Miracle-dz/UniFAR-FTUs huggingface dataset:
A Critical Analysis of the Characterization of Scroll Compressors Energy Consumption

This paper presents the analysis of the energy consumption of scroll type compressors. The study has included the data of several AHRI reports: (especially AHRI-11 and AHRI-21) as well as data from other source. A total of 8 different scroll compressors, of different size, some of them tested with various refrigerants (R134a, R32, R410A, R404A.) have been considered in the study. The values of the compressor consumption, and of the corresponding compressor efficiency, and the shape of the corresponding response surfaces, for all the studied compressors and refrigerants, have been analyzed with the objective of understanding better the dependence of the energy compressor consumption on the operating conditions and the refrigerant. The analyzed data include tests following different superheat control, i.e. constant superheat or constant return temperature, so the effect of the inlet temperature on the energy consumption and efficiency are also discussed. The paper includes the analysis of the compressor consumption as dependent of the temperatures of the tested points, and alternatively as dependent of the corresponding pressures, and as a result it will be shown that the representation as a function of pressures is more universal than the one made with temperatures. Two simple correlation polynomials, based on suction and discharge pressures, are presented, which require less empirical information and have better interpolation-extrapolation characteristics than the AHRI standard correlation.
Twin-peak quasi-periodic oscillations and the kinematics of orbital motion in a curved space–time

Twin peak high frequency quasi-periodic oscillations (HF QPOs) observed in the power spectra of Low Mass X-ray Binaries (LMXBs), with either a black hole or a neutron star, have central frequencies that are typical of the orbital motion time-scale close to the compact object. Thus, twin HF QPOs might carry the fingerprint of physical effects in a strongly curved space-time. We study the azimuth phase \phi(t) for orbital motion in the Schwarzschild metric and calculate the power spectra to check whether they display the features seen in the observed ones. We show that the timing of \phi(t) on non-closed orbits can account for the observed twin peak HF QPOs. The uppermost couple of peaks in frequency has the lower peak that corresponds to the azimuthal frequency \nu_{\phi}, the upper one to \nu_{\phi}+\nu_r. The azimuth phase temporal behavior \phi(t) on a slightly eccentric orbit in the Schwarzschild metric is described by a linear function of slope \nu_{\phi} plus an oscillating term at the relativistic radial frequency \nu_r. We deduce that the twin peak HF QPOs might originate from a frequency modulated (FM) signal driven by the kinematics of orbital motion in a curved space-time.
“Water-cycle” mechanism for writing and erasing nanostructures at the LaAlO3/SrTiO3 interface

Nanoscale control of the metal-insulator transition in LaAlO3/SrTiO3 heterostructures can be achieved using local voltages applied by a conductive atomic force microscope (c-AFM) probe. One proposed mechanism for such process involves adsorbed H2O which dissociates into OH− and H+ which are then selectively removed by a biased AFM probe. To test this mechanism, writing and erasing experiments are performed in a vacuum AFM using various gas mixtures. Writing ability is suppressed in those environments where H2O is not present. The self-erasure process in air can be strongly suppressed by creating a modest vacuum or replacing the humid air with dry inert gas. These experiments provide strong constraints for theories of both the writing process as well as the origin of interfacial conductance.
Thermalization under randomized local Hamiltonians

Recently, there have been significant new insights concerning the conditions under which closed systems equilibrate locally. The question of whether subsystems thermalize—if the equilibrium state is independent of the initial state—is, however, much harder to answer in general. Here, we consider a setting in which thermalization can be addressed: a quantum quench under a Hamiltonian whose spectrum is fixed and whose basis is drawn from the Haar measure. If the Fourier transform of the spectral density is small, almost all bases lead to local equilibration to the thermal state with infinite temperature. This allows us to show that, under almost all Hamiltonians that are unitarily equivalent to a local Hamiltonian, it takes an algebraically small time for subsystems to thermalize.
Prediction and location of malignant nodules and microcalcifications in mammography via Deep Learning

Objectives: To propose a tool to detect and locate malignant nodules and microcalcifications in mammography and judge its potential as a screening tool. Methods: In this institutional review board approved retrospective study we presented a new tool based on deep learning techniques to predict and locate lesions in mammograms, called quantusMM. 3,114 mammograms from 976 patients were collected from Onkologikoa (Instituto Oncologico de Kutxa) databases for this purpose: 1,248 images with malignant nodules, 736 with malignant microcalcifications and 1,131 without any suspicious findings. The proposed methods split the images in patches to be able to locate the lesions in the image. Then, these methods select the patches most likely to have a lesion based on the brightness values of the pixels. 80% of the selected patches (with the corresponding outcome) were used to train deep learning algorithms and the remaining 20% were used to test the performance to classify into malignant parts or control parts. Results: The proposed methods obtain an area under the ROC curve (AUC) of 95.5% to predict malignant nodules using the patches, and 90.4% to predict malignant nodules into the whole images. To predict malignant microcalcifications the method obtains an AUC of 99.0% into patches and 90.0% into the whole images. Conclusions: The proposed tool shows potential to predict and locate malignant nodules and microcalcification lesions in mammography. This new approach could help in the first screening of patients and also could greatly benefit radiologists to support decision making.
Alpha Globin Gene Copy Number and Exhaled Nitric Oxide in Healthy Black Adults

BACKGROUND: Nitric oxide has a range of biological activity in the lung and the fractional exhalation of nitric oxide (FeNO) is useful in patients with asthma in whom treatment is being considered. Normal variation in FeNO may impact the interpretation of this physiologic measurement, yet little is known about genetic factors that influence FeNO, particularly among Black populations. Growing evidence that globins are co-expressed with NOS in airway epithelium, along with the structural similarity of iNOS and eNOS, imply that globins may regulate NO signaling not only in the vascular endothelium, but in the airway epithelium as well. Given that Black individuals have increased HBA allele count variation, we assessed the role of alpha globin in airway nitric oxide physiology, we examined the association of alpha globin gene deletions with FeNO in healthy Black individuals. METHODS: We measured HBA copy number via droplet digital PCR in Black healthy volunteers age 18-40 years were enrolled in a multi-center, cross-sectional cohort from 4 sites near Durham, North Carolina. Subjects self-reported health status, age, sex, race, and ethnicity. Only non-Hispanic African American subjects were enrolled in the original study. Exclusion criteria for this analysis were: 1) not consenting to future research and 2) serum cotinine level >25 ng/mL to exclude active tobacco use. For continuous measures, medians and 25th and 75th percentiles were calculated by alpha globin genotype. Differences between groups were assessed by Kruskal-Wallis test. Categorical variables were assessed by Fisher's exact test. IgE and FeNO were log transformed due to skewness. The association of HBA copy number with FeNO was evaluated using multivariable linear regression employing a linear effect of HBA allele count (2-5 HBA copies). RESULTS: Among DNA specimens from 643 Black individuals, HBA genotype frequencies were: 30 (4.7%) -a/-a, 197 (30.6%) -a/aa, 405 (63%) aa/aa, and 8 (1.2%) aa/aaa, with the median (25th, 75th percentile) measured FeNO value of 20 (13, 31) ppb. Subjects were 35% male with median age 20 (19, 22) years and median IgE level of 58 (22, 160) kU/L. After adjustment for sex, age, and log transformed total IgE, the coefficient for HBA copy number with FeNO was -0.005 (95% CI: -0.042, 0.033, p=0.81). CONCLUSIONS: No clear association between HBA copy number and FeNO was found among healthy Black adults in this cohort. Many questions regarding the roles of alpha and beta globin in epithelial NO signaling and pulmonary pathophysiology remain unanswered.
Detection of maternal transmission of resistant Gram-negative bacteria in a Cambodian hospital setting

Infection with Extended-spectrum beta-lactamase -producing Enterobacterales (ESBL-E) is common in infants and leads to increased intensive care unit admission and mortality, but the role of maternal transmission in colonization of infants is unclear. Using paired isolates from 50 pairs of mothers and neonates admitted to a Cambodian hospital, we investigated antimicrobial resistance in Escherichia coli and Klebsiella pneumoniae using whole genome sequencing. We detected a wide variety of ESBL-E genes present in this population along with high levels of multidrug resistance. From 21 pairs where the same organism was present in both mother and neonate, we identified eight pairs with identical or near-identical isolates from both individuals suggestive of transmission at or around birth, including a pair with transmission of multiple strains. We found no evidence for transmission of plasmids only from mother to infant. This suggests vertical transmission outside hospitals as a common cause of ESBL-E colonization in neonates.
SGD with large step sizes learns sparse features

We showcase important features of the dynamics of the Stochastic Gradient Descent (SGD) in the training of neural networks. We present empirical observations that commonly used large step sizes (i) lead the iterates to jump from one side of a valley to the other causing loss stabilization, and (ii) this stabilization induces a hidden stochastic dynamics orthogonal to the bouncing directions that biases it implicitly toward sparse predictors. Furthermore, we show empirically that the longer large step sizes keep SGD high in the loss landscape valleys, the better the implicit regularization can operate and find sparse representations. Notably, no explicit regularization is used so that the regularization effect comes solely from the SGD training dynamics influenced by the step size schedule. Therefore, these observations unveil how, through the step size schedules, both gradient and noise drive together the SGD dynamics through the loss landscape of neural networks. We justify these findings theoretically through the study of simple neural network models as well as qualitative arguments inspired from stochastic processes. Finally, this analysis allows us to shed a new light on some common practice and observed phenomena when training neural networks. The code of our experiments is available at https://github.com/tml-epfl/sgd-sparse-features.
An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition

Precisely naming the action depicted in a video can be a challenging and oftentimes ambiguous task. In contrast to object instances represented as nouns (e.g. dog, cat, chair, etc.), in the case of actions, human annotators typically lack a consensus as to what constitutes a specific action (e.g. jogging versus running). In practice, a given video can contain multiple valid positive annotations for the same action. As a result, video datasets often contain significant levels of label noise and overlap between the atomic action classes. In this work, we address the challenge of training multi-label action recognition models from only single positive training labels. We propose two approaches that are based on generating pseudo training examples sampled from similar instances within the train set. Unlike other approaches that use model-derived pseudo-labels, our pseudo-labels come from human annotations and are selected based on feature similarity. To validate our approaches, we create a new evaluation benchmark by manually annotating a subset of EPIC-Kitchens-100's validation set with multiple verb labels. We present results on this new test set along with additional results on a new version of HMDB-51, called Confusing-HMDB-102, where we outperform existing methods in both cases. Data and code are available at https://github.com/kiyoon/verb_ambiguity
Single event tolerance of x-ray silicon-on-insulator pixel sensors

Abstract. We evaluate the single event tolerance of the x-ray silicon-on-insulator (SOI) pixel sensor named XRPIX, developed for the future x-ray astronomical satellite FORCE. In this work, we measure the cross-section of single event upset (SEU) of the shift register on XRPIX by irradiating heavy ion beams with linear energy transfer (LET) ranging from 0.022 to 68 MeV / ( mg/cm2 ) . From the SEU cross-section curve, the saturation cross-section and threshold LET are successfully obtained to be 3.4−0.9+2.9×10−10 cm2/bit and 7.3−3.5+1.9 MeV/(mg/cm2), respectively. Using these values, the SEU rate in orbit is estimated to be ≲ 0.1 event / year primarily due to the secondary particles induced by cosmic-ray protons. This SEU rate of the shift register on XRPIX is negligible in the FORCE orbit.
An SIE Formulation With Triangular Discretization and Loop Analysis for Parameter Extraction of Arbitrarily Shaped Interconnects

A surface integral equation (SIE) formulation under the magneto-quasi-static assumption is proposed to efficiently and accurately model arbitrarily shaped interconnects in packages. Through decently transferring all electromagnetic quantities into circuit elements, the loop analysis is used to carefully construct matrix equations with an independent and complete set of unknowns based on graph theory. In addition, an efficient preconditioner is developed, and the proposed formulation is accelerated by the pre-corrected fast Fourier transform (pFFT). Four practical examples, including a rectangular metallic interconnect, bonding wire arrays, interconnects in a real-life circuit, and the power distribution network (PDN) used in packages, are carried out to validate its accuracy, efficiency, and scalability. Results show that the proposed formulation is accurate, efficient, and flexible to model complex interconnects in packages.
PROBING THE EXTREME REALM OF ACTIVE GALACTIC NUCLEUS FEEDBACK IN THE MASSIVE GALAXY CLUSTER, RX J1532.9+3021

We present a detailed Chandra, XMM-Newton, Very Large Array (VLA) and Hubble Space Telescope analysis of one of the strongest cool core clusters known, RX J1532.9+3021 (z = 0.3613). Using new, deep 90 ks Chandra observations, we confirm the presence of a western X-ray cavity or bubble, and report on a newly discovered eastern X-ray cavity. The total mechanical power associated with these active galactic nucleus (AGN) driven outflows is (22 ± 9) × 1044 erg s−1, and is sufficient to offset the cooling, indicating that AGN feedback still provides a viable solution to the cooling flow problem even in the strongest cool core clusters. Based on the distribution of the optical filaments, as well as a jet-like structure seen in the 325 MHz VLA radio map, we suggest that the cluster harbors older outflows along the north to south direction. The jet of the central AGN is therefore either precessing or sloshing-induced motions have caused the outflows to change directions. There are also hints of an X-ray depression to the north aligned with the 325 MHz jet-like structure, which might represent the highest redshift ghost cavity discovered to date. We further find evidence of a cold front (r ≈ 65 kpc) that coincides with the outermost edge of the western X-ray cavity and the edge of the radio mini-halo. The common location of the cold front with the edge of the radio mini-halo supports the idea that the latter originates from electrons being reaccelerated due to sloshing-induced turbulence. Alternatively, its coexistence with the edge of the X-ray cavity may be due to cool gas being dragged out by the outburst. We confirm that the central AGN is highly sub-Eddington and conclude that a >1010 M☉ or a rapidly spinning black hole is favored to explain both the radiative-inefficiency of the AGN and the powerful X-ray cavities.
Simple parking strategies

We investigate simple strategies that embody the decisions that one faces when trying to park near a popular destination. Should one park far from the target (destination), where finding a spot is easy, but then be faced with a long walk, or should one attempt to look for a desirable spot close to the target, where spots may be hard to find? We study an idealized parking process on a one-dimensional geometry where the desired target is located at x = 0, cars enter the system from the right at a rate and each car leaves at a unit rate. We analyze three parking strategies—meek, prudent, and optimistic—and determine which is optimal.
Domain-Specific Word Embeddings with Structure Prediction

Complementary to finding good general word embeddings, an important question for representation learning is to find dynamic word embeddings, for example, across time or domain. Current methods do not offer a way to use or predict information on structure between sub-corpora, time or domain and dynamic embeddings can only be compared after post-alignment. We propose novel word embedding methods that provide general word representations for the whole corpus, domain- specific representations for each sub-corpus, sub-corpus structure, and embedding alignment simultaneously. We present an empirical evaluation on New York Times articles and two English Wikipedia datasets with articles on science and philosophy. Our method, called Word2Vec with Structure Prediction (W2VPred), provides better performance than baselines in terms of the general analogy tests, domain-specific analogy tests, and multiple specific word embedding evaluations as well as structure prediction performance when no structure is given a priori. As a use case in the field of Digital Humanities we demonstrate how to raise novel research questions for high literature from the German Text Archive.
The regularity with respect to domains of the additive eigenvalues of superquadratic Hamilton--Jacobi equation

Let $c(\lambda)$ be the additive eigenvalue with respect to $(1+r(\lambda))\Omega$, we show that $\lambda\mapsto c(\lambda)$ is differentiable except at most a countable set, while one-sided derivatives exist everywhere. The convergence of the vanishing discount problem on changing domains is also considered, and furthermore, the limiting solution can be parametrized by a real function. Finally, we connect the regularity of this real function to the regularity of $\lambda \mapsto c(\lambda)$. Some examples are given where higher regularity is achieved.
Light emission in delta-
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math>
-driven mesoscopic conductors

The scattering picture of electron transport in mesoscopic conductors shows that fluctuations of the current reveal additional information on the scattering mechanism not available through the conductance alone. The electronic fluctuations are coupled to the electromagnetic field and a junction at finite bias or temperature will emit radiation. The nonsymmetrized current-current correlators characterize the emission and absorption spectrum. Recent interest is focused on the so-called delta-T noise, which is the nonequilibrium noise caused by a temperature difference between the terminals. Here, we generalize the notion of delta-T noise to the nonsymmetrized current-current correlator at finite frequencies. We investigate the spectral density for energy-independent scattering and for a resonant level as an example of energy-dependent scattering. We find that a temperature difference $\Delta T$ leads to a partial reduction of the noise for certain frequency ranges. This is a consequence of temperature broadening in combination with a frequency shift of the involved Fermi distributions. In the case of energy-independent scattering, the lowest order is a quadratic $\propto (\Delta T)^2$ correction of the thermal-like noise spectrum. For the resonance, additional contributions to the delta-T noise spectrum arise that are $\propto \Delta T$ to the lowest order.
DPANET: Dual Pooling Attention Network for Semantic Segmentation

Image segmentation is a historic and significant computer vision task. With the help of deep learning techniques, image semantic segmentation has made great progresses. Over recent years, based on guidance of attention mechanism compared with CNN which overcomes the problems of lacking of interaction between different channels, and effective capturing and aggregating contextual information. However, the massive operations generated by the attention mechanism lead to its extremely high complexity and high demand for GPU memory. For this purpose, we propose a lightweight and flexible neural network named Dual Pool Attention Network(DPANet). The most important is that all modules in DPANet generate \textbf{0} parameters. The first component is spatial pool attention module, we formulate an easy and powerful method densely to extract contextual characteristics and reduce the amount of calculation and complexity dramatically.Meanwhile, it demonstrates the power of even and large kernel size. The second component is channel pool attention module. It is known that the computation process of CNN incorporates the information of spatial and channel dimensions. So, the aim of this module is stripping them out, in order to construct relationship of all channels and heighten different channels semantic information selectively. Moreover, we experiments on segmentation datasets, which shows our method simple and effective with low parameters and calculation complexity.
Class-Specific Explainability for Deep Time Series Classifiers

Explainability helps users trust deep learning solutions for time series classification. However, existing explainability methods for multi-class time series classifiers focus on one class at a time, ignoring relationships between the classes. Instead, when a classifier is choosing between many classes, an effective explanation must show what sets the chosen class apart from the rest. We now formalize this notion, studying the open problem of class-specific explainability for deep time series classifiers, a challenging and impactful problem setting. We design a novel explainability method, DEMUX, which learns saliency maps for explaining deep multi-class time series classifiers by adaptively ensuring that its explanation spotlights the regions in an input time series that a model uses specifically to its predicted class. DEMUX adopts a gradient-based approach composed of three interdependent modules that combine to generate consistent, class-specific saliency maps that remain faithful to the classifier’s behavior yet are easily understood by end users. Our experimental study demonstrates that DEMUX outperforms nine state-of-the-art alternatives on five popular datasets when explaining two types of deep time series classifiers. Further, through a case study, we demonstrate that DEMUX’s explanations indeed highlight what separates the predicted class from the others in the eyes of the classifier.