Miracle-dz/UniFAR-FTUs huggingface dataset:
Muon-induced background in a next-generation dark matter experiment based on liquid xenon

Muon-induced neutrons can lead to potentially irreducible backgrounds in rare event search experiments. We have investigated the implication of laboratory depth on the muon-induced background in a future dark matter experiment capable of reaching the so-called neutrino floor. Our simulation study focused on a xenon-based detector with 70 tonnes of active mass, surrounded by additional veto systems plus a water shield. Two locations at the Boulby Underground Laboratory (UK) were analysed as examples: an experimental cavern in salt at a depth of 2850 m w. e. (similar to the location of the existing laboratory), and a deeper laboratory located in polyhalite rock at a depth of 3575 m w. e. Our results show that no cosmogenic background events are likely to survive standard analysis cuts for 10 years of operation at either location. The largest background component we identified comes from beta-delayed neutron emission from $^{17}$N which is produced from $^{19}$F in the fluoropolymer components of the experiment. Our results confirm that a dark matter search with sensitivity to the neutrino floor is viable (from the point of view of cosmogenic backgrounds) in underground laboratories at these levels of rock overburden. This work was conducted in 2019-21 in the context of a feasibility study to investigate the possibility of developing the Boulby Underground Laboratory to host a next-generation dark matter experiment; however, our findings are also relevant for other underground laboratories.
Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation

Machine translation technology has made great progress in recent years, but it cannot guarantee error-free results. Human translators perform post-editing on machine translations to correct errors in the scene of computer aided translation. In favor of expediting the post-editing process, many works have investigated machine translation in interactive modes, in which machines can automatically refine the rest of translations constrained by human’s edits. Translation Suggestion (TS), as an interactive mode to assist human translators, requires machines to generate alternatives for specific incorrect words or phrases selected by human translators. In this paper, we utilize the parameterized objective function of neural machine translation (NMT) and propose a novel constrained decoding algorithm, namely Prefix-Suffix Guided Decoding (PSGD), to deal with the TS problem without additional training. Compared to state-of-the-art lexical-constrained decoding method, PSGD improves translation quality by an average of 10.6 BLEU and reduces time overhead by an average of 63.4% on benchmark datasets. Furthermore, on both the WeTS and the WMT 2022 Translation Suggestion datasets, it is superior over other supervised learning systems trained with TS annotated data.
Efficient HLA imputation from sequential SNPs data by Transformer

Human leukocyte antigen (HLA) genes are associated with a variety of dis-eases, however direct typing of HLA is time and cost consuming. Thus various imputation methods using sequential SNPs data have been pro-posed based on statistical or deep learning models, e.g. CNN-based model, named DEEP*HLA. However, imputation eﬃciency is not suﬃcient for infrequent alleles and a large size of reference panel is required. Here, we developed a Transformer-based model to impute HLA alleles, named ”HLA Reliable IMputatioN by Transformer (HLARIMNT)” to take advantage of sequential nature of SNPs data. We val-idated the performance of HLARIMNT using two diﬀerent reference panels; Pan-Asian reference panel ( n = 530) and Type 1 Diabetes Genetics Consortium (T1DGC) reference panel ( n = 5 , 225), as well as the mixture of those two panels ( n = 1 , 060). HLARIMNT achieved higher accuracy than DEEP*HLA by several indices, especially for infrequent alleles. We also varied the size of data used for training, and HLARIMNT imputed more accu-rately among any size of training data. These results suggest that Transformer-based model may impute eﬃciently not only HLA types but also any other gene types from sequential SNPs data.
On the convergence of the continuous version of the Moudafi's viscosity approximation method

We study the asymptotic behavior of trajectories of the continuous dynamical system (CDS) associated to the the discrete viscosity approximation method for fixed point problem of nonexpansive mapping (DDS) which was introduced by Moudafi in 2000 [A. Moudafi, Viscosity approximation methods for fixed points problems, J. Math. Anal. Appl. 241 (2000), 46-55]. We establish that the trajectories $x(t)$ of the system (CDS) and the sequences $(x_{n})$ generated by the the discrete process (DDS) have a very similar asymptotic behaviors.
A mild Itô formula for SPDEs

This article introduces a certain class of stochastic processes, which we suggest calling mild Itô processes, and a new, somehow mild, Itô-type formula for such processes. Examples of mild Itô processes are mild solutions of stochastic partial differential equations (SPDEs) and their numerical approximation processes. We illustrate the capacity of the mild Itô formula by several applications. In particular, we illustrate how the mild Itô formula can be used to derive improved a priori bounds for SPDEs, we demonstrate how the mild Itô formula can be employed to establish improved Hölder continuity properties for solutions of Kolmogorov partial differential equations (PDEs) in Hilbert spaces, and we illustrate how the mild Itô formula can be used to solve the weak convergence problem for numerical approximations of SPDEs with nonlinear diffusion coefficients.
Nonminimal kinetic coupled gravity: Inflation on the warped DGP brane

We consider the nonminimally kinetic coupled version of DGP brane model, where the kinetic term of the scalar field is coupled to the metric and Einstein tensor on the brane by a coupling constant ζ. We obtain the corresponding field equations, using the Friedmann–Robertson–Walker metric and the perfect fluid, and study the inflationary scenario to confront the numerical analysis of six typical scalar field potentials with the current observational results. We find that among the suggested potentials and coupling constants, subject to the e-folding N = 60, the potentials V (ϕ) = σϕ, V (ϕ) = σϕ2 and V (ϕ) = σϕ3 provide the best fits with both Planck+WP+highL data and Planck+WP+highL+BICEP2 data.
Horizon area–angular momentum–charge–magnetic flux inequalities in the 5D Einstein–Maxwell-dilaton gravity

In this paper, we consider 5D spacetimes satisfying the Einstein–Maxwell-dilaton gravity equations which are U(1)2 axisymmetric but otherwise highly dynamical. We derive inequalities between the area, the angular momenta, the electric charge and the magnetic fluxes for any smooth stably outer marginally trapped surface.
Degradation mechanism of CH3NH3PbI3 perovskite materials upon exposure to humid air

Low stability of organic-inorganic perovskite (CH3NH3PbI3) solar cells in humid air environments is a serious drawback which could limit practical application of this material severely. In this study, from real-time spectroscopic ellipsometry characterization, the degradation mechanism of ultra-smooth CH3NH3PbI3 layers prepared by a laser evaporation technique is studied. We present evidence that the CH3NH3PbI3 degradation in humid air proceeds by two competing reactions of (i) the PbI2 formation by the desorption of CH3NH3I species and (ii) the generation of a CH3NH3PbI3 hydrate phase by H2O incorporation. In particular, rapid phase change occurs in the near-surface region and the CH3NH3PbI3 layer thickness reduces rapidly in the initial 1 h air exposure even at a low relative humidity of 40%. After the prolonged air exposure, the CH3NH3PbI3 layer is converted completely to hexagonal platelet PbI2/hydrate crystals that have a distinct atomic-scale multilayer structure with a period of 0.65 ± 0.05 nm. We .
MAIN-BELT ASTEROIDS WITH WISE/NEOWISE: NEAR-INFRARED ALBEDOS

We present revised near-infrared albedo fits of 2835 main-belt asteroids observed by WISE/NEOWISE over the course of its fully cryogenic survey in 2010. These fits are derived from reflected-light near-infrared images taken simultaneously with thermal emission measurements, allowing for more accurate measurements of the near-infrared albedos than is possible for visible albedo measurements. Because our sample requires reflected light measurements, it undersamples small, low-albedo asteroids, as well as those with blue spectral slopes across the wavelengths investigated. We find that the main belt separates into three distinct groups of 6%, 16%, and 40% reflectance at 3.4 μm. Conversely, the 4.6 μm albedo distribution spans the full range of possible values with no clear grouping. Asteroid families show a narrow distribution of 3.4 μm albedos within each family that map to one of the three observed groupings, with the (221) Eos family being the sole family associated with the 16% reflectance 3.4 μm albedo group. We show that near-infrared albedos derived from simultaneous thermal emission and reflected light measurements are important indicators of asteroid taxonomy and can identify interesting targets for spectroscopic follow-up.
Lagrangian intersections and cuplength in generalised cohomology

We ﬁnd lower bounds on the number of intersection points between two relatively exact Hamiltonian isotopic Lagrangians. The bounds are given in terms of the cuplength of the Lagrangian in various multiplicative generalised cohomology theories. The intersection of the Lagrangians need not be transverse, however, we require certain orientation assump-tions. This gives stronger bounds than previous estimates on the number of intersection points for suitable Lagrangians diﬀeomorphic to Sp(2) or Sp(3). Our proof uses Lusternik-Schnirelmann theory, following and extending work by Hofer [Hof88].
Improving the Robustness of DistilHuBERT to Unseen Noisy Conditions via Data Augmentation, Curriculum Learning, and Multi-Task Enhancement

Self-supervised speech representation learning aims to extract meaningful factors from the speech signal that can later be used across different downstream tasks, such as speech and/or emotion recognition. Existing models, such as HuBERT, however, can be fairly large thus may not be suitable for edge speech applications. Moreover, realistic applications typically involve speech corrupted by noise and room reverberation, hence models need to provide representations that are robust to such environmental factors. In this study, we build on the so-called DistilHuBERT model, which distils HuBERT to a fraction of its original size, with three modiﬁcations, namely: (i) augment the training data with noise and reverberation, while the student model needs to distill the clean representations from the teacher model; (ii) introduce a curriculum learning approach where increasing levels of noise are introduced as the model trains, thus helping with convergence and with the creation of more robust representations; and (iii) introduce a multi-task learning approach where the model also reconstructs the clean waveform jointly with the distillation task, thus also acting as an enhancement step to ensure additional environment robustness to the representation. Experiments on three SUPERB tasks show the advantages of the proposed method not only relative to the original DistilHuBERT, but also to the original HuBERT, thus showing the advantages of the proposed method for “in the wild” edge speech applications.
The Study of Complex Human Locomotion Behaviors: From Crawling to Walking

This paper uses a simple state machine to develop a control algorithm for controlling an infant humanoid in the context of a simple model system. The algorithm is inspired by a baby who starts learning to stand and walk at 7 to 12 months of age: he or she initially learns to crawl and then, once the lower limb muscles are strong enough, can learn to walk by coming to support his or her upper trunk. Ideally, this algorithm-supported locomotion can take the baby to any desired location: a pile of toys, a tasty snack, or the baby's parents or relatives. In this paper we analyze the crawling stage, the simple 2d bipedal model, and the initial walking form from 8 to 18 months of age, and quantitatively evaluate the ideal kinematics model and simulation results for these stages.
Toward reliable calcification detection: calibration of uncertainty in object detection from coronary optical coherence tomography images

Abstract. Significance Optical coherence tomography (OCT) has become increasingly essential in assisting the treatment of coronary artery disease (CAD). However, unidentified calcified regions within a narrowed artery could impair the outcome of the treatment. Fast and objective identification is paramount to automatically procuring accurate readings on calcifications within the artery. Aim We aim to rapidly identify calcification in coronary OCT images using a bounding box and reduce the prediction bias in automated prediction models. Approach We first adopt a deep learning-based object detection model to rapidly draw the calcified region from coronary OCT images using a bounding box. We measure the uncertainty of predictions based on the expected calibration errors, thus assessing the certainty level of detection results. To calibrate confidence scores of predictions, we implement dependent logistic calibration using each detection result’s confidence and center coordinates. Results We implemented an object detection module to draw the boundary of the calcified region at a rate of 140 frames per second. With the calibrated confidence score of each prediction, we lower the uncertainty of predictions in calcification detection and eliminate the estimation bias from various object detection methods. The calibrated confidence of prediction results in a confidence error of ∼0.13, suggesting that the confidence calibration on calcification detection could provide a more trustworthy result. Conclusions Given the rapid detection and effective calibration of the proposed work, we expect that it can assist in clinical evaluation of treating the CAD during the imaging-guided procedure.
Learnable Spatio-Temporal Map Embeddings for Deep Inertial Localization

Indoor localization systems often fuse inertial odometry with map information via hand-defined methods to reduce odometry drift, but such methods are sensitive to noise and struggle to generalize across odometry sources. To address the robustness problem in map utilization, we propose a data-driven prior on possible user locations in a map by combining learned spatial map embeddings and temporal odometry embeddings. Our prior learns to encode which map regions are feasible locations for a user more accurately than previous hand-defined methods. This prior leads to a 49% improvement in inertial-only localization accuracy when used in a particle filter. This result is significant, as it shows that our relative positioning method can match the performance of absolute positioning using bluetooth beacons. To show the gen-eralizability of our method, we also show similar improvements using wheel encoder odometry. Our code will be made publicly available†1project page: https://rebrand.ly/learned-map-prior.
Efficient Contextual Bandits with Knapsacks via Regression

We consider contextual bandits with knapsacks ( CBwK ), a variant of the contextual bandit which places global constraints on budget consumption. We present a new algorithm that is simple, statistically optimal, and computationally eﬃcient. Our algorithm combines LagrangeBwK [19], a Lagrangian-based technique for CBwK
A new long distance contribution to $B^\pm \to K^\pm / \pi^\pm \ell^+\ell^-$ decays

We have identified a missing long-distance (one-photon exchange) contribution to the $B^\pm\to K^\pm/\pi^\pm\ell^+\ell^-$ decays. Although it does not help to explain the anomaly measured by the LHCb Coll. in the ratio $R_K = \mathcal{B}(B^\pm\to K^\pm\mu^+\mu^-)/\mathcal{B}(B^\pm\to K^\pm e^+e^-)\sim0.75$ (2.6 $\sigma$ deviation with respect to $R_K=1+\mathcal{O}(10^{-4})$ in the Standard Model), it provides a sizable contribution to the branching ratio of the $B^\pm\to \pi^\pm\ell^+\ell^-$ decays. The new decay mechanism gives rise to a measurable CP asymmetry, which is of order 1\% in both channels. These predictions can be tested in forthcoming LHCb measurements.
Alternating minimization algorithm with initialization analysis for r-local and k-sparse unlabeled sensing

The unlabeled sensing problem is to recover an unknown signal from permuted linear measurements. We propose an alternating minimization algorithm with a suitable initialization for the widely considered k-sparse permutation model. Assuming either a Gaussian measurement matrix or a sub-Gaussian signal, we upper bound the initialization error for the r-local and k-sparse permutation models in terms of the block size $r$ and the number of shuffles k, respectively. Our algorithm is computationally scalable and, compared to baseline methods, achieves superior performance on real and synthetic datasets.
Efficient Distributed DNNs in the Mobile-Edge-Cloud Continuum

In the mobile-edge-cloud continuum, a plethora of heterogeneous data sources and computation-capable nodes are available. Such nodes can cooperate to perform a distributed learning task, aided by a learning controller (often located at the network edge). The controller is required to make decisions concerning (i) data selection, i.e., which data sources to use; (ii) model selection, i.e., which machine learning model to adopt, and (iii) matching between the layers of the model and the available physical nodes. All these decisions influence each other, to a significant extent and often in counter-intuitive ways. In this paper, we formulate a problem addressing all of the above aspects and present a solution concept called RightTrain, aiming at making the aforementioned decisions in a joint manner, minimizing energy consumption subject to learning quality and latency constraints. RightTrain leverages an expanded-graph representation of the system and a delay-aware Steiner tree to obtain a provably near-optimal solution while keeping the time complexity low. Specifically, it runs in polynomial time and its decisions exhibit a competitive ratio of $2(1+\epsilon)$ , outperforming state-of-the-art solutions by over 50%. Our approach is also validated through a real-world implementation.
A note on power allocation for optimal capacity

—The problems of determining the optimal power allocation, within maximum power bounds, to (i) maximize the minimum Shannon capacity, and (ii) minimize the weighted latency are considered. In the ﬁrst case, the global optima can be achieved in polynomial time by solving a sequence of linear programs (LP). In the second case, the original non-convex problem is replaced by a convex surrogate (a geometric program), using a functional approximation. Since the approximation error is relatively low, the optima of the surrogate is close to the global optimal point of the original problem. In either cases, there is no assumption on the SINR range. The use of LPs and geometric programming make the proposed algorithms numerically efﬁcient. Computations are provided for corroboration. ,
Accounting for temporal variability in functional magnetic resonance imaging improves prediction of intelligence

Neuroimaging‐based prediction methods for intelligence have seen a rapid development. Among different neuroimaging modalities, prediction using functional connectivity (FC) has shown great promise. Most literature has focused on prediction using static FC, with limited investigations on the merits of such analysis compared to prediction using dynamic FC or region‐level functional magnetic resonance imaging (fMRI) times series that encode temporal variability. To account for the temporal dynamics in fMRI, we propose a bi‐directional long short‐term memory (bi‐LSTM) approach that incorporates feature selection mechanism. The proposed pipeline is implemented via an efficient algorithm and applied for predicting intelligence using region‐level time series and dynamic FC. We compare the prediction performance using different fMRI features acquired from the Adolescent Brain Cognitive Development (ABCD) study involving nearly 7000 individuals. Our detailed analysis illustrates the consistently inferior performance of static FC compared to region‐level time series or dynamic FC for single and combined rest and task fMRI experiments. The joint analysis of task and rest fMRI leads to improved intelligence prediction under all models compared to using fMRI from only one experiment. In addition, the proposed bi‐LSTM pipeline based on region‐level time series identifies several shared and differential important brain regions across fMRI experiments that drive intelligence prediction. A test–retest analysis of the selected regions shows strong reliability across cross‐validation folds. Given the large sample size of ABCD study, our results provide strong evidence that superior prediction of intelligence can be achieved by accounting for temporal variations in fMRI.
The HH34 outflow as seen in [FeII]1.64um by LBT-LUCI

Dense atomic jets from young stars copiously emit in [FeII] IR lines, which can, therefore, be used to trace the immediate environments of embedded protostars. We want to investigate the morphology of the bright [FeII] 1.64um line in the jet of the source HH34 IRS and compare it with the most commonly used optical tracer [SII]. We analyse a 1.64um narrow-band filter image obtained with the Large Binocular Telescope (LBT) LUCI instrument, which covers the HH34 jet and counterjet. A Point Spread Function (PSF) deconvolution algorithm was applied to enhance spatial resolution and make the IR image directly comparable to a [SII] HST image of the same source. The [FeII] emission is detected from both the jet, the (weak) counter-jet, and from the HH34-S and HH34-N bow shocks. The deconvolved image allows us to resolve jet knots close to about 1\arcsec from the central source. The morphology of the [FeII] emission is remarkably similar to that of the [SII] emission, and the relative positions of [FeII] and [SII] peaks are shifted according to proper motion measurements, which were previously derived from HST images. An analysis of the [FeII]/[SII] emission ratio shows that Fe gas abundance is much lower than the solar value with up to 90% of Fe depletion in the inner jet knots. This confirms previous findings on dusty jets, where shocks are not efficient enough to remove refractory species from grains.
Partial Visual-Semantic Embedding: Fashion Intelligence System with Sensitive Part-by-Part Learning

In this study, we propose a technology called the Fashion Intelligence System based on the visual-semantic embedding (VSE) model to quantify abstract and complex expressions unique to fashion, such as ''casual,'' ''adult-casual,'' and ''office-casual,'' and to support users' understanding of fashion. However, the existing VSE model does not support the situations in which the image is composed of multiple parts such as hair, tops, pants, skirts, and shoes. We propose partial VSE, which enables sensitive learning for each part of the fashion coordinates. The proposed model partially learns embedded representations. This helps retain the various existing practical functionalities and enables image-retrieval tasks in which changes are made only to the specified parts and image reordering tasks that focus on the specified parts. This was not possible with conventional models. Based on both the qualitative and quantitative evaluation experiments, we show that the proposed model is superior to conventional models without increasing the computational complexity.