Miracle-dz/UniFAR-FTUs huggingface dataset:
Report of the Topical Group on Cosmic Frontier 5 Dark Energy and Cosmic Acceleration: Cosmic Dawn and Before for Snowmass 2021

This report summarizes the envisioned research activities as gathered from the Snowmass 2021 CF5 working group concerning Dark Energy and Cosmic Acceleration: Cosmic Dawn and Before. The scientific goals are to study inflation and to search for new physics through precision measurements of relic radiation from the early universe. The envisioned research activities for this decade (2025–35) are constructing and operating major facilities and developing critical enabling capabilities. The major facilities for this decade are the CMB-S4 project, a new Stage-V spectroscopic survey facility, and existing gravitational wave observatories. Enabling capabilities include aligning and investing in theory, computation and model building, and investing in new technologies needed for early universe studies in the following decade (2035+).
Improving the Performance of DNN-based Software Services using Automated Layer Caching

Deep Neural Networks (DNNs) have become an essential component in many application domains including web-based services. A variety of these services require high throughput and (close to) real-time features, for instance, to respond or react to users' requests or to process a stream of incoming data on time. However, the trend in DNN design is toward larger models with many layers and parameters to achieve more accurate results. Although these models are often pre-trained, the computational complexity in such large models can still be relatively significant, hindering low inference latency. Implementing a caching mechanism is a typical systems engineering solution for speeding up a service response time. However, traditional caching is often not suitable for DNN-based services. In this paper, we propose an end-to-end automated solution to improve the performance of DNN-based services in terms of their computational complexity and inference latency. Our caching method adopts the ideas of self-distillation of DNN models and early exits. The proposed solution is an automated online layer caching mechanism that allows early exiting of a large model during inference time if the cache model in one of the early exits is confident enough for final prediction. One of the main contributions of this paper is that we have implemented the idea as an online caching, meaning that the cache models do not need access to training data and perform solely based on the incoming data at run-time, making it suitable for applications using pre-trained models. Our experiments results on two downstream tasks (face and object classification) show that, on average, caching can reduce the computational complexity of those services up to 58\% (in terms of FLOPs count) and improve their inference latency up to 46\% with low to zero reduction in accuracy.
Two-field mixed hp-finite elements for time-dependent problems in the refined theories of thermodynamics

Modern manufacturing technologies allow heterogeneous materials with complex inner structures (e.g., foams) to be easily produced. However, their utilization is not straightforward, as the classical constitutive laws are not necessarily valid. According to various experimental observations, the Guyer–Krumhansl equation is a promising candidate for modeling such complex structures. However, practical applications need a reliable and efficient algorithm capable of handling both complex geometries and advanced heat equations. In the present paper, we derive new two-field variational formulations which treat the temperature and the heat flux as independent field variables, and we develop new, advanced hp-type mixed finite element methods, which can be reliably applied. We investigate their convergence properties for various situations, challenging in relation to stability and the treatment of fast propagation speeds. That algorithm is also proved to be outstandingly efficient, providing solutions four magnitudes faster than commercial algorithms.
langcc: A Next-Generation Compiler Compiler

Traditionally, parsing has been a laborious and error-prone component of compiler development, and most parsers for full industrial programming languages are still written by hand. The author [Zim22] shows that automatic parser generation can be practical, via a number of new innovations upon the standard LR paradigm of Knuth et al. With this methodology, we can automatically generate efficient parsers for virtually all languages that are intuitively"easy to parse". This includes Golang 1.17. 8 and Python 3.9. 12, for which our generated parsers are, respectively, 1.2x and 4.3x faster than the standard parsers. This document is a companion technical report which describes the software implementation of that work, which is available open-source at https://github.com/jzimmerman/langcc.
Robust leave-one-out cross-validation for high-dimensional Bayesian models

Leave-one-out cross-validation (LOO-CV) is a popular method for estimating out-of-sample predictive accuracy. However, computing LOO-CV criteria can be computationally expensive due to the need to fit the model multiple times. In the Bayesian context, importance sampling provides a possible solution but classical approaches can easily produce estimators whose asymptotic variance is infinite, making them potentially unreliable. Here we propose and analyze a novel mixture estimator to compute Bayesian LOO-CV criteria. Our method retains the simplicity and computational convenience of classical approaches, while guaranteeing finite asymptotic variance of the resulting estimators. Both theoretical and numerical results are provided to illustrate the improved robustness and efficiency. The computational benefits are particularly significant in high-dimensional problems, allowing to perform Bayesian LOO-CV for a broader range of models, and datasets with highly influential observations. The proposed methodology is easily implementable in standard probabilistic programming software and has a computational cost roughly equivalent to fitting the original model once.
TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation

Wearable sensor-based human activity recognition (HAR) has emerged as a principal research area and is utilized in a variety of applications. Recently, deep learning-based methods have achieved significant improvement in the HAR field with the development of human-computer interaction applications. However, they are limited to operating in a local neighborhood in the process of a standard convolution neural network, and correlations between different sensors on body positions are ignored. In addition, they still face significant challenging problems with performance degradation due to large gaps in the distribution of training and test data, and behavioral differences between subjects. In this work, we propose a novel Transformer-based Adversarial learning framework for human activity recognition using wearable sensors via Self-KnowledgE Distillation (TASKED), that accounts for individual sensor orientations and spatial and temporal features. The proposed method is capable of learning cross-domain embedding feature representations from multiple subjects datasets using adversarial learning and the maximum mean discrepancy (MMD) regularization to align the data distribution over multiple domains. In the proposed method, we adopt the teacher-free self-knowledge distillation to improve the stability of the training procedure and the performance of human activity recognition. Experimental results show that TASKED not only outperforms state-of-the-art methods on the four real-world public HAR datasets (alone or combined) but also improves the subject generalization effectively.
Lengths are coordinates for convex structures

Suppose that N is a geometrically finite orientable hyperbolic 3-manifold. Let P(N,C) be the space of all geometrically finite hyperbolic structures on N whose convex core is bent along a set C of simple closed curves. We prove that the map which associates to each structure in P(N,C) the lengths of the curves in the bending locus C is one-to-one. If C is maximal, the traces of the curves in C are local parameters for the representation space R(N).
OPR-Miner: Order-Preserving Rule Mining for Time Series

Discovering frequent trends in time series is a critical task in data mining. Recently, order-preserving matching was proposed to find all occurrences of a pattern in a time series, where the pattern is a relative order (regarded as a trend) and an occurrence is a sub-time series whose relative order coincides with the pattern. Inspired by the order-preserving matching, the existing order-preserving pattern (OPP) mining algorithm employs order-preserving matching to calculate the support, which leads to low efficiency. To address this deficiency, this paper proposes an algorithm called efficient frequent OPP miner (EFO-Miner) to find all frequent OPPs. EFO-Miner is composed of four parts: a pattern fusion strategy to generate candidate patterns, a matching process for the results of sub-patterns to calculate the support of super-patterns, a screening strategy to dynamically reduce the size of prefix and suffix arrays, and a pruning strategy to further dynamically prune candidate patterns. Moreover, this paper explores the order-preserving rule (OPR) mining and proposes an algorithm called OPR-Miner to discover strong rules from all frequent OPPs using EFO-Miner. Experimental results verify that OPR-Miner gives better performance than other competitive algorithms. More importantly, clustering and classification experiments further validate that OPR-Miner achieves good performance.
Allocation Schemes in Analytic Evaluation: Applicant-Centric Holistic or Attribute-Centric Segmented?

Many applications such as hiring and university admissions involve evaluation and selection of applicants. These tasks are fundamentally difficult, and require combining evidence from multiple different aspects (what we term "attributes"). In these applications, the number of applicants is often large, and a common practice is to assign the task to multiple evaluators in a distributed fashion. Specifically, in the often-used holistic allocation, each evaluator is assigned a subset of the applicants, and is asked to assess all relevant information for their assigned applicants. However, such an evaluation process is subject to issues such as miscalibration (evaluators see only a small fraction of the applicants and may not get a good sense of relative quality), and discrimination (evaluators are influenced by irrelevant information about the applicants). We identify that such attribute-based evaluation allows alternative allocation schemes. Specifically, we consider assigning each evaluator more applicants but fewer attributes per applicant, termed segmented allocation. We compare segmented allocation to holistic allocation on several dimensions via theoretical and experimental methods. We establish various tradeoffs between these two approaches, and identify conditions under which one approach results in more accurate evaluation than the other.
Effective horizons for quantum communication in a Schwarzschild spacetime

Communication between a free-falling observer and an observer hovering above the Schwarzschild horizon of a black hole suffers from Unruh-Hawking noise, which degrades communication channel capacities. Ignoring time dilation, which affects all channels equally, we show that for bosonic communication using single and dual rail encoding the classical channel capacity reaches a finite value and the quantum channel capacity falls off exponentially. The latter defines an effective horizon, beyond which quantum communication becomes exponentially resource inefficient. The characteristic length scale associated with this quantum horizon depends on the mass of the black hole and the frequency of the communication channel.
Deep Labeling of fMRI Brain Networks Using Cloud Based Processing

Resting state fMRI is an imaging modality which reveals brain activity localization through signal changes, in what is known as Resting State Networks (RSNs). This technique is gaining popularity in neurosurgical pre-planning to visualize the functional regions and assess regional activity. Labeling of rs-fMRI networks require subject-matter expertise and is time consuming, creating a need for an automated classification algorithm. While the impact of AI in medical diagnosis has shown great progress; deploying and maintaining these in a clinical setting is an unmet need. We propose an end-to-end reproducible pipeline which incorporates image processing of rs-fMRI in a cloud-based workflow while using deep learning to automate the classification of RSNs. We have architected a reproducible Azure Machine Learning cloud-based medical imaging concept pipeline for fMRI analysis integrating the popular FMRIB Software Library (FSL) toolkit. To demonstrate a clinical application using a large dataset, we compare three neural network architectures for classification of deeper RSNs derived from processed rs-fMRI. The three algorithms are: an MLP, a 2D projection-based CNN, and a fully 3D CNN classification networks. Each of the net-works was trained on the rs-fMRI back-projected independent components giving>98% accuracy for each classification method.
Superconvergence of DPG approximations in linear elasticity

Existing a priori convergence results of the discontinuous Petrov-Galerkin method to solve the problem of linear elasticity are improved. Using duality arguments, we show that higher convergence rates for the displacement can be obtained. Post-processing techniques are introduced in order to prove superconvergence and numerical experiments validates our theory.
On the representability of ${\Cal Hilb}^nk[x]_{(x)}$

Let $k[x]_{(x)}$ be the polynomial ring $k[x]$ localized in the maximal ideal $(x)\subseteq k[x]$. We study the Hilbert functor parameterizing ideals of colength $n$ in this ring {\it having support at the origin}. The main result of this article is that this functor is not representable. We also give a complete description of the functor as a limit of representable functors.
THE WEAK CLOSURE OF THE SET OF LEFT TRANSLATION OPERATORS

It is known that for an amenable locally compact group G, 0 is not in the weak closure of {A(g): g E G} of VN(G). In this paper, it is proved that the converse of this is true. In other words, if G is a non-amenable locally compact group, then 0 is in the weak closure of {A(g): g E G}. This answers several questions of Ulger. Applications to the algebra C,; (G) and the dual of the reduced group C*-algebra are obtained.
An analytical blockage correction model for high-solidity turbines

Abstract A significant challenge in the experimental or computational characterisation of porous bodies and wind turbines is the correction of the obtained flow quantities for wall interference effects. Conventional corrective models are based on the Rankine–Froude theory, which is valid when the body solidity, or turbine induction factor, is sufficiently low. To resolve this issue, this work presents a new corrective model that builds on an extension of the Rankine–Froude theory, valid at arbitrary solidities, coupled with the method of mirror images to account for the existence of channel walls. The predictions of the new model are validated using laboratory and numerical experiments of porous plates and wind turbines. The results show that the new model performs equally as well as conventional ones when the solidity is low, but becomes increasingly more accurate as the latter grows.
Remote sensing road segmentation and breakpoint repair based on deep learning

The current high-resolution remote sensing image road extraction methods mostly use an end-to-end network model to predict road, which is helpful to the global feature expression of road extraction, but ignores local feature express. Therefore, in order to better balance the global information and local information extracted by the road, in this paper, we use SEResNext to extract rich semantic information from low to high channel to construct the encoder, and use the global mixed feature module (GMFM) to upsample and recover to the original image size to construct the decoder, in which dense skip connections promote semantic information fusion. In addition, we use polynomial fitting and morphological-based methods to repair road breakpoints. Experiments show that compared with similar networks, the accuracy and completeness of road extraction in this paper have been significantly improved, the precision achieves 88.765%, the recall is 84.224%, and the achieves 86.435%.
Forecasts of Ecological Time Series based on Vector Similarity S-Map

S-map is a method based on state space reconstruction provided an efficient method to infer a proxy for the local effect of species interactions and to make reliable forecasts from nonlinear time series, which uses Euclidean distances in the reconstructed state space to determine nearest neighbor points and corresponding weight values. The distance in the state space measures the similarity of the states of two points in the space, which is essential for predicting the evolution of similar states. If the distance measurement is not accurate, it will be problematic to determine adjacent points that correctly reflect the similarity. To solve this problem, in this paper, we propose a S-map that uses vector similarity to select neighboring points and compute weights. The proposed method is first validated on a simulated dataset generated from 12 resource competition models. To further validate our method, we used vector similarity-based S-map for the Time series of sockeye salmon returns from the Fraser River in British Columbia, Canada.
What Is the Role of 2-Pore Domain Potassium Channels (K2P) in Pain?

Nociceptive dorsal root ganglion (DRG) and trigeminal neurons express a large numbers of ion channels that determine their threshold and responses to both noxious and innocuous stimuli.1 Several voltage-gated sodium channels (Nav),2 voltage-gated calcium (Ca2+) channels,3 transient receptor potential (TRP) channels,4 and other cation channels increase nociceptor responsiveness, whereas a variety of potassium (K+) channels prevent spontaneous nociceptor activity and reduce their firing probability.5-9 The imbalance between these 2 opposing influences, which may occur as a consequence of channel gene sequence variants,10 effect of inflammatory mediators,11 or transcriptional dysregulation,12,13 promotes neuropathic pain, hyperalgesia, and allodynia.14 Given their importance in pain modulation, voltage-gated K+ channels (Kv) and 2-pore domain K+ channels (K2P) are potential targets for designing novel analgesic compounds.6,14-17 This review will focus on K2P channels, which are multimodal sensors that may have an important role in several pain disorders, including neuropathic pain and migraine6,15-20
OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation

As deep learning methods continue to improve medical image segmentation performance, data annotation is still a big bottleneck due to the labor-intensive and time-consuming burden on medical experts, especially for 3D images. To significantly reduce annotation efforts while attaining competitive segmentation accuracy, we propose a self-learning and one-shot learning based framework for 3D medical image segmentation by annotating only one slice of each 3D image. Our approach takes two steps: (1) self-learning of a reconstruction network to learn semantic correspondence among 2D slices within 3D images, and (2) representative selection of single slices for one-shot manual annotation and propagating the annotated data with the well-trained reconstruction network. Extensive experiments verify that our new framework achieves comparable performance with less than 1% annotated data compared with fully supervised methods and generalizes well on several out-of-distribution testing sets.
Extraction of subimages by lifting wavelet filters

This paper proposes a method for extracting subimages from a huge reference image by learning lifting wavelet filters. Lifting wavelet filters are biorthogonal wavelet filters containing free parameters developed by Sweldens. Our method is to learn such free parameters using some training subimages so as to vanish their high frequency components in the yand xdirections. The learnt wavelet filters have the feature of training subimages. Applying such wavelet filters to the reference image, we can detect the locations where the high frequency components are almost the same as those of the target subimage. key words: extraction, subimage, learning, lifting wavelet lter
On Mirror Formulas in Open and Closed Gromov-Witten Theory

We obtain mirror formulas for twisted genus 0 two-point Gromov-Witten (GW) invariants of projective spaces and for the genus 0 two-point GW-invariants of Fano and Calabi-Yau complete intersections. This extends previous results for projective hypersurfaces, following the same approach, but with greater detail in the Fano case. The Calabi-Yau case is used to study a conjecture concerning open GW-invariants at the end of this paper and in a separate paper to obtain mirror formulas for the genus 1 GW-invariants of projective complete intersections.
Handbook of micrometeorology : a guide for surface flux measurement and analysis

Contributing authors. - Preface. - 1. Introduction. - 2. Averaging, detrending and filtering of eddy covariance time series. - 3. Coordinate systems and flux bias error. - 4. Uncertainty in eddy covariance flux estimates resulting from spectral attenuation. - 5. Low frequency atmospheric transport and surface flux measurements. - 6. Measurements of trace gas fluxes in the atmosphere using eddy covariance: WPL corrections revisited. - 7. Concerning the measurement of atmospheric trace gas fluxes with open- and closed-path eddy covariance system: The WPL terms and spectral attenuation. - 8. Stationarity, homogeneity, and ergodicity in canopy turbulence. - 9. Post-field data quality control. - 10. Advection and modeling. Index
Bias in Implicit Measures as Instances of Biased Behavior under Suboptimal Conditions in the Laboratory

The target paper of Gawronski, Ledgerwood, and Eastwick (this issue) provides a valuable contribution to the literature on implicit bias (IB). We find ourselves in agreement with many of the points that the authors put forward. Most importantly, we agree that it is important to realize that scores on implicit measurement tasks such as the Implicit Association Test (IAT) cannot by default be interpreted as instances of unconscious bias. We also agree that the focus on bias in implicit measures (BIM) may have slowed progress in research on IB, that the focus of bias research should be on reducing real-world instances of bias, and that societal dispar-ities can result in social discrimination in a way that is not captured by the psychological concept of bias. We are happy to see that Gawronski et al. share many aspects of our perspective on IB and implicit measures (see De Houwer, 2006, 2014, 2019; De Houwer, Teige-Mocigemba, Spruyt, & Moors, 2009, De Houwer, Gawronski, & Barnes-Holmes, 2013; De Houwer, Van Dessel, & Moran, 2021). Most importantly, (a) IB can indeed be conceived of as a behavioral phenomenon that refers to the impact of social cues on behavior, and (b) implicit measures are not the same as indirect measures, nor do they necessarily reflect associative processes. In sum, we support much of what Gawronski et al. put forward in their target paper. Nevertheless, we also disagree with Gawronski et al. (this issue) on some points. First, we continue to believe IB should not be limited to unconscious bias but should include also instances of bias that are automatic in other ways (e.g., unintentional). Second, we continue to see a potential role for BIM in research on IB, more specifically as an educational tool and as a lab model of IB in the real world.