Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Geometric Knowledge Distillation with Graphs

In most cases deep learning architectures are trained disregarding the amount of operations and energy consumption. However, some applications, like embedded systems, can be resource-constrained during inference. A popular approach to reduce the size of a deep learning architecture consists in distilling knowledge from a bigger network (teacher) to a smaller one (student). Directly training the student to mimic the teacher representation can be effective, but it requires that both share the same latent space dimensions. In this work, we focus instead on relative knowledge distillation (RKD), which considers the geometry of the respective latent spaces, allowing for dimension-agnostic transfer of knowledge. Specifically we introduce a graph-based RKD method, in which graphs are used to capture the geometry of latent spaces. Using classical computer vision benchmarks, we demonstrate the ability of the proposed method to efficiently distillate knowledge from the teacher to the student, leading to better accuracy for the same budget as compared to existing RKD alternatives.
Graph Convolutional Networks Meet with High Dimensionality Reduction

Recently, Graph Convolutional Networks (GCNs) and their variants have been receiving many research interests for learning graph-related tasks. While the GCNs have been successfully applied to this problem, some caveats inherited from classical deep learning still remain as open research topics in the context of the node classification problem. One such inherited caveat is that GCNs only consider the nodes that are a few propagations away from the labeled nodes to classify them. However, taking only a few propagation steps away nodes into account defeats the purpose of using the graph topological information in the GCNs. To remedy this problem, the-state-of-the-art methods leverage the network diffusion approaches, namely personalized page rank and its variants, to fully account for the graph topology, {\em after} they use the Neural Networks in the GCNs. However, these approaches overlook the fact that the network diffusion methods favour high degree nodes in the graph, resulting in the propagation of labels to unlabeled centralized, hub, nodes. To address this biasing hub nodes problem, in this paper, we propose to utilize a dimensionality reduction technique conjugate with personalized page rank so that we can both take advantage from graph topology and resolve the hub node favouring problem for GCNs. Here, our approach opens a new holistic road for message passing phase of GCNs by suggesting the usage of other proximity matrices instead of well-known Laplacian. Testing on two real-world networks that are commonly used in benchmarking GCNs' performance for the node classification context, we systematically evaluate the performance of the proposed methodology and show that our approach outperforms existing methods for wide ranges of parameter values with very limited deep learning training {\em epochs}.
Modal stability analysis of viscoelastic channel and pipe flows using a well-conditioned spectral method

Modal stability analysis provides information about the long-time growth or decay of small-amplitude perturbations around a steady-state solution of a dynamical system. In fluid flows, exponentially growing perturbations can initiate departure from laminar flow and trigger transition to turbulence. Although flow of a Newtonian fluid through a pipe is linearly stable for very large values of the Reynolds number ($Re \sim 10^7$), a transition to turbulence often occurs for $Re$ as low as $1500$. When a dilute polymer solution is used in the place of a Newtonian fluid, the transitional value of the Reynolds number decreases even further. Using the spectral collocation method and Oldroyd-B constitutive equation, Garg et al. (Phys. Rev. Lett. 121:024502, 2018) claimed that such a transition in viscoelastic fluids is related to linear instability. Since differential matrices in the collocation method become ill-conditioned when a large number of basis functions is used, we revisit this problem using the well-conditioned spectral integration method. We show modal stability of viscoelastic pipe flow for a broad range of fluid elasticities and polymer concentrations, including cases considered by Garg et al. Similarly, we find that plane Poiseuille flow is linearly stable for cases where Garg et al. report instability. In both channel and pipe flows, we establish the existence of spurious modes that diverge slowly with finer discretization and demonstrate that these can be mistaken for grid-independent modes if the discretization is not fine enough.
Ecological preference of the diving bell spider Argyroneta aquatica in a resurgence of the Po plain (Northern Italy) (Araneae: Cybaeidae)

The diving bell spider Argyroneta aquatica is the only known spider to conduct a wholly aquatic life. For this reason, it has been the object of an array of studies concerning different aspects of its peculiar biology such as reproductive behavior and sexual dimorphism, physiology, genetic and silk. On the other hand, besides some empirical observations, the autoecology of this spider is widely understudied. We conducted an ecological study in a resurgence located in the Po Plain (Northern Italy, Province of Vercelli) hosting a relatively rich population of Argyroneta aquatica, aiming at identifying the ecological factors driving its presence at the micro-habitat level. By means of a specific sampling methodology, we acquired distributional data of the spiders in the study area and monitored physical-chemical and habitat structure parameters at each plot. We analyzed the data through Bernoulli Generalized Linear Models (GLM). Results pointed out a significant positive effect of the presence of aquatic vegetation in the plot. In addition, the presence of A. aquatica was significantly associated with areas of the resurgence characterized at the same time by high prey availability and low density of predators. Considering the ecological importance and rarity of this species, we update and revise the data on the distribution of A. aquatica in Italy.
Distributed Generalized Nash Equilibrium Seeking for Non-Monotone Energy Sharing Games

With the proliferation of distributed generators and energy storage systems, traditional passive consumers in power systems have been gradually evolving into the so-called "prosumers", i.e., proactive consumers, which can both produce and consume power. To encourage energy exchange among prosumers, energy sharing is increasingly adopted, which is usually formulated as a generalized Nash game (GNG). In this paper, a distributed approach is proposed to seek the Generalized Nash equilibrium (GNE) of the energy sharing game. To this end, we first reveal by proof that the GNG is generally not monotone. In such a situation, the pseudo-gradient of the disutilities is not monotone, which makes existing methods inapplicable. To overcome this obstacle, we convert the GNG into an equivalent optimization problem. A Krasnosel'ski{ǐ}-Mann iteration type algorithm is thereby devised to solve the problem and consequently find the GNE in a distributed manner. We further prove the convergence of the proposed distributed algorithm based on the nonexpansive operator theory. The performance of the algorithm is validated by experiments with three prosumers, and the scalability is tested by simulations using 123 prosumers.
Data Generation for Neural Programming by Example

Programming by example is the problem of synthesizing a program from a small set of input / output pairs. Recent works applying machine learning methods to this task show promise, but are typically reliant on generating synthetic examples for training. A particular challenge lies in generating meaningful sets of inputs and outputs, which well-characterize a given program and accurately demonstrate its behavior. Where examples used for testing are generated by the same method as training data then the performance of a model may be partly reliant on this similarity. In this paper we introduce a novel approach using an SMT solver to synthesize inputs which cover a diverse set of behaviors for a given program. We carry out a case study comparing this method to existing synthetic data generation procedures in the literature, and find that data generated using our approach improves both the discriminatory power of example sets and the ability of trained machine learning models to generalize to unfamiliar data.
Community Structure and Spatial Patterns in Hard Coral Biodiversity in Selected Islands in the Gulf of Mannar, India

The major reefs of the Gulf of Mannar Biosphere Reserve extend from the Tuticorin group of islands (Long.78 0 9’E, Lat.8 0 48’N) to the Shingle Island (Long. 79 0 14’E, Lat.9 0 14’N) in Mandapam, India. These reefs are under increasing threats from anthropogenic interventions. In view of this, a comparative study was conducted to deduce the spatial patterns in the hard coral biodiversity and community structure of five islands of the Mandapam group. The islands studied are Shingle, Krusadai, Pullivasal, Manauli and Hare. Univariate community parameters, Shannon–Wiener diversity index (H’e), Simpson diversity index, and Pielou’s evenness index were calculated for each reef in each sampling station. The highest and lowest percentage of live coral cover was recorded in the reefs of the Shingle (60.8%) and Pullivasal Islands (13.9%) respectively. Shannon index of diversity recorded maximum value in the reefs of Manauli Island (3.01) and the minimum in Pullivasal Island (1.8). Mortality indices were also derived for each island reef and the condition of the reefs was assigned as either good/sick. Relative abundance values were derived for each species and they were assigned Dominant/Abundant/ Common/Unc ommon/Rare species status. Conservation classes (CC’s) of 1,2,3 or 4 were assigned to reef sites dominated by massive and submassive corals (CC1), foliose or branching non Acropora corals (CC2), Acropora corals (CC3), or approximately equal mixes of these three end – members(CC4). Similarities between reefs were studied by Bray–Curtis similarity coefficients and maximum similarity was found between the Manauli and Hare Island reefs (56.47). The similarity matrix was subjected to ordination analysis. There was a distinct separation of reefs in the Pullivasal Island from other reefs in the MDS plot. SIMPER analysis enabled the authors to find out the species important in the community structure of each reef. From the present study it was concluded that certain coral reefs in the Gulf of Mannar ecosystem is on the threshold of degradation.
Online Learning and Optimization Under a New Linear-Threshold Model with Negative Influence

We propose a new class of Linear Threshold Model-based information-diffusion model that incorporates the formation and spread of negative attitude. We call such models negativity-aware. We show that in these models, the influence function is a monotone submodular function. Thus we can use the greedy algorithm to construct seed sets with constant approximation guarantees, when the objective is to select a seed set of fixed size $K$ to maximize total influence. Our models are flexible enough to account for both the features of local users and the features of the information being propagated in the diffusion. We analyze an online-learning setting for a multi-round influence-maximization problem, where an agent is actively learning the diffusion parameters over time while trying to maximize total cumulative positive influence. We assume that in each diffusion step, the agent can only observe whether a node becomes positively or negatively influenced, or remains inactive. In particular, he does not observe the particular edge that brought about the activation of a node, if any, as in the case of most models that assume Independent Cascade (IC)-based diffusions. This model of feedback is called node-level feedback, as opposed to the more common \emph{edge-level feedback} model in which he is able to observe, for each node, the edge through which that node is influenced. Under mild assumptions, we develop online learning algorithms that achieve cumulative expected regrets of order $O(T^{-c})$ for any $c<1$ where $T$ is the total number of rounds. These are the first regret guarantees for node-level feedback models of influence maximization of any kind. Furthermore, with mild assumptions, this result also improves the average regret of $O(\sqrt{\ln T / T})$ for the edge-level feedback models, thus providing a new performance benchmark.
Deep Reinforcement Learning for Distributed Uncoordinated Cognitive Radios Resource Allocation

This paper presents a novel deep reinforcement learning-based resource allocation technique for the multi-agent environment presented by a cognitive radio network that coexists through underlay dynamic spectrum access (DSA) with a primary network. The resource allocation technique presented in this work is distributed, not requiring coordination with other agents. The presented algorithm is the first deep reinforcement learning technique for which convergence to equilibrium policies can be shown in the non-stationary multi-agent environment that results from the uncoordinated dynamic interaction between radios through the shared wireless environment. Moreover, simulation results show that in a finite learning time the presented technique is able to find policies that yield performance within 3 % of an exhaustive search solution, finding the optimal policy in nearly 70 % of cases. Moreover, it is shown that standard single-agent deep reinforcement learning may not achieve convergence when used in a non-coordinated, coupled multi-radio scenario.
Spectral optical monitoring of 3C 390.3 in 1995-2007 II. Variability of the spectral line parameters

Context. We present a study of the variability of the broad emission-line parameters of 3C390.3, an active galaxy with the doublepeaked emission-line profiles. We give a detailed analysis of the variation in the broad Hα and Hβ emission-line profiles, the ratios, and the Balmer decrement of different line segments. Aims. We explore the disk structure with an investigation of the variability of the broad-line profiles. This is assumed to emit the broad double-peaked Hβ and Hα emission lines in the spectrum of 3C390.3. Methods. We divided the observed spectra into two periods (before and after the outburst in 2002) and separately analyzed the variation in these two periods. First we analyzed the spectral emission-line profiles of the Hα and Hβ lines and measured the peak positions. Then, we divided the lines into several segments and measured the line-segment fluxes. The Balmer decrement variation for the entire Hα and Hβ fluxes and for the line segments was investigated and discussed. Additionally, we modeled the variations in the line parameters with an accretion disk model and compared our modeled line parameter variations with observed ones. Results. We compared the variability in the observed line parameters with the disk model predictions and found that the variation in line profiles and in the line segments corresponds to the emission of a disk-like broad-line region (BLR). But there is probably another additional emission component that contributes to the Hα and Hβ line center. We found that the variation in the line profiles is caused by the variation in the parameters of the disk-like BLR, first of all in the inner (outer) radius, which can well explain the line parameters variations in Period I. The Balmer decrement across the line profile has a bell-like shape and is affected not only by physical processes in the disk, but also by different emitting disk dimension of the Hα and Hβ line. Conclusions. The geometry of the BLR of 3C390.3 seems to be very complex, and inflows/outflows may be present, but evidently the broad-line region with its disk-like geometry is the dominant emitter.
Generalised multilevel Picard approximations

It is one of the most challenging problems in applied mathematics to approximatively solve high-dimensional partial differential equations (PDEs). In particular, most of the numerical approximation schemes studied in the scientific literature suffer under the curse of dimensionality in the sense that the number of computational operations needed to compute an approximation with an error of size at most $ \varepsilon > 0 $ grows at least exponentially in the PDE dimension $ d \in \mathbb{N} $ or in the reciprocal of $ \varepsilon $. Recently, so-called full-history recursive multilevel Picard (MLP) approximation methods have been introduced to tackle the problem of approximately solving high-dimensional PDEs. MLP approximation methods currently are, to the best of our knowledge, the only methods for parabolic semi-linear PDEs with general time horizons and general initial conditions for which there is a rigorous proof that they are indeed able to beat the curse of dimensionality. The main purpose of this work is to investigate MLP approximation methods in more depth, to reveal more clearly how these methods can overcome the curse of dimensionality, and to propose a generalised class of MLP approximation schemes, which covers previously analysed MLP approximation schemes as special cases. In particular, we develop an abstract framework in which this class of generalised MLP approximations can be formulated and analysed and, thereafter, apply this abstract framework to derive a computational complexity result for suitable MLP approximations for semi-linear heat equations. These resulting MLP approximations for semi-linear heat equations essentially are generalisations of previously introduced MLP approximations for semi-linear heat equations.
Study of Some Aspects of Astronuclear System

In this work we have studied the bulk properties of compact stellar object which is largely motivated by the recent work of Potekhin group who studied the structure and evolution of compact star on the basis of unified EoS of Brussels-Montreal group. Here we solved ToV equation of state numerically using three sets of parameter models to compute pressure, baryon density and bulk modulus etc over a wide range of matter density. The computed values of the observables like the stellar interior pressure, bulk modulus and baryon density have been presented as a function of mass density in the tabular form. Graphical analysis of computed observables and their comparison with reference data wherever available have been presented as representative cases.
Explicit Pairwise Word Interaction Modeling Improves Pretrained Transformers for English Semantic Similarity Tasks

In English semantic similarity tasks, classic word embedding-based approaches explicitly model pairwise "interactions" between the word representations of a sentence pair. Transformer-based pretrained language models disregard this notion, instead modeling pairwise word interactions globally and implicitly through their self-attention mechanism. In this paper, we hypothesize that introducing an explicit, constrained pairwise word interaction mechanism to pretrained language models improves their effectiveness on semantic similarity tasks. We validate our hypothesis using BERT on four tasks in semantic textual similarity and answer sentence selection. We demonstrate consistent improvements in quality by adding an explicit pairwise word interaction module to BERT.
Impacts of willow and miscanthus bioenergy buffers on biogeochemical N removal processes along the soil–groundwater continuum

In this article, the belowground and aboveground biomass production in bioenergy buffers and biogeochemical N removal processes along the soil–groundwater continuum was assessed. In a sandy loam soil with shallow groundwater, bioenergy buffers of miscanthus and willow (5 and 10 m wide) were planted along a ditch of an agricultural field (AF) located in the Po valley (Italy). Mineral N forms and dissolved organic C (DOC) were monitored monthly over an 18‐month period in groundwater before and after the bioenergy buffers. Soil samples were measured for inorganic N, DOC, microbial biomass C (MBC) and N (MBN), and potential nitrate reductase activity (NRA). The results indicated that bioenergy buffers are able to efficiently remove from groundwater the incoming NO3‐N (62% – 5 m and 80% – 10 m). NO3‐N removal rate was higher when nitrate input from AF increased due to N fertilization. Willow performed better than miscanthus in terms of biomass production (17 Mg DM ha−1 yr−1), fine root biomass (5.3 Mg ha−1) and N removal via harvesting (73 kg N ha−1). The negative nonlinear relationship found between NO3‐N and DOC along the soil–groundwater continuum from AF to bioenergy buffers indicates that DOC:NO3‐N ratio is an important controlling factor for promoting denitrification in bioenergy buffers. Bioenergy buffers promoted soil microbial functioning as they stimulated plant–microbial linkages by increasing the easily available C sources for microorganisms (as DOC). First, willow and miscanthus promoted high rates of biological removal of nitrate (NRA) along the soil profile. Second, rhizosphere processes activated the soil microbial community leading to significant increases in MBC and microbial N immobilization. Herbaceous and woody bioenergy crops have been confirmed as providing good environmental performances when cultivated as bioenergy buffers by mitigating the disservices of agricultural activities such as groundwater N pollution.
Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks

This paper explores domain adaptation for enabling question answering (QA) systems to answer questions posed against documents in new specialized domains. Current QA systems using deep neural network (DNN) technology have proven effective for answering general purpose factoid-style questions. However, current general purpose DNN models tend to be ineffective for use in new specialized domains. This paper explores the effectiveness of transfer learning techniques for this problem. In experiments on question answering in the automobile manual domain we demonstrate that standard DNN transfer learning techniques work surprisingly well in adapting DNN models to a new domain using limited amounts of annotated training data in the new domain.
Identification and quantification of major derivatives of ellagic acid and 1 antioxidant properties of thinning and ripe Spanish pomegranates 2 3

21 Major derivatives of ellagic acid and antioxidant properties of 9 Spanish 22 pomegranate cultivars were studied at two development stages: thinning and 23 ripening. A total of 35 major derivatives of ellagic acid were identified by LC-PDA24 QTOF/MS and quantified by UPLC-PDA methods; however, only 7 of them were 25 found simultaneously in thinning and ripe fruits. The total content of derivatives of 26 ellagic acid was higher in thinning fruits (3521 to 18236 mg 100 g dry matter, dm) 27 than in ripe fruits (608 to 2905 mg 100 g dm). The antioxidant properties were 28 evaluated using four methods: ABTS, DPPH, FRAP, and ORAC. Experimental values 29 for these four methods in thinning fruits ranged from 2837 to 4453, 2127 to 2920, 30 3131 to 4905, and 664 to 925 mmol trolox kg, respectively; ripe fruits had lower 31 values of the antioxidant activities than thinning fruits, and values ranged from 32 1567 to 2905, 928 to 1627, 582 to 1058, and 338 to 582 mmol trolox kg, 33 respectively. In general, sour-sweet cultivars (PTO8 cultivar) had the highest value 34 of derivatives of ellagic acid and antioxidant properties in pomegranates fruits. 35 Experimental results clearly proved the potential of thinning pomegranate fruits for 36 its use as supplement in food, pharmaceutical and cosmetics industries. 37 38