Miracle-dz/UniFAR-FTUs huggingface dataset:
Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms

There has been a significant surge of interest recently around the concept of explainable artificial intelligence (XAI), where the goal is to produce an interpretation for a decision made by a machine learning algorithm. Of particular interest is the interpretation of how deep neural networks make decisions, given the complexity and `black box' nature of such networks. Given the infancy of the field, there has been very limited exploration into the assessment of the performance of explainability methods, with most evaluations centered around subjective visual interpretation of the produced interpretations. In this study, we explore a more machine-centric strategy for quantifying the performance of explainability methods on deep neural networks via the notion of decision-making impact analysis. We introduce two quantitative performance metrics: i) Impact Score, which assesses the percentage of critical factors with either strong confidence reduction impact or decision changing impact, and ii) Impact Coverage, which assesses the percentage coverage of adversarially impacted factors in the input. A comprehensive analysis using this approach was conducted on several state-of-the-art explainability methods (LIME, SHAP, Expected Gradients, GSInquire) on a ResNet-50 deep convolutional neural network using a subset of ImageNet for the task of image classification. Experimental results show that the critical regions identified by LIME within the tested images had the lowest impact on the decision-making process of the network (~38%), with progressive increase in decision-making impact for SHAP (~44%), Expected Gradients (~51%), and GSInquire (~76%). While by no means perfect, the hope is that the proposed machine-centric strategy helps push the conversation forward towards better metrics for evaluating explainability methods and improve trust in deep neural networks.
Arbitrarily High-order Linear Schemes for Gradient Flow Models

We present a paradigm for developing arbitrarily high order, linear, unconditionally energy stable numerical algorithms for gradient flow models. We apply the energy quadratization (EQ) technique to reformulate the general gradient flow model into an equivalent gradient flow model with a quadratic free energy and a modified mobility. Given solutions up to tn = n∆t with ∆t the time step size, we linearize the EQ-reformulated gradient flow model in (tn, tn+1] by extrapolation. Then we employ an algebraically stable Runge-Kutta method to discretize the linearized model in (tn, tn+1]. Then we use the Fourier pseudo-spectral method for the spatial discretization to match the order of accuracy in time. The resulting fully discrete scheme is linear, unconditionally energy stable, uniquely solvable, and may reach arbitrarily high order. Furthermore, we present a family of linear schemes based on predictioncorrection methods to complement the new linear schemes. Some benchmark numerical examples are given to demonstrate the accuracy and efficiency of the schemes.
Understanding the Origin of the Magnetic Field Morphology in the Wide-binary Protostellar System BHR 71

We present 1.3 mm Atacama Large Millimeter/submillimeter Array observations of polarized dust emission toward the wide-binary protostellar system BHR 71 IRS1 and IRS2. IRS1 features what appears to be a natal, hourglass-shaped magnetic field. In contrast, IRS2 exhibits a magnetic field that has been affected by its bipolar outflow. Toward IRS2, the polarization is confined mainly to the outflow cavity walls. Along the northern edge of the redshifted outflow cavity of IRS2, the polarized emission is sandwiched between the outflow and a filament of cold, dense gas traced by N2D+, toward which no dust polarization is detected. This suggests that the origin of the enhanced polarization in IRS2 is the irradiation of the outflow cavity walls, which enables the alignment of dust grains with respect to the magnetic field—but only to a depth of ∼300 au, beyond which the dust is cold and unpolarized. However, in order to align grains deep enough in the cavity walls, and to produce the high polarization fraction seen in IRS2, the aligning photons are likely to be in the mid- to far-infrared range, which suggests a degree of grain growth beyond what is typically expected in very young, Class 0 sources. Finally, toward IRS1 we see a narrow, linear feature with a high (10%–20%) polarization fraction and a well-ordered magnetic field that is not associated with the bipolar outflow cavity. We speculate that this feature may be a magnetized accretion streamer; however, this has yet to be confirmed by kinematic observations of dense-gas tracers.
Non-Hermitian scattering on a tight-binding lattice

We analyze the scattering dynamics and spectrum of a quantum particle on a tight-binding lattice subject to a non-Hermitian (purely imaginary) local potential. The reflection, transmission, and absorption coefficients are studied as a function of the strength of this absorbing potential. The system is found to have an exceptional point at a certain strength of the potential. Unusually, all (or nearly all) of the spectrum pairs up into mutually coalescing eigenstate pairs at this exceptional point. At large potential strengths, the absorption coefficient decreases and the effect of the imaginary potential is similar to that of a real potential. We quantify this similarity by utilizing properties of a localized eigenstate.
Proceedings of The Magnificent CE$\nu$NS Workshop 2018

The Magnificent CE$\nu$NS Workshop (2018) was held November 2 & 3 of 2018 on the University of Chicago campus and brought together theorists, phenomenologists, and experimentalists working in numerous areas but sharing a common interest in the process of coherent elastic neutrino-nucleus scattering (CE$\nu$NS). This is a collection of abstract-like summaries of the talks given at the meeting, including links to the slides presented. This document and the slides from the meeting provide an overview of the field and a snapshot of the robust CE$\nu$NS-related efforts both planned and underway.
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation

Infinite horizon off-policy policy evaluation is a highly challenging task due to the excessively large variance of typical importance sampling (IS) estimators. Recently, Liu et al. (2018a) proposed an approach that significantly reduces the variance of infinite-horizon off-policy evaluation by estimating the stationary density ratio, but at the cost of introducing potentially high biases due to the error in density ratio estimation. In this paper, we develop a bias-reduced augmentation of their method, which can take advantage of a learned value function to obtain higher accuracy. Our method is doubly robust in that the bias vanishes when either the density ratio or the value function estimation is perfect. In general, when either of them is accurate, the bias can also be reduced. Both theoretical and empirical results show that our method yields significant advantages over previous methods.
Conversion Rate Prediction via Post-Click Behaviour Modeling

Effective and efficient recommendation is crucial for modern e-commerce platforms. It consists of two indispensable components named Click-Through Rate (CTR) prediction and Conversion Rate (CVR) prediction, where the latter is an essential factor contributing to the final purchasing volume. Existing methods specifically predict CVR using the clicked and purchased samples, which has limited performance affected by the well-known sample selection bias and data sparsity issues. To address these issues, we propose a novel deep CVR prediction method by considering the post-click behaviors. After grouping deterministic actions together, we construct a novel sequential path, which elaborately depicts the post-click behaviors of users. Based on the path, we define the CVR and several related probabilities including CTR, etc., and devise a deep neural network with multiple targets involved accordingly. It takes advantage of the abundant samples with deterministic labels derived from the post-click actions, leading to a significant improvement of CVR prediction. Extensive experiments on both offline and online settings demonstrate its superiority over representative state-of-the-art methods.
The Gran Telescopio Canarias OSIRIS broad-band first data release

We present the first release of GTC OSIRIS broad-band data archive. This is an effort conducted in the framework of the Spanish Virtual Observatory to help optimize science from the Gran Telescopio Canarias Archive. Data Release 1 includes 6788 broad-band images in the Sloan griz filters obtained between 2009 April and 2014 January and the associated catalogue with roughly 6.23 million detections of more than 630 000 unique sources. The catalogue contains standard PSF and Kron aperture photometry with a mean accuracy better than 0.09 and 0.15 mag, respectively. The relative astrometric residuals are always better than 30 mas and better than 15 mas in most cases. The absolute astrometric uncertainty of the catalogue is of 0.12 arcsec. In this paper we describe the procedure followed to build the image archive and the associated catalogue, as well as the quality tests carried out for validation. To illustrate some of the scientific potential of the catalogue, we also provide two examples of its scientific exploitation: discovery and identification of asteroids and cool dwarfs.
Self-Supervised Learning for ECG-Based Emotion Recognition

We present an electrocardiogram (ECG) -based emotion recognition system using self-supervised learning. Our proposed architecture consists of two main networks, a signal transformation recognition network and an emotion recognition network. First, unlabelled data are used to successfully train the former network to detect specific pre-determined signal transformations in the self-supervised learning step. Next, the weights of the convolutional layers of this network are transferred to the emotion recognition network, and two dense layers are trained in order to classify arousal and valence scores. We show that our self-supervised approach helps the model learn the ECG feature manifold required for emotion recognition, performing equal or better than the fully-supervised version of the model. Our proposed method outperforms the state-of-the-art in ECG-based emotion recognition with two publicly available datasets, SWELL and AMIGOS. Further analysis highlights the advantage of our self-supervised approach in requiring significantly less data to achieve acceptable results.
In Silico Prediction of Cell Traction Forces

Traction Force Microscopy (TFM) is a technique used to determine the tensions that a biological cell conveys to the underlying surface. Typically, TFM requires culturing cells on gels with fluorescent beads, followed by bead displacement calculations. We present a new method allowing to predict those forces from a regular fluorescent image of the cell. Using Deep Learning, we trained a Bayesian Neural Network adapted for pixel regression of the forces and show that it generalises on different cells of the same strain. The predicted forces are computed along with an approximated uncertainty, which shows whether the prediction is trustworthy or not. Using the proposed method could help estimating forces when calculating non-trivial bead displacements and can also free one of the fluorescent channels of the microscope. Code is available at https://github.com/wahlby-lab/InSilicoTFM.
Thermal Hall effect, spin Nernst effect, and spin density induced by thermal gradient in collinear ferrimagnets from magnon-phonon interaction.

We theoretically study the intrinsic thermal Hall and spin Nernst effect in collinear ferrimagnets on a honeycomb lattice with broken inversion symmetry. The broken inversion symmetry allows in-plane Dzyaloshinskii-Moriya interaction between the nearest neighbors, which does not affect the linear spin wave theory. However, the DzyaloshinskiiMoriya interaction induces large Berry curvature in the magnetoelastic excitations through the magnon-phonon interaction (MPI) to produce thermal Hall current. Furthermore, the magnetoelastic excitations transport spin, which is inherited from the magnons. Therefore, spin Nernst current accompanies the thermal Hall current. Because the MPI does not conserve the spin, we examine the spatial distribution of spin induced by thermal gradient in the system having a stripe geometry. We find that spin is accumulated at the edges, reflecting the spin Nernst current. We also find that the total spin of the system, and therefore the magnetization, is changed because of the thermal gradient and MPI.
Explainable Semantic Mapping for First Responders

One of the key challenges in the semantic mapping problem in postdisaster environments is how to analyze a large amount of data efficiently with minimal supervision. To address this challenge, we propose a deep learning-based semantic mapping tool consisting of three main ideas. First, we develop a frugal semantic segmentation algorithm that uses only a small amount of labeled data. Next, we investigate on the problem of learning to detect a new class of object using just a few training examples. Finally, we develop an explainable cost map learning algorithm that can be quickly trained to generate traversability cost maps using only raw sensor data such as aerial-view imagery. This paper presents an overview of the proposed idea and the lessons learned.
Relaxation dynamics in double-spin systems

We consider the relaxation dynamics of two magnetic domains coupled to a common bosonic bath. Interference effects due to the coherent coupling to the common bath gives rise to characteristic features in the relaxation dynamics after a quench or during a periodic external driving. In particular, we find that the steady state during periodic driving depends sensitively on the initial state as well as on system parameters such as coupling asymmetries. When coupled to more than a single reservoir, the interference effects can lead to a cooling mechanism for one of the bosonic reservoirs.
Neural Network Design for Energy-Autonomous AI Applications using Temporal Encoding

Neural Networks (NNs) are steering a new generation of artificial intelligence (AI) applications at the micro-edge. Examples include wireless sensors, wearables and cybernetic systems that collect data and process them to support real-world decisions and controls. For energy autonomy, these applications are typically powered by energy harvesters. As harvesters and other power sources which provide energy autonomy inevitably have power variations, the circuits need to robustly operate over a dynamic power envelope. In other words, the NN hardware needs to be able to function correctly under unpredictable and variable supply voltages. In this paper, we propose a novel NN design approach using the principle of pulse width modulation (PWM). PWM signals represent information with their duty cycle values which may be made independent of the voltages and frequencies of the carrier signals. We design a PWM-based perceptron which can serve as the fundamental building block for NNs, by using an entirely new method of realising arithmetic in the PWM domain. We analyse the proposed approach building from a 3x3 perceptron circuit to a complex multi-layer NN. Using handwritten character recognition as an exemplar of AI applications, we demonstrate the power elasticity, resilience and efficiency of the proposed NN design in the presence of functional and parametric variations including large voltage variations in the power supply.
YACON INULIN LEACHING DURING HOT WATER BLANCHING Dissolução de inulina do yacon durante o branqueamento em água quente

Yacon roots contain inulin, which has prebiotic properties and it may be used as sucrose or fat substitutes. However, inulin is very soluble in water. The loss of this important nutrient during blanching is caused mainly by diffusion or leaching, which might be diminished if blanching temperature time conditions are correctly employed. The aim of this study was to determine the leaching of the sugars inulin, glucose and fructose, present in yacon roots, during hot water blanching under different time/temperature conditions. The samples were cleaned and peeled and cut into geometric forms of 1.75 ± 0.35 mm thick disks. A complete factorial experimental design was used, and the treatments of the samples were compared using the Tukey test. The results indicated that the time and temperature were significant in the dissolution of the sugars. The lowest inulin losses occurred at temperatures and times lower than 60 oC and 3 minutes. For all temperatures, the lowest glucose and fructose losses were obtained at time lower than 3 and 5 minutes, respectively.
Aerosol optical depth in the European Brewer Network : Reply to Anonymous Referee # 1 , RC 1 , 20 Nov 2017

Javier López-Solano1,2,3, Alberto Redondas1,3, Thomas Carlund4,5, Juan J. Rodriguez-Franco1,3, Henri Diémoz6, Sergio F. León-Luis1,3, Bentorey Hernández-Cruz2,3, Carmen Guirado-Fuentes7,1, Natalia Kouremeti4, Julian Gröbner4, Stelios Kazadzis4, Virgilio Carreño1,3, Alberto Berjón2,3, Daniel Santana-Díaz2,3, Manuel Rodríguez-Valido2,3, Veerle De Bock8, Juan R. Moreta9, John Rimmer10, Andrew R. D. Smedley10, Lamine Boulkelia11, Nis Jepsen12, Paul Eriksen12, Alkiviadis F. Bais13, Vadim Shirotov14, José M. Vilaplana15, Keith M. Wilson16, and Tomi Karppinen17
Law, Kristofer and West, Graeme and Murray, Paul and Lynch, Chris (2017) 3-D advanced gas-cooled nuclear reactor fuel channel reconstruction using structure-from-motion. In: 10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human

During planned, periodic outages, a selection of fuel channels within the UK fleet of Advanced Gas-cooled Reactor (AGR) cores are inspected using specialist tools which record video footage and other sensory data for each channel which undergoes inspection. Current visualization techniques comprise of manually produced montages by inspection engineers of points of interest (i.e. structural defects) and 2-D panoramic images of the fuel channels automatically produced using bespoke image stitching software. Both techniques however provide limited structural information due to the loss of depth data as a result of the image formation process. By recovering the depth information from the footage, a 3D model could be constructed and subsequently, allow for more accurate profiling of specific defects observed during inspection in addition to obtaining the fuel channels structure using existing footage. This work explores the preliminary application of a 3-D visualization technique known as Structure-from-Motion (SfM) which aims to obtain 3-D information by exploiting image correspondences across multiple viewpoints of the same scene in the RVI footage. This paper investigates the difficulties of applying state-of-the-art SfM to RVI footage and we present new techniques to improve feature correspondence searching in repetitive, non-descript environments.
Lawrence Berkeley National Laboratory Recent Work Title Complete genome sequence of the chromate-reducing bacterium Thermoanaerobacter thermohydrosulfuricus strain BSB-33 Permalink

Thermoanaerobacter thermohydrosulfuricus BSB-33 is a thermophilic gram positive obligate anaerobe isolated from a hot spring in West Bengal, India. Unlike other T. thermohydrosulfuricus strains, BSB-33 is able to anaerobically reduce Fe(III) and Cr(VI) optimally at 60 °C. BSB-33 is the first Cr(VI) reducing T. thermohydrosulfuricus genome sequenced and of particular interest for bioremediation of environmental chromium contaminations. Here we discuss features of T. thermohydrosulfuricus BSB-33 and the unique genetic elements that may account for the peculiar metal reducing properties of this organism. The T. thermohydrosulfuricus BSB-33 genome comprises 2597606 bp encoding 2581 protein genes, 12 rRNA, 193 pseudogenes and has a G + C content of 34.20 %. Putative chromate reductases were identified by comparative analyses with other Thermoanaerobacter and chromate-reducing bacteria.
Prior work on MaxEnt RL offers a slew of intuitive explanations for why one might prefer MaxEnt

Experimentally, it has been observed that humans and animals often make decisions that do not maximize their expected utility, but rather choose outcomes randomly, with probability proportional to expected utility. Probability matching, as this strategy is called, is equivalent to maximum entropy reinforcement learning (MaxEnt RL). However, MaxEnt RL does not optimize expected utility. In this paper, we formally show that MaxEnt RL does optimally solve certain classes of control problems with variability in the reward function. In particular, we show (1) that MaxEnt RL can be used to solve a certain class of POMDPs, and (2) that MaxEnt RL is equivalent to a two-player game where an adversary chooses the reward function. These results suggest a deeper connection between MaxEnt RL, robust control, and POMDPs, and provide insight for the types of problems for which we might expect MaxEnt RL to produce effective solutions. Specifically, our results suggest that domains with uncertainty in the task goal may be especially well-suited for MaxEnt RL methods.
Sound quality improvement for speech acquisition based on deep learning and harmonic reconstruction with laser microphone

A laser microphone has recently been focused on acquiring the distant target speech without unnecessary sounds. It measures the vibration of the object near the target sound source by irradiating the object surface with the laser beam. However, the speech acquired with this microphone degrades the sound quality. For instance, the speech components at higher frequencies are attenuated by vibration characteristic of the object, and the speech is collapsed by the stationary noise due to lower intensity of the laser beam reflected from the object. To improve the sound quality of the degraded speech, deep neural networks (DNNs) have recently been proposed. They are trained by using a set of acoustic features extracted from the degraded speech and the clean speech. However, the speech components at higher frequencies are still attenuated after processed by the DNN. Therefore, we propose the method to improve the sound quality with harmonic reconstruction after processed by the DNN. The proposed method is based on the harmonic structure of the speech signal and reconstructs the speech harmonics by utilizing a non-linear function. We evaluated the effectiveness of the proposed method through perceptual evaluation of speech quality.
Nitrogen-fixing Bacteria and Trends in Agricultural Applications

Bacteria that can increase the number of nutrients in the soil are important to plants, especially nitrogen-fixing bacteria that fix atmospheric nitrogen and change into the form that plants can use. In recent years, the use of nitrogen-fixing bacteria in agriculture has received a lot of attention because it offers an economically attractive and environmentally friendly method. Many species of nitrogen-fixing bacteria, symbiotic and non-symbiotic, that promote plant growth are used on a regular basis in order to improve crop yields. In addition to agricultural benefits, there are also potential benefits for environmental applications. Many nitrogenfixing bacteria which grow and multiply within plant tissues are called endophytes. They illustrate the tight association with the plant tissues without causing damage. Therefore, different types of endophytes which produce plant growth hormone provide benefit for many plants. A b s t r a c t