evaluation, and a notion of probabilistic bisimulation based equivalence accounting for action durations which is shown to be a congruence. 1. An extended abstract of this paper will appear in Proc. of the 9th Int. Conf. on Concurrency Theory (CONCUR ’98), Nice (France), September 1998. 2. University of Bologna, Department of Computer Science, Mura Anteo Zamboni 7, 40127 Bologna, Italy. E-mail: fbravetti, bernardo, gorrierig@cs.unibo. it
3D thermal-ADI: an efficient chip-level transient thermal simulator

Recent studies show that the nonuniform thermal distribution on the substrate and interconnects has impact on the circuit reliability and performance. Hence three-dimensional (3-D) thermal analysis is crucial to analyze these effects. In this paper, we present and develop an efficient 3-D transient thermal simulator based on the alternating direction implicit (ADI) method for large scale temperature estimation problems. Our simulator, 3D Thermal-ADI, not only has a linear runtime and memory requirement, but also is unconditionally stable. Detailed analysis of the 3-D nonhomogeneous cases and boundary conditions for on-chip VLSI applications are introduced and presented. Extensive experimental results show that our algorithm is not only orders of magnitude faster than the traditional thermal simulation algorithms, but is also highly accurate and memory efficient. The temperature profile of steady state can be reached in few iterations. The software is avaiable on the web [1].
Stable non-BPS states in string theory: a pedagogical review

We present a pedagogical review of the stable non-BPS states in string theory which have recently attracted some attention in the literature. In particular, following the analysis of A. Sen, we discuss in detail the case of the stable non-BPS D-particle of Type I theory whose existence is predicted (and required) by the heterotic/Type I duality. We show that this D-particle originates from an unstable bound state formed by a D1/anti-D1 pair of Type IIB in which the tachyon field acquires a solitonic kink configuration. The mechanism of tachyon condensation is discussed first at a qualitative level and then with an exact conformal field theory analysis.