Miracle-dz/UniFAR-FTUs huggingface dataset:
THE ROLE OF THE MOTHER-CHILD RELATIONSHIP IN DEVELOPMENTAL OUTCOMES OF INFANTS AND YOUNG CHILDREN WITH AND WITHOUT PRENATAL ALCOHOL EXPOSURE

Background Prenatal alcohol exposure has been associated with deficits in many developmental areas. Effects on developmental outcomes can be exacerbated by cumulative risk across the preand postnatal environments. Given that the parent-infant relationship provides the primary context for healthy child development, it is possible that maternal caregiving may play a substantial role in mitigating these effects. Objectives To clarify the role of the quality of the mother-child relationship in the relation between cumulative risk and neurodevelopmental outcomes. Methods Participants were 40 infants/children and their mothers with substance-use problems who were taking part in an early mental health intervention program. Cumulative risk, across the preand postnatal period was measured, and quality of the mother-child relationship was rated based on clinical file reviews and observation of mother-child interactions. Outcome measures were infant/child IQ, and neurobehavioral functioning rated across several developmental domains. Results The quality of the mother-child relationship mediated the direct relation between cumulative risk and neurobehavioral functioning, and cumulative risk was related with IQ indirectly through the mother-child relationship. Conclusions These findings indicate an important role for quality of the mother-child relationship in determining outcomes for infants and young children of substance-using women, and emphasize the need to consider both the larger context of risk, as well as the mother-child relationship for best intervention outcomes.
Reinforcement Learning models of neuropeptide- modulated human brain function

The way alterations in the chemistry of the human brain affect social interactions is still not fully understood. Various Reinforcement Learning algorithms have been used to model learning processes in both animals and humans. This thesis focuses on the study of the relation between the activation of the Reward Centers of the human brain and specific parameters of a Reinforcement Learning algorithm. This algorithm is known as Q-learning and is used as a model for the learning process of an individual playing an iterated Prisoner’s Dilemma styled social game for monetary rewards. This relation is tested and compared between subject groups that are administered, by means of intranasal spray, either placebos, Oxytocin or Vasopressin. Subjects are adults of both genders with ages in the 20 to 40 years range and are grouped by gender during experiments.
Late Information Fusion for Multi-modality Plant Species Identification

This article presents the participation of the ReVeS project to the ImageCLEF 2013 Plant Identification challenge. Our primary target being tree leaves, some extra effort had to be done this year to process images containing other plant organs. The proposed method tries to benefit from the presence of multiple sources of information for a same individual through the introduction of a late fusion system based on the decisions of classifiers for the different modalities. It also presents a way to incorporate the geographical information in the determination of the species by estimating their plausibility at the considered location. While maintaining its performance on leaf images (ranking 3rd on natural images and 4th on plain backgrounds) our team performed honorably on the brand new modalities with a 6th position.
An Interface for Visualizing the Evolution of Enterprise Meta-models

The practice of Enterprise Architecture (EA) has proven its utility in the alignment of business and IT. Managing its evolution when new requirements arise is not a trivial task, thus, threatening the success and efficiency of EA development projects. In this paper, the authors used Design-Science Research (DSR) to propose an interface that provides a consistent environment for visualizing the evolution of enterprise meta-models. The interface used as demonstration an EA evolution project from an European financial organization, and was validated using specific interface evaluation techniques.
An extended modeling approach to assess climate change impacts on groundwater recharge and adaptation in arid areas

The effect of future climate scenarios on surface and groundwater resources was simulated using a model- ing approach for an artificial recharge area in arid south- ern Iran. Future climate data for the periods of 2010-2030 and 2030-2050 were acquired from the Canadian Global Coupled Model (CGCM 3.1) for scenarios A1B, A2, and B1. These scenarios were adapted to the studied region us- ing the delta-change method. A conceptual rainfall-runoff model (Qbox) was used to simulate runoff in a flash flood prone catchment. The model was calibrated and validated for the period 2002-2011 using daily discharge data. The pro- jected climate variables were used to simulate future runoff. The rainfall-runoff model was then coupled to a calibrated groundwater flow and recharge model (MODFLOW) to sim- ulate future recharge and groundwater hydraulic heads. As a result of the rainfall-runoff modeling, under the B1 sce- nario the number of floods is projected to slightly increase in the area. This in turn calls for proper management, as this is the only source of fresh water supply in the studied region. The results of the groundwater recharge modeling showed no significant difference between present and future recharge for all scenarios. Owing to that, four abstraction and recharge scenarios were assumed to simulate the groundwa- ter level and recharge amount in the studied aquifer. The re- sults showed that the abstraction scenarios have the most sub- stantial effect on the groundwater level and the continuation of current pumping rate would lead to a groundwater decline by 18 m up to 2050.
Workﬂow Control for Distributed Natural Language Processing Applications

In this work, we present a workﬂow management system that allows the modelling, execution and monitoring of workﬂows oriented to the ﬁeld of Natural Language Processing (NLP), which is a ﬁeld that requires large amounts of processing power. This ﬁeld imposes requirements, which we build our sys-tem to answer. These requirements include (1) using a language that is simple to learn, even for non-programmers, but at the same time possesses the expressiveness to enable the modelling of complex workﬂows, (2) support for grid environments for components that need to process large amounts of data, and the extensibility to allow the addition of modules to work with other environments, (3) error tolerance that allows recovering an error without restarting the workﬂow and (4) the ability to easily make changes to a workﬂow. The workﬂow management system that this work presents is implemented using a client-server architecture, where, after deploying the server, the users access it using a browser interface. The system allows the speciﬁcation of workﬂows through a graphical interface where components can be linked together to create a workﬂow with little need for coding.
Random networking : between order and chaos

With the arrival of the Internet, a good understanding of networks has become important for everyone. Network theory, which originated in the eighteenth century with Euler, and in the nineteenth century withMarkov, has until recently concentrated its attentionmainly on regular types of graphs. In his inaugural lecture, Remco van der Hofstad shows us a shift towards highly irregular graphs having vertices with extremely high degrees. He argues that this irregularity is a main characteristic of real life networks such as the Internet, social networks and networks describing biophysical phenomena. On January 1, 2005, Remco van der Hofstad was appointed full professor in Probability at the University of Eindhoven.
Secure dynamic reconﬁguration of FPGAs

—This article proposes a solution to improve the security of the partial dynamic reconﬁguration of FPGA, without signiﬁcantly affecting the reconﬁguration performance. The existing solutions for secure partial dynamic reconﬁguration on SRAM based FPGAs impact the reconﬁguration process and the available resources due to their complex multi-layered partial bitstream validation process. This adversely affects the performance of applications using reconﬁgurable hardware. The proposed solution uses high performance encryption engines to change the encryption key of the remotely received bitstream by a randomly generated key, unique to each conﬁguration, when storing the bitstream in the external unsecured memory. An additional CBC-MAC authentication mechanism is also considered that combined with the frame-wise error detection mechanism of the conﬁguration port, allows for an improved countermeasure against replay attack and wrongful bitstream usage. The proposed solution introduces a resource overhead of 1.1% in regard to the base reconﬁgurable system and provides the lowest impact on the reconﬁguration process when compared to the related state of the art, achieving a reconﬁguration throughput of 2.5 Gbps.
On the nature of WO stars: a quantitative analysis of the WO3 star DR1 in IC 1613 ?

Context. Oxygen sequence Wolf-Rayet (WO) stars are thought to represent the final evolutionary stage of the most massive stars. The characteristic strong Ovi emission possibly originates from an enhanced oxygen abundance in the stellar wind. Alternatively, the Ovi emission can be caused by the high temperature of these stars, in which case the WO stars are the high-temperature extension of the more common carbon sequence Wolf-Rayet (WC) stars. Aims. By constraining the physical properties and evolutionary status of DR1, a WO star in the low-metallicity Local Group dwarf galaxy IC 1613 and one of only two objects of its class known in a SMC-like metallicity environment, we aim to investigate the nature of WO stars and their evolutionary connection with WC stars. Methods. We use the non-local thermodynamic equilibrium atmosphere code cmfgen to model the observed spectrum of DR1 and to derive its stellar and wind parameters. We compare our values with other studies of WC and WO stars, as well as with the predictions of evolutionary models. We also model the surrounding nebula using the photo-ionization code cloudy. Results. The oxygen and carbon abundances that we obtain are comparable to values found for WC stars. The temperature and luminosity are, however, higher than those of WC stars. DR1 is embedded in the hottest known Hii region in the Local Group. The nebular properties can be consistently reproduced by cloudy models adopting DR1 as central ionizing source. Conclusions. Comparison of the abundances and temperature of DR1 with core helium-burning models show that DR1 is currently well into the second half of helium burning. If the properties of DR1 are representative for the WO class, it would imply that WO stars are the high-temperature and high-luminosity extension of the WC stars, and do not necessarily represent a later evolutionary stage.
A Perspective From In Vivo Data

We review below published studies of endothelium-dependent vasodilation in vivo. Endothelium-dependent vasodilation has been demonstrated in conduit arteries in vivo and in the cerebral, coronary, mesenteric, and femoral vascular beds as well as in the microcirculation of the brain and the microcirculation of cremaster muscle. The available evidence, although not complete, strongly suggests that the endothellum-derived relaxing factor generated by acetylcholine in the cerebral microcirculation is a nitrosothiol. The endothelium-derived relaxing factor generated by bradykinin in this vascular bed is an oxygen radical generated in association with enhanced arachidonate metabolism via cyclooxygenase. In the microcirculation of skeletal muscle, on the other hand, the vasodilation from bradykinin is mediated partly by prostacycline and partly by an endothelium-derived relaxing factor similar to that generated by acetylcholine. Basal secretion of endothelium-derived relaxing factor is controversial in vivo but is usually present in vitro. On the other hand, it appears that endothelium-derived relaxing factor mediates flow-dependent vasodilation in both large vessels and in the microcirculation in vivo. The generation and release of endothelium-derived relaxing factor from endothelium may be abnormal in a variety of conditions including acute and chronic hypertension, atherosclerosis, and ischemia followed by reperfusion. Several mechanisms for these abnormalities have been identified. These include inability to generate endothelium-derived relaxing factor or destruction of endothelium-derived relaxing factor by oxidants after its release in the extracellular space. These abnormalities in endothelium-dependent relaxation may contribute to the vascular abnormalities in these conditions. (Hypertension 1990;16:371-386)
Treatment of Hypertension with Calcium Antagonists

Calcium channel blockers have a selective action on the cardiovascular system. They reduce the energy requirement of the heart, reduce vascular smooth muscle tone, and increase systemic blood flow. Vasodilatation occurs in both the systemic and the pulmonary systems to an extent proportional to the baseline level of vascular resistance, and results in reduction of blood pressure when it is elevated. Thus, these blockers are useful in patients with high blood pressure. Clinical experience of calcium channel blockers in hypertension is so far confined almost exclusively to verapamil and nifedipine. This article reviews the advantages and limitations of these two compounds, their acute hemodynamic effects in hypertensive subjects, and their use in the treatment of hypertensive emergencies, hypertensive encephalopathy, and pheochromocytoma, and as ventricular afterload reducing agents in hypertensive left ventricular failure. Similarities in the effects of nifedipine on systemic and pulmonary vascular tone are presented as evidence that altered intracellular Ca + + concentration is involved in the vasoconstriction seen in both systems in systemic high blood pressure. They also provide support for the hypothesis that inappropriate C a + + handling may be involved in maintaining elevated blood pressure in human hypertension. (Hypertension 5 (supp II): II-85-II-90, 1983)
Same Side Stance Classification Task: Facilitating Argument Stance Classification by Fine-tuning a BERT Model

Research on computational argumentation is currently being intensively investigated. The goal of this community is to find the best pro and con arguments for a user given topic either to form an opinion for oneself, or to persuade others to adopt a certain standpoint. While existing argument mining methods can find appropriate arguments for a topic, a correct classification into pro and con is not yet reliable. The same side stance classification task provides a dataset of argument pairs classified by whether or not both arguments share the same stance and does not need to distinguish between topic-specific pro and con vocabulary but only the argument similarity within a stance needs to be assessed. The results of our contribution to the task are build on a setup based on the BERT architecture. We fine-tuned a pre-trained BERT model for three epochs and used the first 512 tokens of each argument to predict if two arguments share the same stance.
Magnetically Driven Baryon Winds from Binary Neutron Star Merger Remnants and the Blue Kilonova of 2017 August

The observation of a radioactively powered kilonova associated with the first binary neutron star (BNS) merger detected in gravitational waves proved that these events are ideal sites for the production of heavy r-process elements. However, the physical origin of the ejected material responsible for the early (“blue”) and late (“red”) components of this kilonova is still debated. Here, we investigate the possibility that the early/blue kilonova originated from the magnetically driven baryon wind launched after merger by the metastable neutron star remnant. Exploiting a magnetized BNS merger simulation with over 250 ms of post-merger evolution, we can follow for the first time the full mass-ejection process up to its final decline. We find that the baryon wind carries ≃0.010–0.028 M⊙ of unbound material, proving that the high mass estimated for the blue kilonova can be achieved. We also find expansion velocities of up to ∼0.2c, consistent with the lower end of the observational estimates, and we discuss possible effects neglected here that could further increase the final ejecta velocity. Overall, our results show that the magnetically driven baryon wind represents a viable channel to explain the blue kilonova.
On the adaptive spectral approximation of functions using redundant sets and frames

The approximation of smooth functions with a spectral basis typically leads to rapidly decaying coefficients, where the rate of decay depends on the smoothness of the function and vice versa. The optimal number of degrees of freedom in the approximation can be determined with relative ease by truncating the coefficients once a threshold is reached. Recent approximation schemes based on redundant sets and frames extend the applicability of spectral approximations to functions defined on irregular geometries and to certain nonsmooth functions. However, due to their inherent redundancy, the expansion coefficients in frame approximations do not necessarily decay even for very smooth functions. In this paper, we highlight this lack of equivalence between smoothness and coefficient decay, and we explore approaches to determine an optimal number of degrees of freedom for such redundant approximations.
Permutation inference in factorial survival designs with the CASANOVA

We propose inference procedures for general nonparametric factorial survival designs with possibly right-censored data. Similar to additive Aalen models, null hypotheses are formulated in terms of cumulative hazards. Thereby, deviations are measured in terms of quadratic forms in Nelson-Aalen-type integrals. Different to existing approaches this allows to work without restrictive model assumptions as proportional hazards. In particular, crossing survival or hazard curves can be detected without a significant loss of power. For a distribution-free application of the method, a permutation strategy is suggested. The resulting procedures' asymptotic validity as well as their consistency are proven and their small sample performances are analyzed in extensive simulations. Their applicability is finally illustrated by analyzing an oncology data set.
Kernel Operations on the GPU, with Autodiff, without Memory Overflows

The KeOps library provides a fast and memory-efficient GPU support for tensors whose entries are given by a mathematical formula, such as kernel and distance matrices. KeOps alleviates the major bottleneck of tensor-centric libraries for kernel and geometric applications: memory consumption. It also supports automatic differentiation and outperforms standard GPU baselines, including PyTorch CUDA tensors or the Halide and TVM libraries. KeOps combines optimized C++/CUDA schemes with binders for high-level languages: Python (Numpy and PyTorch), Matlab and GNU R. As a result, high-level "quadratic" codes can now scale up to large data sets with millions of samples processed in seconds. KeOps brings graphics-like performances for kernel methods and is freely available on standard repositories (PyPi, CRAN). To showcase its versatility, we provide tutorials in a wide range of settings online at \url{this http URL}.
What's in a name: the etymology of astrobiology

Abstract Astrobiology has been gaining increasing scientific prominence and public attention as the search for life beyond Earth continues to make significant headway on multiple fronts. In view of these recent developments, the fascinating and dynamic etymology of astrobiology is elucidated, and thus shown to encompass a plethora of vivid characters drawn from different continents, religions, ideologies and centuries.
Enabling Human-Robot Collaboration via Holistic Human Perception and Partner-Aware Control

As robotic technology advances, the barriers to the coexistence of humans and robots are slowly coming down. Application domains like elderly care, collaborative manufacturing, collaborative manipulation, etc., are considered the need of the hour, and progress in robotics holds the potential to address many societal challenges. The future socio-technical systems constitute of blended workforce with a symbiotic relationship between human and robot partners working collaboratively. This thesis attempts to address some of the research challenges in enabling human-robot collaboration. In particular, the challenge of a holistic perception of a human partner to continuously communicate his intentions and needs in real-time to a robot partner is crucial for the successful realization of a collaborative task. Towards that end, we present a holistic human perception framework for real-time monitoring of whole-body human motion and dynamics. On the other hand, the challenge of leveraging assistance from a human partner will lead to improved human-robot collaboration. In this direction, we attempt at methodically defining what constitutes assistance from a human partner and propose partner-aware robot control strategies to endow robots with the capacity to meaningfully engage in a collaborative task.
Agile Autotuning of a Transprecision Tensor Accelerator Overlay for TVM Compiler Stack

Specialized accelerators for tensor-operations, such as blocked-matrix operations and multi-dimensional convolutions, have emerged as powerful architecture choices for high-performance Deep-Learning computing. The rapid development of frameworks, models, and precision options challenges the adaptability of such tensor-accelerators since the adaptation to new requirements incurs significant engineering costs. Programmable tensor accelerators offer a promising alternative by allowing reconfiguration of a virtual architecture that overlays on top of the physical FPGA configurable fabric. We propose an overlay (τ-VTA) and an optimization method guided by agile-inspired auto-tuning techniques. We achieve higher performance of up to 2.5x and faster convergence of up to 8.1x.
Software-Level Accuracy Using Stochastic Computing With Charge-Trap-Flash Based Weight Matrix

The in-memory computing paradigm with emerging memory devices has been recently shown to be a promising way to accelerate deep learning. Resistive processing unit (RPU) has been proposed to enable the vector-vector outer product in a crossbar array using a stochastic train of identical pulses to enable one-shot weight update, promising intense speed-up in matrix multiplication operations, which form the bulk of training neural networks. However, the performance of the system suffers if the device does not satisfy the condition of linear conductance change over around 1,000 conductance levels. This is a challenge for nanoscale memories. Recently, Charge Trap Flash (CTF) memory was shown to have a large number of levels before saturation, but variable non-linearity. In this paper, we explore the trade-off between the range of conductance change and linearity. We show, through simulations, that at an optimum choice of the range, our system performs nearly as well as the models trained using exact floating point operations, with less than 1% reduction in the performance. Our system reaches an accuracy of 97.9% on MNIST dataset, 89.1% and 70.5% accuracy on CIFAR-10 and CIFAR-100 datasets (using pre-extracted features). We also show its use in reinforcement learning, where it is used for value function approximation in Q-Learning, and learns to complete an episode the mountain car control problem in around 146 steps. Benchmarked to state-of-the-art, the CTF based RPU shows best in class performance to enable software equivalent performance.