Miracle-dz/UniFAR-FTUs huggingface dataset:
DFT quantization of the Gibbs Free Energy of a quantum body.

In this article it is introduced a theoretical model made in order to perform calculations of the quantum heat of a body that could be acquired or delivered during a thermal transformation of its quantum states. Here the model is mainly targeted to the electronic structure of matter[1] at the nano and micro scale where DFT models have been frequently developed of the total energy of quantum systems. Defining an Entropy functional $S[\rho]$ makes us able optimizing a free energy $G[\rho]$ of the quantum system at finite temperatures. Due to the generality of the model, the latter can be also applied to several first principles computational codes where ab initio modelling of quantum matter is asked.
NEAT: Non-linearity Aware Training for Accurate and Energy-Efficient Implementation of Neural Networks on 1T-1R Memristive Crossbars

Memristive crossbars suffer from non-idealities (such as, sneak paths) that degrade computational accuracy of the Deep Neural Networks (DNNs) mapped onto them. A 1T-1R synapse, adding a transistor (1T) in series with the memristive synapse (1R), has been proposed to mitigate such non-idealities. We observe that the non-linear characteristics of the transistor affect the overall conductance of the 1T-1R cell which in turn affects the Matrix-Vector-Multiplication (MVM) operation in crossbars. This 1T-1R non-ideality arising from the input voltage-dependent non-linearity is not only difficult to model or formulate, but also causes a drastic performance degradation of DNNs when mapped onto crossbars. In this paper, we analyse the non-linearity of the 1T-1R crossbar and propose a novel Non-linearity Aware Training (NEAT) method to address the non-idealities. Specifically, we first identify the range of network weights, which can be mapped into the 1T-1R cell within the linear operating region of the transistor. Thereafter, we regularize the weights of the DNNs to exist within the linear operating range by using iterative training algorithm. Our iterative training significantly recovers the classification accuracy drop caused by the non-linearity. Moreover, we find that each layer has a different weight distribution and in turn requires different gate voltage of transistor to guarantee linear operation. Based on this observation, we achieve energy efficiency while preserving classification accuracy by applying heterogeneous gate voltage control to the 1T-1R cells across different layers. Finally, we conduct various experiments on CIFAR10 and CIFAR100 benchmark datasets to demonstrate the effectiveness of our non-linearity aware training. Overall, NEAT yields ~20% energy gain with less than 1% accuracy loss (with homogeneous gate control) when mapping ResNet18 networks on 1T-1R crossbars.
Depression Status Estimation by Deep Learning based Hybrid Multi-Modal Fusion Model

Preliminary detection of mild depression could immensely help in effective treatment of the common mental health disorder. Due to the lack of proper awareness and the ample mix of stigmas and misconceptions present within the society, mental health status estimation has become a truly difficult task. Due to the immense variations in character level traits from person to person, traditional deep learning methods fail to generalize in a real world setting. In our study we aim to create a human allied AI workflow which could efficiently adapt to specific users and effectively perform in real world scenarios. We propose a Hybrid deep learning approach that combines the essence of one shot learning, classical supervised deep learning methods and human allied interactions for adaptation. In order to capture maximum information and make efficient diagnosis video, audio, and text modalities are utilized. Our Hybrid Fusion model achieved a high accuracy of 96.3% on the Dataset; and attained an AUC of 0.9682 which proves its robustness in discriminating classes in complex real-world scenarios making sure that no cases of mild depression are missed during diagnosis. The proposed method is deployed in a cloud-based smartphone application for robust testing. With user-specific adaptations and state of the art methodologies, we present a state-of-the-art model with user friendly experience.
Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restoration

The dual-pixel (DP) hardware works by splitting each pixel in half and creating an image pair in a single snapshot. Several works estimate depth/inverse depth by treating the DP pair as a stereo pair. However, dual-pixel disparity only occurs in image regions with the defocus blur. The heavy defocus blur in DP pairs affects the performance of matching-based depth estimation approaches. Instead of removing the blur effect blindly, we study the formation of the DP pair which links the blur and the depth information. In this paper, we propose a mathematical DP model which can benefit depth estimation by the blur. These explorations motivate us to propose an end-to-end DDDNet (DP-based Depth and Deblur Network) to jointly estimate the depth and restore the image. Moreover, we define a re-blur loss, which reflects the relationship of the DP image formation process with depth information, to regularise our depth estimate in training. To meet the requirement of a large amount of data for learning, we propose the first DP image simulator which allows us to create datasets with DP pairs from any existing RGBD dataset. As a side contribution, we collect a real dataset for further research. Extensive experimental evaluation on both synthetic and real datasets shows that our approach achieves competitive performance compared to state-of-the-art approaches.
Bifurcation Analysis of the Eigenstructure of the Discrete Single-curl Operator in Three-dimensional Maxwell's Equations with Pasteur Media

This paper focuses on studying the bifurcation analysis of the eigenstructure of the $\gamma$-parameterized generalized eigenvalue problem ($\gamma$-GEP) arising in three-dimensional (3D) source-free Maxwell's equations with Pasteur media, where $\gamma$ is the magnetoelectric chirality parameter. For the weakly coupled case, namely, $\gamma \gamma_{*}$, the $\gamma$-GEP is no longer positive definite, introducing a totally different and complicated structure. For the critical strongly coupled case, numerical computations for electromagnetic fields have been presented by Huang et. \ al, 2018. In this paper, we build several theoretical results on the eigenstructure behavior of the $\gamma$-GEPs. We prove that the $\gamma$-GEP is regular for any $\gamma > 0$, and the $\gamma$-GEP has $2 \times 2$ Jordan blocks of infinite eigenvalues at the critical value $\gamma_{*}$. Then, we show that the $2 \times 2$ Jordan block will split into a complex conjugate eigenvalue pair that rapidly goes down and up and then collides at some real point near the origin. Next, it will bifurcate into two real eigenvalues, with one moving toward the left and the other to the right along the real axis as $\gamma$ increases. A newly formed state whose energy is smaller than the ground state can be created as $\gamma$ is larger than the critical value. This stunning feature of the physical phenomenon would be very helpful in practical applications. Therefore, the purpose of this paper is to clarify the corresponding theoretical eigenstructure of 3D Maxwell's equations with Pasteur media.
A tale of two mergers: constraints on kilonova detection in two short GRBs at z$\sim$0.5

We present a detailed multi-wavelength analysis of two short Gamma-Ray Bursts (sGRBs) detected by the Neil Gehrels Swift Observatory: GRB 160624A at $z=0.483$ and GRB 200522A at $z=0.554$. These sGRBs demonstrate very different properties in their observed emission and environment. GRB 160624A is associated to a late-type galaxy with an old stellar population ($\approx$3 Gyr) and moderate on-going star formation ($\approx$1 $M_{\odot}$ yr$^{-1}$). Hubble and Gemini limits on optical/nIR emission from GRB 160624A are among the most stringent for sGRBs, leading to tight constraints on the allowed kilonova properties. In particular, we rule out any kilonova brighter than AT2017gfo, disfavoring large masses of wind ejecta ($\lesssim$0.03 $M_\odot$). In contrast, observations of GRB 200522A uncovered a luminous ($L_\textrm{F125W}\approx 10^{42}$ erg s$^{-1}$ at 2.3~d) and red ($r-H\approx 1.3$ mag) counterpart. The red color can be explained either by bright kilonova emission powered by the radioactive decay of a large amount of wind ejecta (0.03 $M_\odot$ $\lesssim$ $M$ $\lesssim$ 0.1 $M_\odot$) or moderate extinction, $E(B-V)\approx0.1-0.2$ mag, along the line of sight. The location of this sGRB in the inner regions of a young ($\approx$0.1 Gyr) star-forming ($\approx$2-4 $M_{\odot}$ yr$^{-1}$) galaxy and the limited sampling of its counterpart do not allow us to rule out dust effects as contributing, at least in part, to the red color.
LifeStream: A High-performance Stream Processing Engine for Waveform Data

Hospitals around the world collect massive amount of physiological data from their patients every day. Recently, there has been increasing research interest to subject this data into statistical analysis for gaining more insights and providing improved medical diagnoses. Enabling such advancements in healthcare require efficient data processing systems. In this paper, we show that currently available data processing solutions either fail to meet the performance requirements or lack simple and flexible programming interfaces. To address this problem, we propose LifeStream, a high performance stream processing engine for physiological data. LifeStream hits the sweet spot between ease of programming by providing a rich temporal query language support and performance by employing optimizations that exploit the constant frequency nature of physiological data. LifeStream demonstrates end-to-end performance up to $7.5\times$ higher than state-of-the-art streaming engines and $3.2\times$ than hand-optimized numerical libraries.
LocaRDS: A Localization Reference Data Set

The use of wireless signals for the purposes of localization enables a host of applications relating to the determination and verification of the positions of network participants ranging from radar to satellite navigation. Consequently, this has been a longstanding interest of theoretical and practical research in mobile networks and many solutions have been proposed in the scientific literature. However, it is hard to assess the performance of these in the real world and, more importantly, to compare their advantages and disadvantages in a controlled scientific manner. With this work, we attempt to improve the current state of art methodology in localization research and to place it on a solid scientific grounding for future investigations. Concretely, we developed LocaRDS, an open reference data set of real-world crowdsourced flight data featuring more than 222 million measurements from over 50 million transmissions recorded by 323 sensors. We demonstrate how we can verify the quality of LocaRDS measurements so that it can be used to test, analyze and directly compare different localization methods. Finally, we provide an example implementation for the aircraft localization problem and a discussion of possible metrics for use with LocaRDS.
Iterative VAE as a predictive brain model for out-of-distribution generalization

Our ability to generalize beyond training data to novel, out-of-distribution, image degradations is a hallmark of primate vision. The predictive brain, exemplified by predictive coding networks (PCNs), has become a prominent neuroscience theory of neural computation. Motivated by the recent successes of variational autoencoders (VAEs) in machine learning, we rigorously derive a correspondence between PCNs and VAEs. This motivates us to consider iterative extensions of VAEs (iVAEs) as plausible variational extensions of the PCNs. We further demonstrate that iVAEs generalize to distributional shifts significantly better than both PCNs and VAEs. In addition, we propose a novel measure of recognizability for individual samples which can be tested against human psychophysical data. Overall, we hope this work will spur interest in iVAEs as a promising new direction for modeling in neuroscience.
Dynamic Humanoid Locomotion over Uneven Terrain With Streamlined Perception-Control Pipeline

Although bipedal locomotion provides the ability to traverse unstructured environments, it requires careful planning and control to safely walk across without falling. This poses an integrated challenge for the robot to perceive, plan, and control its movements, especially with dynamic motions where the robot may have to adapt its swing-leg trajectory onthe-fly in order to safely place its foot on the uneven terrain. In this paper we present an efficient geometric footstep planner and the corresponding walking controller that enables a humanoid robot to dynamically walk across uneven terrain at speeds up to 0.3 m/s. As dynamic locomotion, we refer first to the continuous walking motion without stopping, and second to the on-the-fly replanning of the landing footstep position in middle of the swing phase during the robot gait cycle. This is mainly achieved through the streamlined integration between an efficient sampling-based planner and robust walking controller. The footstep planner is able to generate feasible footsteps within 5 milliseconds, and the controller is able to generate a new corresponding swing leg trajectory as well as the wholebody motion to dynamically balance the robot to the newly updated footsteps. The proposed perception-control pipeline is evaluated and demonstrated with real experiments using a fullscale humanoid to traverse across uneven terrains featured by static stepping stones, dynamically movable stepping stone, or narrow path.
No Crash, No Exploit: Automated Verification of Embedded Kernels

The kernel is the most safety- and security-critical component of many computer systems, as the most severe bugs lead to complete system crash or exploit. It is thus desirable to guarantee that a kernel is free from these bugs using formal methods, but the high cost and expertise required to do so are deterrent to wide applicability. We propose a method that can verify both absence of runtime errors (i.e. crashes) and absence of privilege escalation (i.e. exploits) in embedded kernels from their binary executables. The method can verify the kernel runtime independently from the application, at the expense of only a few lines of simple annotations. When given a specific application, the method can verify simple kernels without any human intervention. We demonstrate our method on two different use cases: we use our tool to help the development of a new embedded realtime kernel, and we verify an existing industrial real-time kernel executable with no modification. Results show that the method is fast, simple to use, and can prevent real errors and security vulnerabilities.
Adaptive Neural Architectures for Recommender Systems

Deep learning has proved an effective means to capture the non-linear associations of user preferences. However, the main drawback of existing deep learning architectures is that they follow a fixed recommendation strategy, ignoring users' real time-feedback. Recent advances of deep reinforcement strategies showed that recommendation policies can be continuously updated while users interact with the system. In doing so, we can learn the optimal policy that fits to users' preferences over the recommendation sessions. The main drawback of deep reinforcement strategies is that are based on predefined and fixed neural architectures. To shed light on how to handle this issue, in this study we first present deep reinforcement learning strategies for recommendation and discuss the main limitations due to the fixed neural architectures. Then, we detail how recent advances on progressive neural architectures are used for consecutive tasks in other research domains. Finally, we present the key challenges to fill the gap between deep reinforcement learning and adaptive neural architectures. We provide guidelines for searching for the best neural architecture based on each user feedback via reinforcement learning, while considering the prediction performance on real-time recommendations and the model complexity.
Spatial Distribution of O vi Covering Fractions in the Simulated Circumgalactic Medium

We use adaptive mesh refinement cosmological simulations to study the spatial distribution and covering fraction of absorption in the circumgalactic medium (CGM) as a function of projected virial radius and azimuthal angle. We compare these simulations to an observed sample of 53 galaxies from the Multiphase Galaxy Halos Survey. Using Mockspec, an absorption-line analysis pipeline, we generate synthetic quasar absorption-line observations of the simulated CGM. To best emulate observations, we studied the averaged properties of 15,000 “mock samples,” each of 53 sight lines having a distribution of and sight-line orientation statistically consistent with the observations. We find that the covering fraction obtained for the simulated galaxies agrees well with the observed value for the inner halo ( ) and is within 1. 1σ in the outer halo ( ), but is underproduced within . The observed bimodal distribution of the covering fraction with azimuthal angle, showing a higher frequency of absorption along the projected major and minor axes of galaxies, is not reproduced in the simulations. Further analysis reveals the spatial-kinematic distribution of -absorbing gas is dominated by outflows in the inner halo mixed with an inflowing gas that originates from farther out in the halo. Though the CGM of the individual simulated galaxies exhibits spatial structure, the flat azimuthal distribution occurs because the individual simulated galaxies do not develop a CGM structure that is universal from galaxy to galaxy.
Higher-curvature corrections and tensor modes

Higher-curvature corrections to the effective gravitational action may leave signatures in the spectrum of primordial tensor perturbations if the inflationary energy scale is sufficiently high. In this paper, we further investigate the effects of a coupling of the Inflaton field to higher-curvature tensors in models with a minimal breaking of conformal symmetry. We show that an observable violation of the tensor consistency relation from higher-curvature tensors implies also a relatively large running of the tensor tilt, enhanced even by some order of magnitude with respect to the standard slow-roll case. This may leave signatures in the tensor two-point function that we could test to recognize higher-curvature effects, above all if they are translated into a blue tilted spectrum visible by future gravitational wave experiments. Exploiting current cosmic microwave background and gravitational wave data, we also derive constraints on the inflationary parameters, inferring that large higher-curvature corrections seem to be disfavored.
NuSTAR Observations of Abell 2163: Constraints on Non-thermal Emission

Since the first non-thermal reports of inverse Compton (IC) emission from the intracluster medium (ICM) of galaxy clusters at hard X-ray energies, we have yet to unambiguously confirm IC emission in observations with newer facilities. RXTE detected IC emission in one of the hottest known clusters, Abell 2163 (A2163), a massive merging cluster with a giant radio halo—the presumed source of relativistic electrons IC scattering CMB photons to X-ray energies. The cluster’s redshift (z ∼ 0.2) allows its thermal and non-thermal radio emission to fit the field of view of the Nuclear Spectroscopic Telescope Array (NuSTAR), permitting a deep observation capable of confirming or ruling out the RXTE report. The IC flux provides constraints on the average magnetic field strength in a cluster. To determine the global diffuse IC emission in A2163, we fit its global NuSTAR spectrum with four models: single- (1T) and two-temperature (2T), 1T+power-law component (T+IC), and multi-temperature+power law (9T+IC). Each represents different characterizations of the thermal ICM emission, with power-law components added to represent IC emission. We find that the 3–30 keV spectrum can be described by purely thermal emission, with a global average temperature of kT = (11.8 ± 0.2) keV. The IC flux is constrained to <4.0 × 10−12 erg s−1 cm−2 using the 1T+IC model and <1.6 × 10−12 erg s−1 cm−2 with the more physical 9T+IC model, both to 90% confidence levels. Combining these limits with 1.4 GHz diffuse radio data from the VLA, we find the average magnetic field strength to be >0.22 μG and >0.35 μG, respectively, providing the strongest constraints on these values in A2163 to date.
Applying Machine Learning Advances to Data Visualization: A Survey on ML4VIS

Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VIS is needed. In this paper, we systematically survey \paperNum ML4VIS studies, aiming to answer two motivating questions: "what visualization processes can be assisted by ML? " and "how ML techniques can be used to solve visualization problems? " This survey reveals six main processes where the employment of ML techniques can benefit visualizations: VIS-driven Data Processing, Data Presentation, Insight Communication, Style Imitation, VIS Interaction, VIS Perception. The six processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations. Meanwhile, the six processes are mapped into main learning tasks in ML to align the capabilities of ML with the needs in visualization. Current practices and future opportunities of ML4VIS are discussed in the context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are still needed in the area of ML4VIS, we hope this paper can provide a stepping-stone for future exploration. A web-based interactive browser of this survey is available at https://ml4vis.github. io.
Machine-learning recognition of light orbital-angular-momentum superpositions

We developed a method to characterize arbitrary superpositions of light orbital angular momentum (OAM) with high fidelity by using astigmatic tomography and machine learning processing. In order to define each superposition unequivocally, we combine two intensity measurements. The first one is the direct image of the input beam, which cannot distinguish between opposite OAM components. This ambiguity is removed by a second image obtained after astigmatic transformation of the input beam. Samples of these image pairs are used to train a convolution neural network and achieve high fidelity recognition of arbitrary OAM superpositions with dimension up to five.