Miracle-dz/UniFAR-FTUs huggingface dataset:
Model adaptation in a discrete fracture network: existence of solutions and numerical strategies

Fractures are normally present in the underground and are, for some physical processes, of paramount importance. Their accurate description is fundamental to obtain reliable numerical outcomes useful, e.g., for energy management. Depending on the physical and geometrical properties of the fractures, fluid flow can behave differently, going from a slow Darcian regime to more complicated Brinkman or even Forchheimer regimes for high velocity. The main problem is to determine where in the fractures one regime is more adequate than others. In order to determine these low-speed and high-speed regions, this work proposes an adaptive strategy which is based on selecting the appropriate constitutive law linking velocity and pressure according to a threshold criterion on the magnitude of the fluid velocity itself. Both theoretical and numerical aspects are considered and investigated, showing the potentiality of the proposed approach. From the analytical viewpoint, we show existence of weak solutions to such model under reasonable hypotheses on the constitutive laws. To this end, we use a variational approach identifying solutions with minimizers of an underlying energy functional. From the numerical viewpoint, we propose a one-dimensional algorithm which tracks the interface between the low- and high-speed regions. By running numerical experiments using this algorithm, we illustrate some interesting behaviors of our adaptive model on a single fracture and small networks of intersecting fractures.
[CII] emission properties of the massive star-forming region RCW 36 in a filamentary molecular cloud

Aims. We investigate the properties of [C II] 158 μm emission of RCW 36 in a dense filamentary cloud. Methods. [C II] observations of RCW 36, covering an area of ~30′ × 30′, were carried out with a Fabry-Pérot spectrometer on board a 100-cm balloon-borne far-infrared (IR) telescope with an angular resolution of 90′′. Using AKARI and Herschel images, we compared the spatial distribution of the [C II] intensity with the emission from the large grains and polycyclic aromatic hydrocarbon (PAH). Results. The [C II] emission is in good spatial agreement with shell-like structures of a bipolar lobe observed in IR images, which extend along the direction perpendicular to the direction of cold dense filament. We found that the [C II]–160 μm relation for RCW 36 shows a higher brightness ratio of [C II]/160 μm than that for RCW 38, while the [C II]–9 μm relation for RCW 36 is in good agreement with that for RCW 38. Conclusions. Via a spectral decomposition analysis on a pixel-by-pixel basis using IR images, the [C II] emission is spatially well correlated with PAH and cold dust emissions. This means that the observed [C II] emission predominantly comes from photo-dissociation regions. Moreover, the L[C II]∕LFIR ratio shows large variation (10−2–10−3), as compared with the L[C II]/LPAH ratio. In view of the observed tight correlation between L[C II]∕LFIR and the optical depth at λ = 160 μm, the large variation in L[C II]∕LFIR can be simply explained by the geometrical effect, that is, LFIR has contributions from the entire dust-cloud column along the line of sight, while L[C II] has contributions from far-UV illuminated cloud surfaces. Based on the picture of the geometry effect, the enhanced brightness ratio of [C II]/160 μm is attributed to the difference in gas structures where massive stars are formed: filamentary (RCW 36) and clumpy (RCW 38) molecular clouds; thus suggesting that RCW 36 is dominated by far-UV illuminated cloud surfaces, as compared with RCW 38.
The interaction of elementary waves for nonisentropic flow in a variable cross-section duct

The interaction of elementary waves for isentropic flow in a variable cross-section duct is obtained (\cite{ShengZhang}). The authors have discussed rarefaction wave or shock wave interacts with stationary wave. In this paper, we extend their results to the nonisentropic flow. It can be proved that if one changes $(u,\rho)$ plane in isentropic flow with $(u,p)$ plane in nonisentropic case, the interaction results of rarefaction wave or shock wave with stationary wave can be moved parallel from the isentropic case when contact discontinuity is involved. Thus we mainly focus on the interactions between contact discontinuity and stationary wave. Some numerical results are given to verify our analysis. The results can apply to the interaction of more complicated wave patterns.
SimHumalator: An Open Source WiFi Based Passive Radar Human Simulator For Activity Recognition

This work presents a simulation framework to generate human micro-Dopplers in WiFi based passive radar scenarios, wherein we simulate IEEE 802.11g complaint WiFi transmissions using MATLAB's WLAN toolbox and human animation models derived from a marker-based motion capture system. We integrate WiFi transmission signals with the human animation data to generate the micro-Doppler features that incorporate the diversity of human motion characteristics, and the sensor parameters. In this paper, we consider five human activities. We uniformly benchmark the classification performance of multiple machine learning and deep learning models against a common dataset. Further, we validate the classification performance using the real radar data captured simultaneously with the motion capture system. We present experimental results using simulations and measurements demonstrating good classification accuracy of $\geq$ 95\% and $\approx$ 90\%, respectively.
Generative Particle Variational Inference via Estimation of Functional Gradients

Recently, particle-based variational inference (ParVI) methods have gained interest because they directly minimize the Kullback-Leibler divergence and do not suffer from approximation errors from the evidence-based lower bound. However, many ParVI approaches do not allow arbitrary sampling from the posterior, and the few that do allow such sampling suffer from suboptimality. This work proposes a new method for learning to approximately sample from the posterior distribution. We construct a neural sampler that is trained with the functional gradient of the KL-divergence between the empirical sampling distribution and the target distribution, assuming the gradient resides within a reproducing kernel Hilbert space. Our generative ParVI (GPVI) approach maintains the asymptotic performance of ParVI methods while offering the flexibility of a generative sampler. Through carefully constructed experiments, we show that GPVI outperforms previous generative ParVI methods such as amortized SVGD, and is competitive with ParVI as well as gold-standard approaches like Hamiltonian Monte Carlo for fitting both exactly known and intractable target distributions.
Facile synthesis of MWCNT–ZnFe2O4 nanocomposites as anode materials for lithium ion batteries

Monodisperse ZnFe2O4 nanoparticles with sizes less than 10 nm have been successfully assembled on multi-walled carbon nanotubes (MWCNTs) by in situ high-temperature decomposition of the precursor iron(III) acetylacetonate, zinc acetate and MWCNTs in polyol solution. A possible formation mechanism was proposed, which suggests that the ZnFe2O4 nanoparticles are formed on the surface of MWCNTs through an aggregation thermochemical reaction process between ZnO and γ-Fe2O3 subparticles. It was found that the coverage density on the MWCNTs could be easily controlled by changing the concentration of the precursor. As anode materials for Li-ion batteries, the MWCNT–ZnFe2O4 nanocomposites showed high rate capability and superior cycling stability with a specific capacity of 1152 mA h g−1, which was much higher than that of ZnFe2O4 nanoparticles. The MWCNTs served as good electron conductors and volume buffers in improving the lithium performance of MWCNT–ZnFe2O4 nanocomposites during the discharge–charge process. Magnetic measurements showed that the MWCNT–ZnFe2O4 nanocomposites are superparamagnetic at room temperature and the magnetization of the samples can be controlled by the reaction conditions. The as-synthesized MWCNT–ZnFe2O4 nanocomposites are water dispersible and can be manipulated by an external magnetic field. Therefore, the nanocomposites have significant potential for application in the fields of energy storage, composites, wastewater treatment and biomaterials.
A Data-driven Approach to Estimate User Satisfaction in Multi-turn Dialogues

Digital assistants are experiencing rapid growth due to their ability to assist users with day-to-day tasks where most dialogues are happening multi-turn. However, evaluating multi-turn dialogues remains challenging, especially at scale. We suggest a context-sensitive method to estimate the turn-level satisfaction for dialogue considering various types of user preferences. The costs of interactions between users and dialogue systems are formulated using a budget consumption concept. We assume users have an initial interaction budget for a dialogue formed based on the task complexity and that each turn has a cost. When the task is completed, or the budget has been exhausted, users quit the dialogue. We demonstrate our method's effectiveness by extensive experimentation with a simulated dialogue platform and real multi-turn dialogues.
Mapping topology to nuclear dilaton-HLS effective field theory for dense baryonic matter

Yong-Liang Ma 2, ∗ and Mannque Rho † School of Fundamental Physics and Mathematical Sciences, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, 310024, China International Center for Theoretical Physics Asia-Pacific (ICTP-AP) (Beijing/Hangzhou), UCAS, Beijing 100190, China Institut de Physique Théorique, Université Paris-Saclay, CNRS, CEA, 91191 Gif-sur-Yvette cédex, France (Dated: March 3, 2021)
Improving EFX Guarantees through Rainbow Cycle Number

We study the problem of fairly allocating a set of indivisible goods among n agents with additive valuations. Envy-freeness up to any good (EFX) is arguably the most compelling fairness notion in this context. However, the existence of EFX allocations has not been settled and is one of the most important problems in fair division [5]. Towards resolving this problem, many impressive results show the existence of its relaxations. In particular, [1] shows the existence of 0.618-EFX allocations, and [4] shows that EFX allocation exists if we do not allocate at most n - 1 goods. The latter result was recently improved for three agents in [2], in which the two unallocated goods are allocated through an involved procedure. Reducing the number of unallocated goods for an arbitrary number of agents is a systematic way to settle the big question.
All at Once Network Quantization via Collaborative Knowledge Transfer

Network quantization has rapidly become one of the most widely used methods to compress and accelerate deep neural networks on edge devices. While existing approaches offer impressive results on common benchmark datasets, they generally repeat the quantization process and retrain the low-precision network from scratch, leading to different networks tailored for different resource constraints. This limits scalable deployment of deep networks in many real-world applications, where in practice dynamic changes in bit-width are often desired. “All at Once” quantization addresses this problem, by ﬂexibly adjusting the bit-width of a single deep network during inference, without requiring re-training or additional memory to store separate models, for instant adaptation in different scenarios. In this paper, we develop a novel collaborative knowledge transfer approach for efﬁciently training the all-at-once quantization network. Speciﬁcally, we propose an adaptive selection strategy to choose a high-precision “teacher” for transferring knowledge to the low-precision “student” while jointly optimizing the model with all bit-widths. Furthermore, to effectively transfer knowledge, we develop a dynamic block swap-ping method by randomly replacing the blocks in the lower-precision student network with the corresponding blocks in the higher-precision teacher network. Extensive experiments on several challenging and diverse datasets for both image and video classiﬁcation well demonstrate the efﬁcacy of our proposed approach over state-of-the-art methods.
Effect of hydrogen back pressure on dehydrogenation behavior of LiBH4-based reactive hydride composites

Hydrogen back pressure remarkably promotes the formation of metal boride during the dehydrogenation of 4LiBH4 + YH3, 6LiBH4 + CeH2 and 6LiBH4 + CaH2 composites, which seems to be a general phenomenon in LiBH4-based reactive hydride composites that enables mutual destabilization between LiBH4 and metal hydride. The formation of metal boride plays a crucial role in the reversible hydrogen storage properties of these composites. The dependence of the dehydrogenation behavior on hydrogen back pressure might be associated with the microstructural evolution of the dehydrogenation products formed by a solid−liquid reaction.
Time-Optimal Navigation in Uncertain Environments with High-Level Specifications

Mixed observable Markov decision processes (MOMDPs) are a modeling framework for autonomous systems described by both fully and partially observable states. In this work, we study the problem of synthesizing a control policy for MOMDPs that minimizes the expected time to complete the control task while satisfying syntactically co-safe Linear Temporal Logic (scLTL) specifications. First, we present an exact dynamic programming update to compute the value function. Leveraging this result, we propose a point-based approximation, which allows us to compute a lower bound of the closed-loop probability of satisfying the specifications. The effectiveness of the proposed approach and comparisons with standard strategies are shown on high-fidelity navigation tasks with partially observable static obstacles.
SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification

Environmental Sound Classification (ESC) is a challenging field of research in non-speech audio processing. Most of current research in ESC focuses on designing deep models with special architectures tailored for specific audio datasets, which usually cannot exploit the intrinsic patterns in the data. However recent studies have surprisingly shown that transfer learning from models trained on ImageNet is a very effective technique in ESC. Herein, we propose SoundCLR, a supervised contrastive learning method for effective environment sound classification with state-of-the-art performance, which works by learning representations that disentangle the samples of each class from those of other classes. Our deep network models are trained by combining a contrastive loss that contributes to a better probability output by the classification layer with a cross-entropy loss on the output of the classifier layer to map the samples to their respective 1-hot encoded labels. Due to the comparatively small sizes of the available environmental sound datasets, we propose and exploit a transfer learning and strong data augmentation pipeline and apply the augmentations on both the sound signals and their log-mel spectrograms before inputting them to the model. Our experiments show that our masking based augmentation technique on the log-mel spectrograms can significantly improve the recognition performance. Our extensive benchmark experiments show that our hybrid deep network models trained with combined contrastive and cross-entropy loss achieved the state-of-the-art performance on three benchmark datasets ESC-10, ESC-50, and US8K with validation accuracies of 99.75\%, 93.4\%, and 86.49\% respectively. The ensemble version of our models also outperforms other top ensemble methods. The code is available at https://github.com/alireza-nasiri/SoundCLR.
Device-independent quantum authorization based on the Clauser-Horne-Shimony-Holt game

In the spirit of device-independent cryptography, we present a two-party quantum authorization primitive with non-locality as its fueling resource. Therein, users are attributed authorization levels granting them access to a private database accordingly. The authorization levels are encoded in the non-local resources distributed to the users, and subsequently confirmed by their ability to win CHSH games using such resources. We formalize the protocol, prove its security, and frame it in the device-independent setting employing the notion of CHSH self-testing via simulation. Finally, we provide a proof-of-concept implementation using the Qiskit open-source framework.
Hessian Eigenspectra of More Realistic Nonlinear Models

Given an optimization problem, the Hessian matrix and its eigenspectrum can be used in many ways, ranging from designing more efficient second-order algorithms to performing model analysis and regression diagnostics. When nonlinear models and non-convex problems are considered, strong simplifying assumptions are often made to make Hessian spectral analysis more tractable. This leads to the question of how relevant the conclusions of such analyses are for more realistic nonlinear models. In this paper, we exploit deterministic equivalent techniques from random matrix theory to make a \emph{precise} characterization of the Hessian eigenspectra for a broad family of nonlinear models, including models that generalize the classical generalized linear models, without relying on strong simplifying assumptions used previously. We show that, depending on the data properties, the nonlinear response model, and the loss function, the Hessian can have \emph{qualitatively} different spectral behaviors: of bounded or unbounded support, with single- or multi-bulk, and with isolated eigenvalues on the left- or right-hand side of the bulk. By focusing on such a simple but nontrivial nonlinear model, our analysis takes a step forward to unveil the theoretical origin of many visually striking features observed in more complex machine learning models.
How Developers Engineer Test Cases: An Observational Study

One of the main challenges that developers face when testing their systems lies in engineering test cases that are good enough to reveal bugs. And while our body of knowledge on software testing and automated test case generation is already quite significant, in practice, developers are still the ones responsible for engineering test cases manually. Therefore, understanding the developers’ thought- and decision-making processes while engineering test cases is a fundamental step in making developers better at testing software. In this paper, we observe 13 developers thinking-aloud while testing different real-world open-source methods, and use these observations to explain how developers engineer test cases. We then challenge and augment our main findings by surveying 72 software developers on their testing practices. We discuss our results from three different angles. First, we propose a general framework that explains how developers reason about testing. Second, we propose and describe in detail the three different overarching strategies that developers apply when testing. Third, we compare and relate our observations with the existing body of knowledge and propose future studies that would advance our knowledge on the topic.
Predictive Maintenance Tool for Non-Intrusive Inspection Systems

Cross-border security is of topmost priority for societies. Economies lose billions each year due to counterfeiters and other threats. Security checkpoints equipped with X-ray Security Systems (NIIS-Non-Intrusive Inspection Systems) like airports, ports, border control and customs authorities tackle the myriad of threats by using NIIS to inspect bags, air, land, sea and rail cargo, and vehicles. The reliance on the X-ray scanning systems necessitates their continuous 24/7 functioning being provided for. Hence the need for their working condition being closely monitored and preemptive actions being taken to reduce the overall X-ray systems downtime. In this paper, we present a predictive maintenance decision support system, abbreviated as PMT4NIIS (Predictive Maintenance Tool for Non-Intrusive Inspection Systems), which is a kind of augmented analytics platforms that provides real-time AI-generated warnings for upcoming risk of system malfunctioning leading to possible downtime. The industrial platform is the basis of a 24/7 Service Desk and Monitoring center for the working condition of various X-ray Security Systems.
On ground states of the Bogoliubov energy functional: A direct proof

The Bogoliubov energy functional proposed recently by Napiórkowski, Reuvers and Solovej is revisited. We offer a direct proof of the existence of minimizers at zero temperature, which covers a significantly larger class of interaction potentials. The ideas used in this proof also imply that in any ground state, more than half of the particles are inside the Bose-Einstein condensate.
Cyclic Voltammetrically-Prepared MnO2 Coated on an ITO Glass Substrate

For the first time, nanostructured manganese dioxide was successfully electrodeposited onto an ITO (indium tin oxide) glass substrate by cyclic voltammetry (CV) method from an aqueous solution of 0.1 M Na 2 SO 4 containing 5 x 10 -3 M MnSO 4 . The obtained manganese dioxide-modified ITO glass substrates were characterized by energy dispersive spectrometry (EDS), Fourier transform infrared spectrometry (FTIR) and scanning electron microscopy (SEM), respectively. All results not only proved the existence of MnO 2 on an ITO glass substrate but also demonstrated that the morphology of the obtained MnO 2 was greatly affected by the electrodeposition conditions. Also, this MnO 2 -modified ITO electrode was systematically investigated by cyclic voltammetry (CV), chronopotentiometry and electrochemical impedance spectroscopy (EIS) in an aqueous electrolyte ofO. 1 M Na 2 SO 4 . The results obtained from electrochemical measurement indicated that this developed MnO 2 -modified ITO electrode has a satisfied specific capacitance value of 264 F·g -1 and exhibits excellent electrochemical stability and reversibility.
Decomposing lexical and compositional syntax and semantics with deep language models

The activations of language transformers like GPT2 have been shown to linearly map onto brain activity during speech comprehension. However, the nature of these activations remains largely unknown and presumably conﬂate distinct linguistic classes. Here, we propose a taxonomy to factor-ize the high-dimensional activations of language models into four combinatorial classes: lexical, compositional, syntactic, and semantic representations. We then introduce a statistical method to de-compose, through the lens of GPT2’s activations, the brain activity of 345 subjects recorded with functional magnetic resonance imaging (fMRI) during the listening of 4.6 hours of narrated text. The results highlight two ﬁndings. First, compositional representations recruit a more widespread cortical network than lexical ones, and encompass the bilateral temporal, parietal and prefrontal cortices. Second, contrary to previous claims, syntax and semantics are not associated with separated modules, but, instead, appear to share a common and distributed neural substrate. Overall, this study introduces a general framework to isolate the distributed representations of linguistic constructs generated in naturalistic settings.