Miracle-dz/UniFAR-FTUs huggingface dataset:
Sexual behaviour is impaired by the abused inhalant toluene in adolescent male rats

Inhalant misuse is a worldwide problem, especially among adolescents. Toluene is the most widely misused inhalant. One hallmark of adolescence is the emergence of sexual behaviour, which can be affected by drug use. The aim of this study was to analyse the effects of toluene inhalation on different aspects of male rats’ sexual behaviour using a binge pattern of exposure in adolescent rats. Male Wistar rats were individually exposed to air or 6,000 ppm toluene for 30 min (acute exposure; n = 8 each) or twice a day for 12 days (repeated exposure; n = 9 each) in static exposure chambers. Independent groups of sexually experienced, adolescent rats (postnatal day 63, PN63) were tested after acute toluene exposure for copulation, sexual incentive motivation or noncontact erections. Sexually naïve, adolescent rats (PN45–59) were repeatedly exposed to toluene and tested for sexual behaviour after completing the 1st, 3rd, 5th, 7th, 9th, 11th and 13th exposure sessions. Acute toluene exposure impaired copulatory performance, diminished sexual incentive motivation and delayed noncontact erection occurrence in sexually experienced rats. Repeated toluene exposure during adolescence completely inhibited the onset of copulatory behaviours in sexually naïve rats, at the time at which they should have appeared. However, once the inhalant exposure ended, copulatory responses appeared gradually, and animals attained a typical, stable copulatory pattern. In conclusion, acute toluene exposure impairs sexual behaviour in young, sexually experienced animals, while repeated toluene exposure during adolescence prevents the onset of copulatory behaviour, although this effect is transitory.
Heterosis Is a Systemic Property Emerging From Non-linear Genotype-Phenotype Relationships: Evidence From in Vitro Genetics and Computer Simulations

Heterosis, the superiority of hybrids over their parents for quantitative traits, represents a crucial issue in plant and animal breeding. Heterosis has given rise to countless genetic, genomic and molecular studies, but has rarely been investigated from the point of view of systems biology. We hypothesized that heterosis is an emergent property of living systems resulting from frequent concave relationships between genotypic variables and phenotypes, or between different phenotypic levels. We chose the enzyme-flux relationship as a model of the concave genotype-phenotype (GP) relationship, and showed that heterosis can be easily created in the laboratory. First, we reconstituted in vitro the upper part of glycolysis. We simulated genetic variability of enzyme activity by varying enzyme concentrations in test tubes. Mixing the content of “parental” tubes resulted in “hybrids”, whose fluxes were compared to the parental fluxes. Frequent heterotic fluxes were observed, under conditions that were determined analytically and confirmed by computer simulation. Second, to test this model in a more realistic situation, we modeled the glycolysis/fermentation network in yeast by considering one input flux, glucose, and two output fluxes, glycerol and acetaldehyde. We simulated genetic variability by randomly drawing parental enzyme concentrations under various conditions, and computed the parental and hybrid fluxes using a system of differential equations. Again we found that a majority of hybrids exhibited positive heterosis for metabolic fluxes. Cases of negative heterosis were due to local convexity between certain enzyme concentrations and fluxes. In both approaches, heterosis was maximized when the parents were phenotypically close and when the distributions of parental enzyme concentrations were contrasted and constrained. These conclusions are not restricted to metabolic systems: they only depend on the concavity of the GP relationship, which is commonly observed at various levels of the phenotypic hierarchy, and could account for the pervasiveness of heterosis.
Learn to Combine Modalities in Multimodal Deep Learning

Combining complementary information from multiple modalities is intuitively appealing for improving the performance of learning-based approaches. However, it is challenging to fully leverage different modalities due to practical challenges such as varying levels of noise and conflicts between modalities. Existing methods do not adopt a joint approach to capturing synergies between the modalities while simultaneously filtering noise and resolving conflicts on a per sample basis. In this work we propose a novel deep neural network based technique that multiplicatively combines information from different source modalities. Thus the model training process automatically focuses on information from more reliable modalities while reducing emphasis on the less reliable modalities. Furthermore, we propose an extension that multiplicatively combines not only the single-source modalities, but a set of mixtured source modalities to better capture cross-modal signal correlations. We demonstrate the effectiveness of our proposed technique by presenting empirical results on three multimodal classification tasks from different domains. The results show consistent accuracy improvements on all three tasks.
TBT Signal Model for Improved Accuracy of High-level Dynamic Power Estimation Procedure

When estimating the dynamic power consumption of DSP datapaths, it is crucial to accurately calculate switching activity produced inside the design. For accurate switching activity calculation the existence of an appropriate data signal model is essential. This paper presents a triple-bit type (TBT) signal model which is used to represent bit-level switching activity at the output of multipliers. The model depends on wordlevel signal statistics and the number of multiplied input signals. For the sake of comparison with the standard dual-bit type (DBT) signal model, both models (TBT and DBT) are applied to the high-level power estimation of three reference designs implemented in FPGA. Calculated with respect to the measured power, the relative errors of here presented TBT model are four to five times smaller than the errors of the DBT model.
Task-specific Language Modeling for Selecting Peer-written Explanations

Students who are learning to program, often write “buggy” code, especially when they are solving problems on paper. Such bugs can be used as a pedagogical device to engage students in reading and debugging tasks. One can take this a step further and require students to explain in writing how the bugs affect the code. Such written explanations can indicate students’ current level of computational thinking, and concurrently be used in intelligent systems that leverage “learner-sourcing”, the process of generating course material for other learners. In this paper, we discuss how to combine learning analytics techniques and artiﬁcial intelligence (AI) algorithms to help an intelligent system distinguish between strong and weak textual explanations.
The interplay among stress, frustration tolerance, mindfulness, and social support in Internet gaming disorder symptoms among Chinese working adults

Internet gaming disorder (IGD) is a growing mental health threat across age groups, but existing literature regarding IGD mainly focuses on student populations. Empirical investigation of the risk and protective factors in adult populations is warranted. This study aimed to fill the research gap by examining whether stress and 3 positive psychology factors (ie, frustration tolerance, mindfulness, and social support) are associated with IGD symptoms in working adults. It was also the first attempt to test the buffering effects of these positive psychology factors on the relationship between stress and IGD vulnerability.
Autogynephilia: A Paraphilic Model of Gender Identity Disorder

SUMMARY Autogynephilia is defined as a male's propensity to be sexually aroused by the thought or image of himself as female. Autogynephilia explains the desire for sex reassignment of some male-to-female (MTF) transsexuals. It can be conceptualized as both a paraphilia and a sexual orientation. The concept of autogynephilia provides an alternative to the traditional model of transsexualism that emphasizes gender identity. Autogynephilia helps explain mid-life MTF gender transition, progression from transvestism to transsexualism, the prevalence of other paraphilias among MTF transsexuals, and late development of sexual interest in male partners. Hormone therapy and sex reassignment surgery can be effective treatments in autogynephilic transsexualism. The concept of autogynephilia can help clinicians better understand MTF transsexual clients who recognize a strong sexual component to their gender dysphoria.
Systemic Chemotherapy for Urothelial Cancer – How to Select Systemic Therapy in Bladder Cancer

U rothelial cancer (UC) has long been recognised as a chemosensitive disease, and systemic chemotherapy plays a crucial role in the management of localised and advanced disease. Unfortunately, there has been a paucity of progress during the last 15 years in this area. Neoadjuvant use of cisplatin-based combinations has shown survival benefit and together with radical cystectomy should constitute the cornerstone of early disease management. Most importantly, Galsky criteria concerning fitness for cisplatin provide a useful tool for clinicians, in order to select patients with substantial benefit from cisplatin-based chemotherapy. Adjuvant chemotherapy may be useful in selected cases, but its wide implementation has to be proved. Recently, the advent of modern immunotherapy seems to offer new effective choices for patients with advanced UC.
Condom use behaviour among people living with HIV: a seven-country community-based participatory research in the Asia-Pacific region

Objectives We examined the prevalence of inconsistent condom use and its correlates among people living with HIV (PLHIV) in the Asia-Pacific region. Methods Between 1 October 2012 and 31 May 2013, a total of 7843 PLHIV aged 18–50 years were recruited using targeted and venue-based sampling in Bangladesh, Indonesia, Lao People’s Democratic Republic (PDR), Nepal, Pakistan, Philippines and Vietnam. Logistic regression was used to explore the association between condom use behaviour and demographics, social support, stigma and discrimination and various health-related variables. Results Overall, 43% of 3827 PLHIV practised inconsistent condom use at sexual intercourse with their regular partner. An even higher proportion, 46% of 2044 PLHIV admitted that they practised unprotected sex with a casual partner. Participants from Lao PDR reported the lowest prevalence of inconsistent condom use for both regular and casual partners, while participants from the Philippines had the highest risk behaviour. Inconsistent condom use was significantly associated with belonging to a key population (drug user, sex worker or refugee subpopulation), not knowing that condoms are still needed if both partners are HIV positive, having a regular partner whose HIV status was either positive or unknown, having experienced physical assault and not receiving antiretroviral treatment. Conclusions This large seven-country study highlights a high prevalence of inconsistent condom use among PLHIV in the Asia-Pacific region. In addition to knowledge-imparting interventions, the adoption and expansion of the ‘Test and Treat’ strategy could help to maximise the prevention benefits of antiretroviral treatment.
Visually Grounded, Situated Learning in Neural Models

The theory of situated cognition postulates that language is inseparable from its physical context--words, phrases, and sentences must be learned in the context of the objects or concepts to which they refer. Yet, statistical language models are trained on words alone. This makes it impossible for language models to connect to the real world--the world described in the sentences presented to the model. In this paper, we examine the generalization ability of neural language models trained with a visual context. A multimodal connectionist language architecture based on the Differential State Framework is proposed, which outperforms its equivalent trained on language alone, even when no visual context is available at test time. Superior performance for language models trained with a visual context is robust across different languages and models.
Pantothenic Acid, Vitamin C, and Biotin Play Important Roles in the Growth of Lactobacillus helveticus

Lactobacillus helveticus is an important lactic acid bacterium. The strains used in this study have proven probiotic function, and the potential to produce functional dairy products and bioactive peptides. To explore the effects of vitamins on the growth of L. helveticus, a chemically defined medium was designed and nine vitamins were tested. Pantothenic acid (Vb5), vitamin C (Vc), and biotin were necessary for the growth of L. helveticus CICC 22171. These three vitamins had an important effect on the glucose metabolism and energy metabolism of strain CICC 22171. Through transcriptomic analysis, we found that three vitamins were related to the synthesis of fatty acids and participate in the energy supply of the cells. Additionally, Vb5 was involved in the metabolism of bacterial proteins and lipids and was related to the activity of various enzymes. The results indicated that Vc was involved in protein metabolism, and biotin affected the intracellular transport mechanism of bacteria. The ability of vitamins to promote the growth of the strain was verified in skim milk medium. The results indicated that Vc, biotin, and Vb5 could promote the proliferation of L. helveticus but had no significant effect on Lactobacillus bulgaricus.
Graph Sparsification, Spectral Sketches, and Faster Resistance Computation, via Short Cycle Decompositions

We develop a framework for graph sparsification based on a new tool, short cycle decomposition for graphs – a decomposition of a graph into a collection of short cycles, plus a small number of extra edges. A simple observation gives that every graph G on n vertices with m edges can be decomposed in O(mn) time into cycles of length at most 2 log n, and at most 2n extra edges. We give an m1+o(1) time algorithm for constructing a short cycle decomposition of the graph, with cycles of length n^o(1), and n^1+o(1) extra edges. Both the existential and algorithmic variants of this decomposition enable us to make progress on several open problems in randomized graph algorithms. 1. We present an algorithm that runs in time m^1+o(1)ε^-1.5 and returns (1 ± ε)-approximations to effective resistances of all edges, improving over the previous best of Õ(min{mε^-2, n^2 ε^-1}) This gives an algorithm to approximate the determinant of a graph Laplacian up to a factor of (1 ± ε) in roughly m + n^15/8 ε^-7/4. 2. We show existence and efficient algorithms for constructing graphical spectral sketches – a distribution over sparse graphs H with about nε^-1 edges such that for a fixed vector x, we have x^T L_H x = (1 ± eps) x^T L_G x and x^T L+_H x = (1 ± ε) x^T L+_G x with high probability, where L is the graph Laplacian and L+ is its pseudoinverse. This implies resistance-sparsifiers with about nε edges that preserve the effective resistances between every pair of vertices up to (1 + eps). 3. By combining short cycle decomposition with importance sampling, we show the existence of nearly-linear sized degree-preserving spectral sparsifiers, as well as significantly sparser approximations of directed graphs. The latter is critical to recent breakthroughs on faster algorithms for directed random walks and linear systems in directed Laplacian. The running time and output qualities of our spectral sketch and degree-preserving (directed) sparsification algorithms are limited by the efficiency of our routines for producing short cycle decompositions. Improved algorithms for short cycle decompositions will lead to improvements for each of these algorithms.
Estimating Summary Quality with Pairwise Preferences

Automatic evaluation systems in the field of automatic summarization have been relying on the availability of gold standard summaries for over ten years. Gold standard summaries are expensive to obtain and often require the availability of domain experts to achieve high quality. In this paper, we propose an alternative evaluation approach based on pairwise preferences of sentences. In comparison to gold standard summaries, they are simpler and cheaper to obtain. In our experiments, we show that humans are able to provide useful feedback in the form of pairwise preferences. The new framework performs better than the three most popular versions of ROUGE with less expensive human input. We also show that our framework can reuse already available evaluation data and achieve even better results.
Risk Averse Members Coordination with Extended Buy-Back Contract

This paper considers how to coordinate a supply chain (SC) consisted of one supplier and one retailer who possess different risk aversion preference with a contract. Based on the classical buy-back contract, this paper presents an extended buy-back contract. In addition to the member’s objective of maximizing his expected profit, downside risk constraint is used to represent the SC member’s risk aversion preference. Under different risk aversion preference combination, the SC perfect solution existence conditions are identified and the specific contract is provided accordingly. This research finds out that, with a low risk aversion supplier and a high risk aversion retailer, the supplier as the SC coordinator can give the retailer incentive to increase the order quantity so as to reach SC perfect coordination. Finally a numerical analysis verifies the effectiveness of the extended buy-back contract.
Engineered PWM Drives for Achieving Rapid Step and Settle Times for MEMS Actuation

Microelectromechanical systems (MEMS) provide engineers with a rich palette of technical solutions to a wide range of actuation and sensing challenges. MEMS devices are low cost, easily integrated with sense and drive electronics, are robust, and can be designed to respond to electrical, mechanical, or chemical stimuli. Because they are mechanical, MEMS devices suffer from being relatively slow in comparison with purely electronic devices. However, it has been shown that by using feedforward drives developed using controls theory approaches, it is possible to significantly improve the step and settle time of MEMS actuators. This paper uses this technique to demonstrate the use of pulse width modulation to linearly drive MEMS. Furthermore, it demonstrates an overdrive method capable of improving the step and settle time of a commercial MEMS device by a factor of 1500. The approach is general and can be used for a wide range of devices and actuation methods, such as electrostatic, electromagnetic, and thermal actuation. This provides engineers a simple method to design high ${Q}$ MEMS devices with sub-millisecond response times, opening the phase space for more micromechanical solutions. [2017-0261]
#TeamINF at SemEval-2018 Task 2: Emoji Prediction in Tweets

In this paper, we describe a methodology to predict emoji in tweets. Our approach is based on the classic bag-of-words model in conjunction with word embeddings. The used classification algorithm was Logistic Regression. This architecture was used and evaluated in the context of the SemEval 2018 challenge (task 2, subtask 1).
Novel interactions between non‐native mammals and fungi facilitate establishment of invasive pines

The role of novel ecological interactions between mammals, fungi and plants in invaded ecosystems remains unresolved, but may play a key role in the widespread successful invasion of pines and their ectomycorrhizal fungal associates, even where mammal faunas originate from different continents to trees and fungi as in New Zealand. We examine the role of novel mammal associations in dispersal of ectomycorrhizal fungal inoculum of North American pines (Pinus contorta, Pseudotsuga menziesii), and native beech trees (Lophozonia menziesii) using faecal analyses, video monitoring and a bioassay experiment. Both European red deer (Cervus elaphus) and Australian brushtail possum (Trichosurus vulpecula) pellets contained spores and DNA from a range of native and non‐native ectomycorrhizal fungi. Faecal pellets from both animals resulted in ectomycorrhizal infection of pine seedlings with fungal genera Rhizopogon and Suillus, but not with native fungi or the invasive fungus Amanita muscaria, despite video and DNA evidence of consumption of these fungi. Native L. menziesii seedlings never developed any ectomycorrhizal infection from faecal pellet inoculation. Synthesis. Our results show that introduced mammals from Australia and Europe facilitate the co‐invasion of invasive North American trees and Northern Hemisphere fungi in New Zealand, while we find no evidence that introduced mammals benefit native trees or fungi. This novel tripartite ‘invasional meltdown’, comprising taxa from three kingdoms and three continents, highlights unforeseen consequences of global biotic homogenization.
Advances in Web of Things for IoT Interoperability

It is widely acknowledged that the current Internet of Things (IoT) ecosystem is highly fragmented. The Web of Things (WoT) aims to counter the fragmentation using the principles of Web, standardized metadata and APIs. This paper describes the recent advances in W3C WoT including its architecture and building blocks. We also highlight an experiment using WoT Thing Description that help achieve interoperability between two IoT systems.
Enhancing discovery in spatial data infrastructures using a search engine

A spatial data infrastructure (SDI) is a framework of geospatial data, metadata, users and tools intended to provide an efficient and flexible way to use spatial information. One of the key software components of an SDI is the catalogue service which is needed to discover, query and manage the metadata. Catalogue services in an SDI are typically based on the Open Geospatial Consortium (OGC) Catalogue Service for the Web (CSW) standard which defines common interfaces for accessing the metadata information. A search engine is a software system capable of supporting fast and reliable search, which may use ‘any means necessary’ to get users to the resources they need quickly and efficiently. These techniques may include full text search, natural language processing, weighted results, fuzzy tolerance results, faceting, hit highlighting, recommendations and many others. In this paper we present an example of a search engine being added to an SDI to improve search against large collections of geospatial datasets. The Centre for Geographic Analysis (CGA) at Harvard University re-engineered the search component of its public domain SDI (Harvard WorldMap) which is based on the GeoNode platform. A search engine was added to the SDI stack to enhance the CSW catalogue discovery abilities. It is now possible to discover spatial datasets from metadata by using the standard search operations of the catalogue and to take advantage of the new abilities of the search engine, to return relevant and reliable content to SDI users.