Miracle-dz/UniFAR-FTUs huggingface dataset:
Microscopic theory of organic magnetoresistance based on kinetic equations for quantum spin correlations

The correlation kinetic equation approach is developed that allows describing spin correlations in a material with hopping transport. The quantum nature of spin is taken into account. The approach is applied to the problem of the bipolaron mechanism of organic magnetoresistance (OMAR) in the limit of large Hubbard energy and small applied electric field. The spin relaxation that is important to magnetoresistance is considered to be due to hyperfine interaction with atomic nuclei. It is shown that the lineshape of magnetoresistance depends on short-range transport properties. Different model systems with identical hyperfine interaction but different statistics of electron hops lead to different lineshapes of magnetoresistance including the two empirical laws $H^2/(H^2 + H_0^2)$ and $H^2/(|H| + H_0)^2$ that are commonly used to fit experimental results.
Open Domain Question Answering Using Web Tables

Tables extracted from web documents can be used to directly answer many web search queries. Previous works on question answering (QA) using web tables have focused on factoid queries, i.e., those answerable with a short string like person name or a number. However, many queries answerable using tables are non-factoid in nature. In this paper, we develop an open-domain QA approach using web tables that works for both factoid and non-factoid queries. Our key insight is to combine deep neural network-based semantic similarity between the query and the table with features that quantify the dominance of the table in the document as well as the quality of the information in the table. Our experiments on real-life web search queries show that our approach significantly outperforms state-of-the-art baseline approaches. Our solution is used in production in a major commercial web search engine and serves direct answers for tens of millions of real user queries per month.
Simultaneous Signal-and-Interference Alignment for Two-Cell Over-the-Air Computation

The next-generation wireless -20mm]Our records show Qiao Lan is a Graduate Student Member, IEEE. Please verify. networks are envisioned to support large-scale sensing and distributed machine learning, thereby enabling new intelligent mobile applications. One common network operation will be the aggregation of distributed data (such as sensor observations or AI-model updates) for functional computation (e.g., averaging) so as to support large-scale sensing and distributed machine learning. An efficient solution for data aggregation, called over-the-air computation (AirComp), embeds functional computation into simultaneous access by many edge devices. Such schemes exploit the waveform superposition of a multi-access channel to allow an access point to receive a desired function of simultaneous signals. In this letter, we aim at realizing AirComp in a two-cell multi-antenna system. To this end, a novel scheme of simultaneous signal-and-interference alignment (SIA) is proposed that builds on classic IA to manage interference for multi-cell AirComp. The principle of SIA is to divide the spatial channel space into two subspaces with equal dimensions: one for signal alignment required by AirComp and the other for inter-cell IA. As a result, the number of interference-free spatially multiplexed functional streams received by each AP is maximized (equal to half of the available spatial degrees-of-freedom). Furthermore, the number is independent of the population of devices in each cell. In addition, the extension to SIA for more than two cells is discussed.
A Probabilistic Simulator of Spatial Demand for Product Allocation

Connecting consumers with relevant products is a very important problem in both online and offline commerce. In physical retail, product placement is an effective way to connect consumers with products. However, selecting product locations within a store can be a tedious process. Moreover, learning important spatial patterns in offline retail is challenging due to the scarcity of data and the high cost of exploration and experimentation in the physical world. To address these challenges, we propose a stochastic model of spatial demand in physical retail. We show that the proposed model is more predictive of demand than existing baselines. We also perform a preliminary study into different automation techniques and show that an optimal product allocation policy can be learned through Deep Q-Learning.
Multi-layer optimizations for end-to-end data analytics

We consider the problem of training machine learning models over multi-relational data. The mainstream approach is to first construct the training dataset using a feature extraction query over input database and then use a statistical software package of choice to train the model. In this paper we introduce Iterative Functional Aggregate Queries (IFAQ), a framework that realizes an alternative approach. IFAQ treats the feature extraction query and the learning task as one program given in the IFAQ's domain-specific language, which captures a subset of Python commonly used in Jupyter notebooks for rapid prototyping of machine learning applications. The program is subject to several layers of IFAQ optimizations, such as algebraic transformations, loop transformations, schema specialization, data layout optimizations, and finally compilation into efficient low-level C++ code specialized for the given workload and data. We show that a Scala implementation of IFAQ can outperform mlpack, Scikit, and TensorFlow by several orders of magnitude for linear regression and regression tree models over several relational datasets.
Pruning Convolutional Neural Networks with Self-Supervision

Convolutional neural networks trained without supervision come close to matching performance with supervised pre-training, but sometimes at the cost of an even higher number of parameters. Extracting subnetworks from these large unsupervised convnets with preserved performance is of particular interest to make them less computationally intensive. Typical pruning methods operate during training on a task while trying to maintain the performance of the pruned network on the same task. However, in self-supervised feature learning, the training objective is agnostic on the representation transferability to downstream tasks. Thus, preserving performance for this objective does not ensure that the pruned subnetwork remains effective for solving downstream tasks. In this work, we investigate the use of standard pruning methods, developed primarily for supervised learning, for networks trained without labels (i.e. on self-supervised tasks). We show that pruned masks obtained with or without labels reach comparable performance when retrained on labels, suggesting that pruning operates similarly for self-supervised and supervised learning. Interestingly, we also find that pruning preserves the transfer performance of self-supervised subnetwork representations.
microbatchGAN: Stimulating Diversity with Multi-Adversarial Discrimination

We propose to tackle the mode collapse problem in generative adversarial networks (GANs) by using multiple discriminators and assigning a different portion of each minibatch, called microbatch, to each discriminator. We gradually change each discriminator’s task from distinguishing between real and fake samples to discriminating samples coming from inside or outside its assigned microbatch by using a diversity parameter α. The generator is then forced to promote variety in each minibatch to make the micro-batch discrimination harder to achieve by each discriminator. Thus, all models in our framework benefit from having variety in the generated set to reduce their respective losses. We show evidence that our solution promotes sample diversity since early training stages on multiple datasets.
Gushel-Mukai varieties

Gushel-Mukai varieties are smooth (complex) dimensionally transverse intersections of a cone over the Grassmannian Gr(2,5) with a linear space and a quadratic hypersurface. They occur in each dimension 1 through 6 and they are Fano varieties (their anticanonical bundle is ample) in dimensions 3, 4, 5, and 6. The aim of this survey is to discuss the geometry, moduli, Hodge structures, and categorical aspects of these varieties. It is based on joint work with Alexander Kuznetsov and earlier work of Logachev, Iliev, Manivel, O'Grady, and Perry.
Wiretap channels with causal state information: revisited

The coding problem for wiretap channels with causal channel state information (CSI) available at the encoder (Alice) and/or the decoder (Bob) is studied. We are concerned here particularily with the problem of achievable secret-message secret-key rate pairs under the semantic security criterion. Our main result extends all the previous results on achievable rates as given by Chia and El Gamal [10], Fujita [11], and Han and Sasaki [23]. In order to do this, we first derive a unifying theorem (Theorem 2) with causal CSI at Alice, which follows immediately by leveraging the unifying seminal theorem for wiretap channels with non-causal CSI at Alice as recently established by Bunin et al. [22]. The only thing to do here is just to re-interpret the latter non-causal one in a causal manner. A prominent feature of this approach is that we are able to dispense with the block-Markov encoding scheme as used in the previous works. Also, the exact secret-message key capacity region for wiretap channels with non-causal CSI at both Alice and Bob is given.
Studying the Anisotropy of Compressible Magnetohydrodynamic Turbulence by Synchrotron Polarization Intensity

Based on statistical analysis of synchrotron polarization intensity, we study the anisotropic properties of compressible magnetohydrodynamic (MHD) turbulence. The second-order normalized structure function, quadrupole ratio modulus, and anisotropic coefficient are synergistically used to characterize the anisotropy of the polarization intensity. On the basis of predecomposition data cubes, we first explore the anisotropy of the polarization intensity in different turbulence regimes and find that the most significant anisotropy occurs in the sub-Alfvénic regime. Using postdecomposition data cubes in this regime, we then study the anisotropy of the polarization intensity from Alfvén, slow, and fast modes. The statistics of the polarization intensity from Alfvén and slow modes demonstrate the significant anisotropy, while the statistics of the polarization intensity from fast modes show isotropic structures. This is consistent with earlier results provided in Cho & Lazarian. As a result, both quadrupole ratio modulus and anisotropic coefficient for polarization intensities can quantitatively recover the anisotropy of underlying compressible magnetohydrodynamic turbulence. The synergistic use of the two methods helps enhance the reliability of the magnetic field measurement.
Gamma-ray observations of low-luminosity active galactic nuclei

The majority of the activity around nearby (z ~ 0) supermassive black holes is found in low-luminosity active galactic nuclei (LLAGN), the most of them being classified as low ionization nuclear emission regions. Although these sources are well studied from radio up to X-rays, they are poorly understood in gamma-rays. In this work we take advantage of the all sky-surveying capabilities of the Large Area Telescope on board Fermi Gamma ray Space Telescope to study the whole Palomar sample of LLAGN in gamma-rays. Precisely, the four radio-brightest LLAGN in the sample are identified as significant gamma-ray emitters, all of which are recognized as powerful Fanaroff-Riley I galaxies. These results suggest that the presence of powerful radio jets is of substantial importance for observing a significant gamma-ray counterpart even if these jets are misaligned with respect to the line of sight. We also find that most of the X-ray-brightest LLAGN do not have a significant gamma-ray and strong radio emission, suggesting that the X-rays come mainly from the accretion flow in these cases. A detailed analysis of the spectral energy distributions (SEDs) of NGC 315 and NGC 4261, both detected in gamma-rays, is provided where we make a detailed comparison between the predicted hadronic gamma-ray emission from a radiatively inefficient accretion flow (RIAF) and the gamma-ray emission from a leptonic jet-dominated synchrotron self-Compton (SSC) model. Both SEDs are better described by the SSC model while the RIAF fails to explain the gamma-ray observations.
Text as Data: Real-time Measurement of Economic Welfare

Economists are showing increasing interest in the use of text as an input to economic research. Here, we analyse online text to construct a real time metric of welfare. For purposes of description, we call it the Feel Good Factor (FGF). The particular example used to illustrate the concept is confined to data from the London area, but the methodology is readily generalisable to other geographical areas. The FGF illustrates the use of online data to create a measure of welfare which is not based, as GDP is, on value added in a market-oriented economy. There is already a large literature which measures wellbeing/happiness. But this relies on conventional survey approaches, and hence on the stated preferences of respondents. In unstructured online media text, users reveal their emotions in ways analogous to the principle of revealed preference in consumer demand theory. The analysis of online media offers further advantages over conventional survey-based measures of sentiment or well-being. It can be carried out in real time rather than with the lags which are involved in survey approaches. In addition, it is very much cheaper.
RSL-Net: Localising in Satellite Images From a Radar on the Ground

This letter is about localising a vehicle in an overhead image using FMCW radar mounted on a ground vehicle. FMCW radar offers extraordinary promise and efficacy for vehicle localisation. It is impervious to all weather types and lighting conditions. However the complexity of the interactions between millimetre radar wave and the physical environment makes it a challenging domain. Infrastructure-free large-scale radar-based localisation is in its infancy. Typically here a map is built and suitable techniques, compatible with the nature of sensor, are brought to bear. In this work we eschew the need for a radar-based map; instead we simply use an overhead image – a resource readily available everywhere. This letter introduces a method that not only naturally deals with the complexity of the signal type but does so in the context of cross modal processing.
Nontrivial effective lower bounds for the least common multiple of some quadratic sequences

This paper is devoted to studying the numbers $L_{c,m,n} := \mathrm{lcm}\{m^2+c ,(m+1)^2+c , \dots , n^2+c\}$, where $c,m,n$ are positive integers such that $m \leq n$. Precisely, we prove that $L_{c,m,n}$ is a multiple of the rational number \[\frac{\displaystyle\prod_{k=m}^{n}\left(k^2+c\right)}{c \cdot (n-m)! \displaystyle\prod_{k=1}^{n-m}\left(k^2+4c\right)} ,\] and we derive (as consequences) some nontrivial lower bounds for $L_{c,m,n}$. We prove for example that if $n- \frac{1}{2} n^{2/3} \leq m \leq n$, then we have $L_{c,m,n} \geq \lambda(c) \cdot n e^{3 (n - m)}$, where $\lambda(c) := \frac{e^{- \frac{2 \pi^2}{3} c - \frac{5}{12}}}{(2 \pi)^{3/2} c}$. Further, it must be noted that our approach (focusing on commutative algebra) is new and different from those using previously by Farhi, Oon and Hong.
Paradoxical Results of Long-Term Potentiation explained by Voltage-based Plasticity Rule

Experiments have shown that the same stimulation pattern that causes Long-Term Potentiation in proximal synapses, will induce Long-Term Depression in distal ones. In order to understand these, and other, surprising observations we use a phenomenological model of Hebbian plasticity at the location of the synapse. Our computational model describes the Hebbian condition of joint activity of pre- and post-synaptic neuron in a compact form as the interaction of the glutamate trace left by a presynaptic spike with the time course of the postsynaptic voltage. We test the model using experimentally recorded dendritic voltage traces in hippocampus and neocortex. We find that the time course of the voltage in the neighborhood of a stimulated synapse is a reliable predictor of whether a stimulated synapse undergoes potentiation, depression, or no change. Our model can explain the existence of different -- at first glance seemingly paradoxical -- outcomes of synaptic potentiation and depression experiments depending on the dendritic location of the synapse and the frequency or timing of the stimulation.
The evolution of dermal shield vascularization in Testudinata and Pseudosuchia: phylogenetic constraints versus ecophysiological adaptations

Studies on living turtles have demonstrated that shells are involved in the resistance to hypoxia during apnea via bone acidosis buffering; a process which is complemented with cutaneous respiration, transpharyngeal and cloacal gas exchanges in the soft-shell turtles. Bone acidosis buffering during apnea has also been identified in crocodylian osteoderms, which are also known to employ heat transfer when basking. Although diverse, many of these functions rely on one common trait: the vascularization of the dermal shield. Here, we test whether the above ecophysiological functions played an adaptive role in the evolutionary transitions between land and aquatic environments in both Pseudosuchia and Testudinata. To do so, we measured the bone porosity as a proxy for vascular density in a set of dermal plates before performing phylogenetic comparative analyses. For both lineages, the dermal plate porosity obviously varies depending on the animal lifestyle, but these variations prove to be highly driven by phylogenetic relationships. We argue that the complexity of multi-functional roles of the post-cranial dermal skeleton in both Pseudosuchia and Testudinata probably is the reason for a lack of obvious physiological signal, and we discuss the role of the dermal shield vascularization in the evolution of these groups. This article is part of the theme issue ‘Vertebrate palaeophysiology’.
Importance of adipocyte browning in the evolution of endothermy

Endothermy changes the relationship between organisms and their environment fundamentally, and it is therefore of major ecological and evolutionary significance. Endothermy is characterized by non-shivering thermogenesis, that is metabolic heat production in the absence of muscular activity. In many eutherian mammals, brown adipose tissue (BAT) is an evolutionary innovation that facilitates non-shivering heat production in mitochondria by uncoupling food-derived substrate oxidation from chemical energy (ATP) production. Consequently, energy turnover is accelerated resulting in increased heat release. The defining characteristics of BAT are high contents of mitochondria and vascularization, and the presence of uncoupling protein 1. Recent insights, however, reveal that a range of stimuli such as exercise, diet and the immune system can cause the browning of white adipocytes, thereby increasing energy expenditure and heat production even in the absence of BAT. Here, we review the molecular mechanisms that cause browning of white adipose tissue, and their potential contribution to thermoregulation. The significance for palaeophysiology lies in the presence of adipose tissue and the mechanisms that cause its browning and uncoupling in all amniotes. Hence, adipocytes may have played a role in the evolution of endothermy beyond the more specific evolution of BAT in eutherians. This article is part of the theme issue ‘Vertebrate palaeophysiology’.
Physical Activity, Sedentary Behavior, and Quality of Life among University Students

The aim of our study was to explore the relation between physical activity, sedentary behavior, and the subjective and objective indicators of quality of life as well as life satisfaction among university students, whose education is related to different dimensions on health. Participants (N = 595) were invited to fill in a set of suitable questionnaires. The path analysis and linear regression were used to establish a relationship between the examined constructs. Only some types of physical activity have shown a positive relation with the quality of life; the study also revealed some age and gender regularities. Physical activity in the household was most positively correlated to the quality of life. The amount of leisure and transport physical activity decreased with age, and there were also gender differences regarding the intensity and type of physical activity. Sedentary behavior during the week related positively with the subjective quality of life and its intimacy dimension, but sedentary behavior at the weekends was negatively related to objective and subjective quality of life as well as dimensions including intimacy, safety, and communicative aspect of the quality of life. Neither physical activity nor sedentary behavior demonstrated a significant relation with the level of life satisfaction. The type of physical activity undertaken and its matching to the needs of the young person affected their objective and subjective quality of life. Those findings may have important implications for institutions responsible for promoting active lifestyle.
Postpartum Seizure and Subarachnoid Haemorrhage Secondary to Moyamoya Disease

Postpartum seizures secondary to subarachnoid haemorrhage (SAH) are rare. The incidence of pregnancy-related SAH is increasing and is highest during the delivery and postpartum periods. While there have been cases in the literature of SAH occurring postpartum, very few are associated with Moyamoya disease. We present a rare case of a young woman diagnosed with Moyamoya disease following immediate postpartum seizures secondary to a SAH. She was medically managed and discharged without any neurological deficits. This case highlights how seizures and SAH may develop in the immediate postpartum period in an otherwise healthy young woman.
An Automatic Collision Avoidance Algorithm for Multiple Marine Surface Vehicles

Abstract In recent years, unmanned surface vehicles have been widely used in various applications from military to civil domains. Seaports are crowded and ship accidents have increased. Thus, collision accidents occur frequently mainly due to human errors even though international regulations for preventing collisions at seas (COLREGs) have been established. In this paper, we propose a real-time obstacle avoidance algorithm for multiple autonomous surface vehicles based on constrained convex optimization. The proposed method is simple and fast in its implementation, and the solution converges to the optimal decision. The algorithm is combined with the PD-feedback linearization controller to track the generated path and to reach the target safely. Forces and azimuth angles are efficiently distributed using a control allocation technique. To show the effectiveness of the proposed collision-free path-planning algorithm, numerical simulations are performed.
Robust Extremum Seeking for a Second Order Uncertain Plant Using a Sliding Mode Controller

Abstract This paper suggests a novel continuous-time robust extremum seeking algorithm for an unknown convex function constrained by a dynamical plant with uncertainties. The main idea of the proposed method is to develop a robust closed-loop controller based on sliding modes where the sliding surface takes the trajectory around a zone of the optimal point. We assume that the output of the plant is given by the states and a measure of the function. We show the stability and zone-convergence of the proposed algorithm. In order to validate the proposed method, we present a numerical example.
Facile Synthesis of Spiro[cyclohexane-1,3’-indoline]-2,2’-diones

Abstract Spiro[cyclohexane-1,3’-indoline]-2,2’-diones were easily prepared in good to high yields by the oxidation of N-aryl-N-methyl-2-oxocyclohexane-1-carboxamides in one pot with a short reaction time. The spiroindolinediones could be important for the total synthesis of natural products.