Miracle-dz/UniFAR-FTUs huggingface dataset:
Screening Rules for Lasso with Non-Convex Sparse Regularizers

Leveraging on the convexity of the Lasso problem , screening rules help in accelerating solvers by discarding irrelevant variables, during the optimization process. However, because they provide better theoretical guarantees in identifying relevant variables, several non-convex regulariz-ers for the Lasso have been proposed in the literature. This work is the first that introduces a screening rule strategy into a non-convex Lasso solver. The approach we propose is based on a iterative majorization-minimization (MM) strategy that includes a screening rule in the inner solver and a condition for propagating screened variables between iterations of MM. In addition to improve efficiency of solvers, we also provide guarantees that the inner solver is able to identify the zeros components of its critical point in finite time. Our experimental analysis illustrates the significant computational gain brought by the new screening rule compared to classical coordinate-descent or proximal gradient descent methods.
The State of the Art in Multilayer Network Visualization

Modelling relationship between entities in real‐world systems with a simple graph is a standard approach. However, reality is better embraced as several interdependent subsystems (or layers). Recently, the concept of a multilayer network model has emerged from the field of complex systems. This model can be applied to a wide range of real‐world data sets. Examples of multilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domain of graph visualization, there are many systems which visualize data sets having many characteristics of multilayer graphs. This report provides a state of the art and a structured analysis of contemporary multilayer network visualization, not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those developing systems across application domains. We have explored the visualization literature to survey visualization techniques suitable for multilayer graph visualization, as well as tools, tasks and analytic techniques from within application domains. This report also identifies the outstanding challenges for multilayer graph visualization and suggests future research directions for addressing them.
The cost of drug use in adolescence: Young people, money and substance abuse

It is now common for young people in full-time compulsory education to hold part-time jobs. However, while the 1990s experienced a rise in illicit drug use particularly among young people and an increase in the level of interest in identifying factors associated with drug use, little attention has been paid to the influence of the money young people have to spend and its potential links with drug use. Four thousand five hundred and twenty-four young people living in Northern Ireland completed a questionnaire in school year 10 (aged 13/14 years). The findings suggested that there was a positive association between the amount of money young people received (and its source) and higher rates of drug use. The study concludes that money, and how it is spent by young people, may be an important factor for consideration when investigating drug use during adolescence. The findings may help inform drug prevention strategies, particularly through advice on money management, and taking responsibility for their own money.
Response of Greengram [Vigna radiata (L.) Wilczek] Grown in Herbicide-Amended Soil to Inoculation with Bradyrhizobium sp. (vigna) MRM6

The present study was conducted to determine the plant growth-promoting activities of Bradyrhizobium sp. ( vigna ) strain MRM6 grown in the presence and absence of the selected herbicides, quizalafop-p-ethyl and clodinafop. The herbicide tolerant Bradyrhizobium sp. ( vigna ) strain MRM6 was further tested for bioremediation and plant growth promoting potential using greengram as a test crop, grown in soils treated with quizalafop-p-ethyl and clodinafop, at both recommended and higher dose rates. The quizalafop-p-ethyl and clodinafop tolerant Bradyrhizobium sp. ( vigna ) strain MRM6 recovered from the nodules of greengram plants produced a substantial amount of indole acetic acid, siderophores, hydrogen cyanide and ammonia, both in the presence and absence of technical grade quizalafop-p-ethyl and clodinafop under in vitro conditions. Both quizalafop-p-ethyl [40 (recommended dose), 80, and 120 µg kg -1 soil] and clodinafop [400 (recommended dose), 800, and 1200 µg kg -1 soil] decreased the growth of Bradyrhizobium sp . MRM6-inoculated and un-inoculated plants. Quizalafop-p-ethyl at all concentrations showed more phytotoxicity and affected the growth in terms of nodulation, total dry biomass, nutrients (nitrogen and phosphorus) uptake and seed yield compared to clodinafop or un-inoculated control. When the inoculant strain MRM6 was used with any concentration of the two herbicides, the growth and nodulation parameters of the plants were relatively better compared to the plants grown in soils treated solely (without inoculant) with the same concentration of each herbicide. For example, when strain MRM6 was used with 1200 µg clodinafop kg -1 soil, it increased the symbiotic attributes (nodule number, nodule dry mass, leghaemoglobin), whole biomass, root N, shoot N, root P, shoot P, seed yield, and grain protein by 14%, 62%, 60%, 102%, 23%, 31%, 9%, 10%, 72% and 4%, respectively, compared to the un-inoculated treatment having the same concentration of clodinafop. The present findings suggest that the bradyrhizobial strain MRM6 endowed with multiple properties could be used to facilitate the productivity of greengram under herbicide-stressed soils.
HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed images. Hyperspectral imagery includes varying bands of images. Convolutional neural network (CNN) is one of the most frequently used deep learning-based methods for visual data processing. The use of CNN for HSI classification is also visible in recent works. These approaches are mostly based on 2-D CNN. On the other hand, the HSI classification performance is highly dependent on both spatial and spectral information. Very few methods have used the 3-D-CNN because of increased computational complexity. This letter proposes a hybrid spectral CNN (HybridSN) for HSI classification. In general, the HybridSN is a spectral–spatial 3-D-CNN followed by spatial 2-D-CNN. The 3-D-CNN facilitates the joint spatial–spectral feature representation from a stack of spectral bands. The 2-D-CNN on top of the 3-D-CNN further learns more abstract-level spatial representation. Moreover, the use of hybrid CNNs reduces the complexity of the model compared to the use of 3-D-CNN alone. To test the performance of this hybrid approach, very rigorous HSI classification experiments are performed over Indian Pines, University of Pavia, and Salinas Scene remote sensing data sets. The results are compared with the state-of-the-art hand-crafted as well as end-to-end deep learning-based methods. A very satisfactory performance is obtained using the proposed HybridSN for HSI classification. The source code can be found at https://github.com/gokriznastic/HybridSN.
TOWARDS AN INDUCTIVE METHOD TO RESEARCH THE FORMATION OF VALUES REGARDING ACTIVE CITIZENSHIP

Challenging economic and political conditions are appealing people to get involved in civic engagement. Some of these actions are creating new ways of participation and widening active citizenship, which is desirable, but some others are threatening democratic values. Moral education research has the key role of finding out the relationship between unprecedented ways of active citizenship and the formation of democratic moral values. This paper proposes an inductive research process aimed to trace the formation of moral values in active citizenship, having as mainstays the epistemology of the Actor-Network Theory, and the general researching process of the Grounded Theory.
Enhanced phosphatidylserine-selective cancer therapy with irradiation and SapC-DOPS nanovesicles

Normal living cells exhibit phosphatidylserine (PS) primarily within the intracellular leaflet of the plasma membrane. In contrast, viable cancer cells have high levels of PS on the external surface, and exhibit a broad range of surface PS, even within specific types of cancer. Agents that target surface PS have recently been developed to treat tumors and are expected to be more effective with higher surface PS levels. In this context, we examined whether surface PS is increased with irradiation. In vitro irradiation of cancer cell lines selected surviving cells that had higher surface PS in a dose- and time-dependent manner. This was more pronounced if surface PS was initially in the lower range for cancer cells. Radiation also increased the surface PS of tumor cells in subcutaneous xenografts in nude mice. We found an inverse relationship between steady state surface PS level of cancer cell lines and their sensitivity to radiation-induced cell death. In addition, serial irradiation, which selected surviving cells with higher surface PS, also increased resistance to radiation and to some chemotherapeutic drugs, suggesting a PS-dependent mechanism for development of resistance to therapy. On the other hand, fractionated radiation enhanced the effect of a novel anti-cancer, PS-targeting drug, SapC-DOPS, in some cancer cell lines. Our data suggest that we can group cancer cells into cells with low surface PS, which are sensitive to radiation, and high surface PS, which are sensitive to SapC-DOPS. Combination of these interventions may provide a potential new combination therapy.
In Vitro Susceptibility to Metronidazole of Tritrichomonas foetus Bovine Isolates from Argentina

Tritrichomonas foetus is the etiologic agent of the sexually transmitted disease Bovine Trichomonosis (BT). In Argentina, BT is endemic and represents a relevant health problem that causes reproductive inefficiency in cattle and large economic losses. Metronidazole is the drug of choice in the treatment of BT. Treatment has been associated with a temporary resolution of the clinical signs but is not able to control the disease. In recent years, the apparition of in vivo and in vitro aerobic and anaerobic resistance leading to ineffective treatments has been reported. Thus, the aim of the present study was to explore the susceptibility of six different isolates of T. foetus under aerobic (AC) and anaerobic (ANC) conditions. Six isolates of T. foetus were obtained from samples of preputial smegma of bovine origin. Values of minimum lethal concentration and minimum inhibitory concentration were higher than those observed in other works and represent current data in Argentina and provide information to establish new treatment protocols.
Thompson Sampling for the Control of a Queue with Demand Uncertainty

Thompson Sampling for the Control of a Queue with Demand Uncertainty Michael Gimelfarb Master of Applied Science Graduate Department of Mechanical and Industrial Engineering University of Toronto 2017 We study an admission control problem in which the customer arrival rate is unknown and needs to be learned from data using Bayesian inference. Two key defining features of this model are that: (1) when the arrival rate is known, the DP equations can be solved explicitly to obtain the optimal policy over the infinite horizon, and (2) uninformative actions are unavoidable and occur infinitely often. We extend the standard proof techniques for Thompson sampling to admission control, in which uninformative actions occur infinitely often, and show that asymptotically optimal convergence rates of the posterior error and worst-case average regret are achieved. Finally, we show that under simple assumptions, our techniques generalize to a broader class of policies, which we call Generalized Thompson sampling. We show that this class of policies achieves asymptotically optimal convergence rates and can outperform standard Thompson sampling in numerical simulation.
Adaptive Immunity Induces Tolerance to Flagellin by Attenuating TLR5 and NLRC4-Mediated Innate Immune Responses

The host immune system is constantly exposed to diverse microbial ligands, including flagellin (FliC; a ligand for TLR5 and NLRC4) and lipopolysaccharide (LPS; a ligand for TLR4), which could induce immune tolerance to subsequent exposure. Herein, we investigated the extent to which FliC induces self-tolerance in vivo and the role of adaptive immunity in mediating such effect. Mice pre-treated with FliC displayed attenuated serum keratinocyte-derived chemokine (KC), interleukin (IL)-6 and IL-18 responses to secondary challenge of FliC. A negative correlation was observed between high anti-FliC titer and reduced KC, IL-6, and IL-18 responses upon FliC re-challenge in WT mice, but not Rag1KO mice, suggesting that adaptive immunity could tolerize TLR5 and NLRC4. However, administration of LPS during FliC pre-treatment impaired the generation of anti-FliC antibodies and resulted in a partial loss of self-tolerance to FliC re-challenge. These findings may be relevant in the context of bacterial infection, as we observed that anti-FliC response are protective against systemic infection by Salmonella typhimurium. Taken together, our study delineates a distinct co-operative and reciprocal interaction between the innate and adaptive arms of immunity in modulating their responses to a bacterial protein.
libMPNode: An OpenMP Runtime For Parallel Processing Across Incoherent Domains

In this work we describe libMPNode, an OpenMP runtime designed for efficient multithreaded execution across systems composed of multiple non-cache-coherent domains. Rather than requiring extensive compiler-level transformations or building new programming model abstractions, libMPNode builds on recent works that allow developers to use a traditional shared-memory programming model to build applications that are migratable between incoherent domains. libMPNode handles migrating threads between domains, or nodes, and optimizes many OpenMP mechanisms to reduce cross-node communication. While applications may not scale as written, we describe early experiences in simple code refactoring techniques that help scale performance by only changing a handful of lines of code. We describe and evaluate the current implementation, report on experiences using the runtime, and describe future research directions for multi-domain OpenMP.
Using an Ancillary Neural Network to Capture Weekends and Holidays in an Adjoint Neural Network Architecture for Intelligent Building Management

The US EIA estimated in 2017 about 39\% of total U.S. energy consumption was by the residential and commercial sectors. Therefore, Intelligent Building Management (IBM) solutions that minimize consumption while maintaining tenant comfort are an important component in addressing climate change. A forecasting capability for accurate prediction of indoor temperatures in a planning horizon of 24 hours is essential to IBM. It should predict the indoor temperature in both short-term (e.g. 15 minutes) and long-term (e.g. 24 hours) periods accurately including weekends, major holidays, and minor holidays. Other requirements include the ability to predict the maximum and the minimum indoor temperatures precisely and provide the confidence for each prediction. To achieve these requirements, we propose a novel adjoint neural network architecture for time series prediction that uses an ancillary neural network to capture weekend and holiday information. We studied four long short-term memory (LSTM) based time series prediction networks within this architecture. We observed that the ancillary neural network helps to improve the prediction accuracy, the maximum and the minimum temperature prediction and model reliability for all networks tested.
Towards an Adaptive Conversational Robot using Biosignals

Social robots are becoming ubiquitous. In contrast with traditional robots, they are designed to interact with other humans through gestures, voice and even changes in mood. Advances in speech recognition and synthesis have promoted the development of conversational robots capable of interacting through relatively simple speech commands. We describe work aimed at developing conversational robots that adapt their interaction based on cues related to mood and behavior gathered from wearable sensors. In particular, we describe scenarios in the area of assisted living that have been developed using a participatory design process and a cloud-based architecture designed to gather, process and distribute sensor data to adapt the robot. The analysis of data gathered for two of these scenarios is analyzed to illustrate the feasibility of the approach.
Progress in Tourism Management Film tourism e Evolution , progress and prospects

Film tourism has emerged as a major growth sector for research in tourism and it is widely recognised as a driver of tourism development for many destinations. To date, there has been a relatively substantial literature on the subject, little of which has sought to synthesise and engage with the wider social science paradigm of culture, film and media research. The aim of this Progress Review is to critically evaluate film tourism as a subject of cross-disciplinary academic study, highlighting the major research themes, issues and contributing conceptual frameworks, critiquing existing and developing perspectives and addressing critical gaps in knowledge. This review is particularly timely as the literature on film tourism has not been synthesised from a cross-disciplinary perspective. Accordingly, this paper seeks to provide a road map of the exponential growth in social science literature associated with the cognate areas that study, or contribute to, film tourism. 2012 Elsevier Ltd. All rights reserved.
Natural Hosts and Genetic Diversity of the Emerging Tomato Leaf Curl New Delhi Virus in Spain

Knowledge about the host range and genetic structure of emerging plant viruses provides insights into fundamental ecological and evolutionary processes, and from an applied perspective, facilitates the design and implementation of sustainable disease control measures. Tomato leaf curl New Delhi virus (ToLCNDV) is an emerging whitefly transmitted begomovirus that is rapidly spreading and inciting economically important diseases in cucurbit crops of the Mediterranean basin. Genetic characterization of the ToLCNDV Mediterranean populations has shown that they are monophyletic in cucurbit plants. However, the extent to which other alternative (cultivated and wild) hosts may affect ToLCNDV genetic population structure and virus prevalence remains unknown. In this study a total of 683 samples from 13 cultivated species, and 203 samples from 24 wild species from three major cucurbit-producing areas of Spain (Murcia, Alicante and Castilla-La Mancha) from five cropping seasons (2012–2016) were analyzed for ToLCNDV infection. Except for watermelon, ToLCNDV was detected in all cultivated-cucurbit species as well as in tomato. Among weeds, Ecballium elaterium, Datura stramonium, Sonchus oleraceus, and Solanum nigrum were identified as alternative ToLCNDV plant hosts, which could act as new potential sources of virus inoculum. Furthermore, we performed full-genome deep-sequencing of 80 ToLCNDV isolates from different hosts, location and cropping year. Our phylogenetic analysis supports a Mediterranean virus population that is genetically very homogeneous, with no clustering pattern, and clearly different from Asian virus populations. Additionally, D. stramonium displayed higher levels of within-host genetic diversity than cultivated plants, and this variability appeared to increase with time. These results suggest that the potential ToLCNDV adaptive evolution occurring in wild plant hosts could serve as a source of virus genetic variability, thereby affecting the genetic structure and spatial-temporal dynamics of the viral population.
Two‐dimensional mapping of fine structures in the Kuroshio Current using seismic reflection data

Multi‐channel seismic reflection data acquired in the Pacific Ocean off the Muroto peninsula of Shikoku Island, Japan reveal the two‐dimensional distribution of fine structures in the Kuroshio Current. Eighty‐one seismic sections, each extending 80 km perpendicular to the current and separated by 100 m, were acquired from 20 June to 15 August 1999 (57 days). The seismic data clearly show that fine structures extend over 40 km perpendicular to the current in almost all of the profiles. A simulation study using acoustic model from CTD data demonstrates that fine structure of temperature and salinity identified in CTD data acquired from the Kuroshio Current off the Ashizuri peninsula yield a synthetic seismic profile with characteristics similar to the Muroto transect profiles.
Taking-and-merging games as rewrite games

This work is a contribution to the study of rewrite games. Positions are finite words, and the possible moves are defined by a finite number of local rewriting rules. We introduce and investigate taking-and-merging games, that is, where each rule is of the form a^k->epsilon. We give sufficient conditions for a game to be such that the losing positions (resp. the positions with a given Grundy value) form a regular language or a context-free language. We formulate several related open questions in parallel with the famous conjecture of Guy about the periodicity of the Grundy function of octal games. Finally we show that more general rewrite games quickly lead to undecidable problems. Namely, it is undecidable whether there exists a winning position in a given regular language, even if we restrict to games where each move strictly reduces the length of the current position. We formulate several related open questions in parallel with the famous conjecture of Guy about the periodicity of the Grundy function of octal games.
WIDER Face and Pedestrian Challenge 2018: Methods and Results

This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian. The challenge focuses on the problem of precise localization of human faces and bodies, and accurate association of identities. It comprises of three tracks: (i) WIDER Face which aims at soliciting new approaches to advance the state-of-the-art in face detection, (ii) WIDER Pedestrian which aims to find effective and efficient approaches to address the problem of pedestrian detection in unconstrained environments, and (iii) WIDER Person Search which presents an exciting challenge of searching persons across 192 movies. In total, 73 teams made valid submissions to the challenge tracks. We summarize the winning solutions for all three tracks. and present discussions on open problems and potential research directions in these topics.
Low-cost deterministic C++ exceptions for embedded systems

The C++ programming language offers a strong exception mechanism for error handling at the language level, improving code readability, safety, and maintainability. However, current C++ implementations are targeted at general-purpose systems, often sacrificing code size, memory usage, and resource determinism for the sake of performance. This makes C++ exceptions a particularly undesirable choice for embedded applications where code size and resource determinism are often paramount. Consequently, embedded coding guidelines either forbid the use of C++ exceptions, or embedded C++ tool chains omit exception handling altogether. In this paper, we develop a novel implementation of C++ exceptions that eliminates these issues, and enables their use for embedded systems. We combine existing stack unwinding techniques with a new approach to memory management and run-time type information (RTTI). In doing so we create a compliant C++ exception handling implementation, providing bounded runtime and memory usage, while reducing code size requirements by up to 82%, and incurring only a minimal runtime overhead for the common case of no exceptions.