Miracle-dz/UniFAR-FTUs huggingface dataset:
Decay-aware neural network for event classification in collider physics

The goal of event classification in collider physics is to distinguish signal events of interest from background events to the extent possible to search for new phenomena in nature. We propose a decay-aware neural network based on a multi-task learning technique to effectively address this event classification. The proposed model is designed to learn the domain knowledge of particle decays as an auxiliary task, which is a novel approach to improving learning efficiency in the event classification. Our experiments using simulation data confirmed that an inductive bias was successfully introduced by adding the auxiliary task, and significant improvements in the event classification were achieved compared with boosted decision tree and simple multi-layer perceptron models.
Precipitation retrieval over land and ocean with the SSM/I - Identification and characteristics of the scattering signal

Abstract The subject of this study is the identification of precipitation in warm and cold land and ocean environments from the Defense Meteorological Satellite Program's (DMSP) Special Sensor Micmwave/Imager (SSM/I). The high sensitivity of the SSM/I 85.5 GHz channels to volume scattering by precipitation, especially ice above the freezing level, is the basis for this identification. This ice scattering process causes SSM/I 85.5 GHz brightness temperatures to occasionally fall below 100 K. It is demonstrated that the polarization diversity available at 85.5 GHz from the SSM/I allows discrimination between low brightness temperatures due to surface water bodies versus those due to precipitation. An 85.5 GHz polarization corrected temperature (PCT) is formulated to isolate the precipitation effect. A PCT threshold of 255 K is suggested for the delineation of precipitation. This threshold is shown to be lower than what would generally be expected from nonprecipitating cloud water alone, yet high enough to s.
Anomalous Coherence Length of Majorana Zero Modes at Vortices in Superconducting Topological Insulators

The coherence length of two Majorana zero energy modes in a p-wave topological superconductor is inversely proportional to the superconducting order parameter. We studied the finite size effect of the Majorana zero modes at vortices in a topological insulator/superconductor heterostructure in the presence of a vortex and found that the the coherence length of the two zero energy modes at the terminals of a vortex line is independent of superconducting order parameter, and determined by the intrinsic properties of the topological insulator. This anomalous property illustrates that the superconducting topological insulator is topologically distinct, contrary to a $p$-wave topological superconductor.
EffMulti: Efficiently Modeling Complex Multimodal Interactions for Emotion Analysis

Humans are skilled in reading the interlocutor's emotion from multimodal signals, including spoken words, simultaneous speech, and facial expressions. It is still a challenge to effectively decode emotions from the complex interactions of multimodal signals. In this paper, we design three kinds of multimodal latent representations to refine the emotion analysis process and capture complex multimodal interactions from different views, including a intact three-modal integrating representation, a modality-shared representation, and three modality-individual representations. Then, a modality-semantic hierarchical fusion is proposed to reasonably incorporate these representations into a comprehensive interaction representation. The experimental results demonstrate that our EffMulti outperforms the state-of-the-art methods. The compelling performance benefits from its well-designed framework with ease of implementation, lower computing complexity, and less trainable parameters.
Mechanisms Influencing the Efficiency of Oscillating Airfoil Propulsion

A NACA0012 airfoil undergoing pitching and plunging motion at Re = 20,000-40,000 was simulated using a two-dimensional Navier-Stokes flow solver. Results were compared with experimental measurements in the literature and those from an inviscid analytical method and an unsteady panel method code. Although the peak in propulsive efficiency with Strouhal number demonstrated in the experimental results was predicted by the inviscid methods, it was found to be significantly modified by leading-edge vortex shedding and viscous drag at low Strouhal numbers. The occurrence and influence of vortex shedding is controlled by both the motion of the airfoil (amplitudes and phases of plunging and pitching) and the flapping frequency, which limits the time available for vortex formation and convection over the airfoil surface. Thus, Strouhal number alone is insufficient to characterize the efficiency of flapping-foil propulsion.
Accelerating Antimicrobial Peptide Discovery with Latent Sequence-Structure Model

Antimicrobial peptide (AMP) is a promising therapy in the treatment of broad-spectrum antibiotics and drug-resistant infections. Recently, an increasing number of researchers have been introducing deep generative models to accelerate AMP discovery. However, current studies mainly focus on sequence attributes and ignore structure information, which is important in AMP biological functions. In this paper, we propose a latent sequence-structure model for AMPs ( LSSAMP ) with multi-scale VQ-VAE to incorporate secondary structures. By sampling in the latent space, LSSAMP can simultaneously generate peptides with ideal sequence attributes and secondary structures. Experimental results show that the peptides generated by LSSAMP have a high probability of AMP, and two of the 21 candidates have been veriﬁed to have good antimicrobial activity. Our model will be released to help create high-quality AMP candidates for follow-up biological experiments and accelerate the whole AMP discovery 1 .
Exploring Workplace Behaviors through Speaking Patterns using Large-scale Multimodal Wearable Recordings: A Study of Healthcare Providers

Interpersonal spoken communication is central to human interaction and the exchange of information. Such interactive processes involve not only speech and spoken language but also non-verbal cues such as hand gestures, facial expressions, and nonverbal vocalization, that are used to express feelings and provide feedback. These multimodal communication signals carry a variety of information about the people: traits like gender and age as well as about physical and psychological states and behavior. This work uses wearable multimodal sensors to investigate interpersonal communication behaviors focusing on speaking patterns among healthcare providers with a focus on nurses. We analyze longitudinal data collected from 99 nurses in a large hospital setting over ten weeks. The results indicate that speaking pattern differences across shift schedules and working units. Moreover, results show that speaking patterns combined with physiological measures can be used to predict affect measures and life satisfaction scores. The implementation of this work can be accessed at https://github.com/usc-sail/tiles-audio-arousal.
Improving Levenberg-Marquardt Algorithm for Neural Networks

We explore the usage of the Levenberg-Marquardt (LM) algorithm for regression (non-linear least squares) and classification (generalized Gauss-Newton methods) tasks in neural networks. We compare the performance of the LM method with other popular first-order algorithms such as SGD and Adam, as well as other second-order algorithms such as L-BFGS , Hessian-Free and KFAC. We further speed up the LM method by using adaptive momentum, learning rate line search, and uphill step acceptance.
Confidence-aware Training of Smoothed Classifiers for Certified Robustness

Any classifier can be "smoothed out" under Gaussian noise to build a new classifier that is provably robust to l2-adversarial perturbations, viz., by averaging its predictions over the noise via randomized smoothing. Under the smoothed classifiers, the fundamental trade-off between accuracy and (adversarial) robustness has been well evidenced in the literature: i.e., increasing the robustness of a classifier for an input can be at the expense of decreased accuracy for some other inputs. In this paper, we propose a simple training method leveraging this trade-off to obtain robust smoothed classifiers, in particular, through a sample-wise control of robustness over the training samples. We make this control feasible by using "accuracy under Gaussian noise" as an easy-to-compute proxy of adversarial robustness for an input. Specifically, we differentiate the training objective depending on this proxy to filter out samples that are unlikely to benefit from the worst-case (adversarial) objective. Our experiments show that the proposed method, despite its simplicity, consistently exhibits improved certified robustness upon state-of-the-art training methods. Somewhat surprisingly, we find these improvements persist even for other notions of robustness, e.g., to various types of common corruptions. Code is available at https://github.com/alinlab/smoothing-catrs.
Fake it, Mix it, Segment it: Bridging the Domain Gap Between Lidar Sensors

Segmentation of lidar data is a task that provides rich, point-wise information about the environment of robots or autonomous vehicles. Currently best performing neural networks for lidar segmentation are fine-tuned to specific datasets. Switching the lidar sensor without retraining on a big set of annotated data from the new sensor creates a domain shift, which causes the network performance to drop drastically. In this work we propose a new method for lidar domain adaption, in which we use annotated panoptic lidar datasets and recreate the recorded scenes in the structure of a different lidar sensor. We narrow the domain gap to the target data by recreating panoptic data from one domain in another and mixing the generated data with parts of (pseudo) labeled target domain data. Our method improves the nuScenes to SemanticKITTI unsupervised domain adaptation performance by 15.2 mean Intersection over Union points (mIoU) and by 48.3 mIoU in our semi-supervised approach. We demonstrate a similar improvement for the SemanticKITTI to nuScenes domain adaptation by 21.8 mIoU and 51.5 mIoU, respectively. We compare our method with two state of the art approaches for semantic lidar segmentation domain adaptation with a significant improvement for unsupervised and semi-supervised domain adaptation. Furthermore we successfully apply our proposed method to two entirely unlabeled datasets of two state of the art lidar sensors Velodyne Alpha Prime and InnovizTwo, and train well performing semantic segmentation networks for both.
Dealing with observability in interaction-based Offline Runtime Verification of Distributed Systems

Interactions are formal models describing asynchronous communications within a Distributed System (DS). They can be drawn in the fashion of sequence diagrams and executed thanks to an operational semantics akin to that of process algebras. Executions of DS can be characterized by tuples of local traces (one per subsystem) called multi-traces. For a given execution, those local traces can be collected via monitoring and the resulting multi-trace can be analysed using offline Runtime Verification (RV). To that end, interactions may serve as formal references. In practice, however, not all subsystems may be observed and, without synchronising the end of monitoring on different subsystems, some events may not be observed, e.g. the reception of a message may be observed but not the corresponding emission. So as to be able to consider all such cases of partial observation, we propose an offline RV algorithm which uses removal operations to restrict the reference interaction on-the-fly, disregarding the parts concerning no longer observed subsystems. We prove the correctness of the algorithm and assess the performance of an implementation.
Real time reconstruction of the fast electron spectrum from high intensity laser plasma interaction using gamma counting technique

X-ray and gamma fluxes from the high intensity laser-plasma interaction are extremely short, well beyond temporal resolution of any detectors. If laser pulses come repetitively, the single photon counting technique allows to accumulate the photon spectra, however, its relation to the spectrum of the initial fast electron population in plasma is not straightforward. We present efficient and fast approach based on the Geant4 package that significantly reduces computer time needed to re-construct the high energy tail of electron spectrum from experimental data accounting for the pileup effect. Here, we first tabulate gamma spectrum from monoenergetic electron bunches of different energy for a given experimental setup, and then compose the simulated spectrum. To account for the pileups, we derive analytical formula to reverse the data. We also consider errors coming from the approximation of the initial electron spectrum by the sum of monoenergetic impacts, the finite range of the electron spectrum, etc. and give estimates on how to choose modelling parameters to minimize the approximation errors. Finally, we present an example of the experimental data treatment for the case of laser-solid interaction using 50 fs laser pulse with intensity above 1018 W/cm2.
Let’s Negotiate! A Survey of Negotiation Dialogue Systems

Negotiation is a crucial ability in human communication. Recently, there has been a resurgent research interest in negotiation dialogue systems, whose goal is to create intelligent agents that can assist people in resolving conflicts or reaching agreements. Although there have been many explorations into negotiation dialogue systems, a systematic review of this task has not been performed to date. We aim to fill this gap by investigating recent studies in the field of negotiation dialogue systems, and covering benchmarks, evaluations and methodologies within the literature. We also discuss potential future directions, including multi-modal, multi-party and cross-cultural negotiation scenarios. Our goal is to provide the community with a systematic overview of negotiation dialogue systems and to inspire future research.
Model-robust and efficient covariate adjustment for cluster-randomized experiments

Cluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However, the validity of model-based covariate adjustment is unclear when the working models are misspecified, leading to ambiguity of estimands and risk of bias. In this article, we first adapt two conventional model-based methods, generalized estimating equations and linear mixed models, with weighted g-computation to achieve robust inference for cluster-average and individual-average treatment effects. To further overcome the limitations of model-based covariate adjustment methods, we propose an efficient estimator for each estimand that allows for flexible covariate adjustment and additionally addresses cluster size variation dependent on treatment assignment and other cluster characteristics. Such cluster size variations often occur post-randomization and, if ignored, can lead to bias of model-based estimators. For our proposed efficient covariate-adjusted estimator, we prove that when the nuisance functions are consistently estimated by machine learning algorithms, the estimator is consistent, asymptotically normal, and efficient. When the nuisance functions are estimated via parametric working models, the estimator is triply-robust. Simulation studies and analyses of three real-world cluster-randomized experiments demonstrate that the proposed methods are superior to existing alternatives.
Coordinate Descent Methods for DC Minimization

Difference-of-Convex (DC) minimization, referring to the problem of minimizing the difference of two convex functions, has been found rich applications in statistical learning and studied extensively for decades. However, existing methods are primarily based on multi-stage convex relaxation, only leading to weak optimality of critical points. This paper proposes a coordinate descent method for minimizing a class of DC functions based on sequential nonconvex approximation. Our approach iteratively solves a nonconvex one-dimensional subproblem globally, and it is guaranteed to converge to a coordinate-wise stationary point. We prove that this new optimality condition is always stronger than the standard critical point condition and directional point condition under a mildlocally bounded nonconvexity assumption. For comparisons, we also include a naive variant of coordinate descent methods based on sequential convex approximation in our study. When the objective function satisfies a globally bounded nonconvexity assumption and Luo-Tseng error bound assumption, coordinate descent methods achieve Q-linear convergence rate. Also, for many applications of interest, we show that the nonconvex one-dimensional subproblem can be computed exactly and efficiently using a breakpoint searching method. Finally, we have conducted extensive experiments on several statistical learning tasks to show the superiority of our approach.
A Survey of Learning Criteria Going Beyond the Usual Risk

Virtually all machine learning tasks are characterized using some form of loss function, and “good performance” is typically stated in terms of a sufficiently small average loss, taken over the random draw of test data. While optimizing for performance on average is intuitive, convenient to analyze in theory, and easy to implement in practice, such a choice brings about trade-offs. In this work, we survey and introduce a wide variety of non-traditional criteria used to design and evaluate machine learning algorithms, place the classical paradigm within the proper historical context, and propose a view of learning problems which emphasizes the question of “what makes for a desirable loss distribution? ” in place of tacit use of the expected loss.
Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution

Single Image Super-Resolution (SISR) tasks have achieved significant performance with deep neural networks. However, the large number of parameters in CNN-based methods for SISR tasks require heavy computations. Although several efficient SISR models have been recently proposed, most are handcrafted and thus lack flexibility. In this work, we propose a novel differentiable Neural Architecture Search (NAS) approach on both the cell-level and network-level to search for lightweight SISR models. Specifically, the cell-level search space is designed based on an information distillation mechanism, focusing on the combinations of lightweight operations and aiming to build a more lightweight and accurate SR structure. The network-level search space is designed to consider the feature connections among the cells and aims to find which information flow benefits the cell most to boost the performance. Unlike the existing Reinforcement Learning (RL) or Evolutionary Algorithm (EA) based NAS methods for SISR tasks, our search pipeline is fully differentiable, and the lightweight SISR models can be efficiently searched on both the cell-level and network-level jointly on a single GPU. Experiments show that our methods can achieve state-of-the-art performance on the benchmark datasets in terms of PSNR, SSIM, and model complexity with merely 68G Multi-Adds for $\times 2$ and 18G Multi-Adds for $\times 4$ SR tasks.
Spatial properties of large-area crossed-strip CZT detectors

We present results of detailed characterization of the spatial properties of large area crossed strip CZT detectors now under development at CASS/UCSD for use in coded aperture astrophysical hard X-ray instrumentation. We address the volumetric uniformity of spectral response for these detectors as determined by charge sharing and loss, diffusion, and electron trapping. Results are presented for our prototype detector having 500 μm pitch and collecting area 32×32 mm2; however, we also explore these characteristics as they effect performance of larger and smaller pitch detectors of similar design.
Left Atrial Mechanical Dysfunction and the Risk for Ischemic Stroke in People Without Prevalent Atrial Fibrillation or Stroke

BACKGROUND Atrial myopathy-characterized by changes in left atrial function and size-may precede and promote atrial fibrillation (AF) and cardiac thromboembolism. In people without prior AF or stroke, whether analysis of left atrial function and size can improve ischemic stroke prediction is unknown. OBJECTIVE To evaluate the association of echocardiographic left atrial function (reservoir, conduit, and contractile strain) and left atrial size (left atrial volume index) with ischemic stroke and determine whether these measures can improve the stroke prediction achieved by CHA2DS2-VASc score variables. DESIGN Prospective cohort study. SETTING ARIC (Atherosclerosis Risk in Communities) study. PARTICIPANTS 4917 ARIC participants without prevalent stroke or AF. MEASUREMENTS Ischemic stroke events (2011 to 2019) were adjudicated by physicians. Left atrial strain was measured using speckle-tracking echocardiography. RESULTS Over 5 years, the cumulative incidences of ischemic stroke in the lowest quintiles of left atrial reservoir, conduit, and contractile strain were 2.99% (95% CI, 1.89% to 4.09%), 3.18% (CI, 2.14% to 4.22%), and 2.15% (CI, 1.09% to 3.21%), respectively, and that of severe left atrial enlargement was 1.99% (CI, 0.23% to 3.75%). On the basis of the Akaike information criterion, left atrial reservoir strain plus CHA2DS2-VASc variables was the best predictive model. With the addition of left atrial reservoir strain to CHA2DS2-VASc variables, 11.6% of the 112 participants with stroke after 5 years were reclassified to higher risk categories and 1.8% to lower risk categories. Among the 4805 participants who did not develop stroke, 12.2% were reclassified to lower and 12.7% to higher risk categories. Decision curve analysis showed a predicted net benefit of 1.34 per 1000 people at a 5-year risk threshold of 5%. LIMITATION Underascertainment of subclinical AF. CONCLUSION In people without prior AF or stroke, when added to CHA2DS2-VASc variables, left atrial reservoir strain improves stroke prediction and yields a predicted net benefit, as shown by decision curve analysis. PRIMARY FUNDING SOURCE National Heart, Lung, and Blood Institute of the National Institutes of Health.
Calcium Phosphate Nanoparticles as Carriers of Therapeutic Peptides

Calcium phosphate is a natural biomineral and the major inorganic constituent of bones and teeth. Therefore, synthetic calcium phosphates that mimic the biogenic ones possess excellent biocompatibility as well as biodegradability and are promising materials for medicine. Due to their unique physiochemical properties, calcium phosphate nanoparticles (CaP NPs) are extensively exploited in nanomedicine as carriers of biomolecules, including peptides, proteins, and nucleic acids. In this regard, peptides are of particular interest as they are exceptionally selective and efficacious for the treatment of a broad range of diseases. Among various peptides for biomedical applications, cardio-specific peptides are particularly interesting since they represent a valuable alternative to conventional treatments. Moreover, they can contribute to overcome important clinical limitations, including drug resistance and non-specific biodistribution of traditional drug products. In this work, we have investigated the loading of a therapeutic mimetic peptide, which was previously shown to improve myocardial contraction and results in the restoration of cardiac function. Peptide-loaded CaP NPs were prepared by exploiting a biomineralization approach, by using a mineralizing solution containing Ca2+, Mg2+, and PO43- ions. Several experimental conditions were tested by varying the reaction time, as well as the drug concentration. Colloidal stability, morphology, size, as well as drug loading were evaluated to identify the best candidate to be tested in vitro in the future.