Miracle-dz/UniFAR-FTUs huggingface dataset:
Application and modeling of an online distillation method to reduce krypton and argon in XENON1T

A novel online distillation technique was developed for the XENON1T dark matter experiment to reduce intrinsic background components more volatile than xenon, such as krypton or argon, while the detector was operating. The method is based on a continuous purification of the gaseous volume of the detector system using the XENON1T cryogenic distillation column. A krypton-in-xenon concentration of $(360 \pm 60)$ ppq was achieved. It is the lowest concentration measured in the fiducial volume of an operating dark matter detector to date. A model was developed and fit to the data to describe the krypton evolution in the liquid and gas volumes of the detector system for several operation modes over the time span of 550 days, including the commissioning and science runs of XENON1T. The online distillation was also successfully applied to remove Ar-37 after its injection for a low energy calibration in XENON1T. This makes the usage of Ar-37 as a regular calibration source possible in the future. The online distillation can be applied to next-generation experiments to remove krypton prior to, or during, any science run. The model developed here allows further optimization of the distillation strategy for future large scale detectors.
Setting Boundaries for Statistical Mechanics

Statistical mechanics has grown without bounds in space. Statistical mechanics of noninteracting point particles in an unbounded perfect gas is widely used to describe liquids like concentrated salt solutions of life and electrochemical technology, including batteries. Liquids are filled with interacting molecules. A perfect gas is a poor model of a liquid. Statistical mechanics without spatial bounds is impossible as well as imperfect, if molecules interact as charged particles, as nearly all atoms do. The behavior of charged particles is not defined until boundary structures and values are defined because charges are governed by Maxwell’s partial differential equations. Partial differential equations require boundary structures and conditions. Boundary conditions cannot be defined uniquely ‘at infinity’ because the limiting process that defines ‘infinity’ includes such a wide variety of structures and behaviors, from elongated ellipses to circles, from light waves that never decay, to dipolar fields that decay steeply, to Coulomb fields that hardly decay at all. Boundaries and boundary conditions needed to describe matter are not prominent in classical statistical mechanics. Statistical mechanics of bounded systems is described in the EnVarA system of variational mechanics developed by Chun Liu, more than anyone else. EnVarA treatment does not yet include Maxwell equations.
ML4CO: Is GCNN All You Need? Graph Convolutional Neural Networks Produce Strong Baselines For Combinatorial Optimization Problems, If Tuned and Trained Properly, on Appropriate Data

The 2021 NeurIPS Machine Learning for Combinatorial Optimization (ML4CO) competition was designed with the goal of improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning models. The competition's main scientific question was the following: is machine learning a viable option for improving traditional combinatorial optimization solvers on specific problem distributions, when historical data is available? This was motivated by the fact that in many practical scenarios, the data changes only slightly between the repetitions of a combinatorial optimization problem, and this is an area where machine learning models are particularly powerful at. This paper summarizes the solution and lessons learned by the Huawei EI-OROAS team in the dual task of the competition. The submission of our team achieved the second place in the final ranking, with a very close distance to the first spot. In addition, our solution was ranked first consistently for several weekly leaderboard updates before the final evaluation. We provide insights gained from a large number of experiments, and argue that a simple Graph Convolutional Neural Network (GCNNs) can achieve state-of-the-art results if trained and tuned properly.
Dynamic Page Placement on Real Persistent Memory Systems

As persistent memory (PM) technologies emerge, hybrid memory architectures combining DRAM with PM bring the potential to provide a tiered, byte-addressable main memory of unprecedented capacity. Nearly a decade after the first proposals for these hybrid architectures, the real technology has finally reached commercial availability with Intel Optane(TM) DC Persistent Memory (DCPMM). This raises the challenge of designing systems that realize this potential in practice, namely through effective approaches that dynamically decide at which memory tier should pages be placed. In this paper, we are the first, to our knowledge, to systematically analyze tiered page placement on real DCPMM-based systems. To this end, we start by revisiting the assumptions of state-of-the-art proposals, and confronting them with the idiosyncrasies of today's off-the-shelf DCPMM-equipped architectures. This empirical study reveals that some of the key design choices in the literature rely on important assumptions that are not verified in present-day DRAM-DCPMM memory architectures. Based on the lessons from this study, we design and implement HyPlacer, a tool for tiered page placement in off-the-shelf Linux-based systems equipped with DRAM+DCPMM. In contrast to previous proposals, HyPlacer follows an approach guided by two main practicality principles: 1) it is tailored to the performance idiosyncrasies of off-theshelf DRAM+DCPMM systems; and 2) it can be seamlessly integrated into Linux with minimal kernel-mode components, while ensuring extensibility to other HMAs and other data placement policies. Our experimental evaluation of HyPlacer shows that it outperforms both solutions proposed in past literature and placement options that are currently available in off-the-shelf DCPMM-equipped Linux systems, reaching an improvement of up to 11x when compared to the default memory policy in Linux.
Numerical study of acoustic cell trapping above elastic membrane disks driven in higher-harmonic modes by thin-film transducers with patterned electrodes

Excitations of MHz acoustic modes are studied numerically in 10-μm-thick silicon disk membranes with a radius of 100 and 500 μm actuated by an attached 1-μm-thick (AlSc)N thin-film transducer. It is shown how higher-harmonic membrane modes can be excited selectively and efficiently by appropriate patterning of the transducer electrodes. When filling the half-space above the membrane with a liquid, the higher-harmonic modes induce acoustic pressure fields in the liquid with interference patterns that result in the formation of a single, strong trapping region located 50 100 μm above the membrane, where a single suspended cell can be trapped in all three spatial directions. The trapping strength depends on the acoustic contrast between the cell and the liquid, and as a specific example it is shown by numerical simulation that by using a 60% iodixanol solution, a cancer cell can be held in the trap.
ProBF: Learning Probabilistic Safety Certificates with Barrier Functions

Safety-critical applications require controllers/policies that can guarantee safety with high confidence. The control barrier function is a useful tool to guarantee safety if we have access to the ground-truth system dynamics. In practice, we have inaccurate knowledge of the system dynamics, which can lead to unsafe behaviors due to unmodeled residual dynamics. Learning the residual dynamics with deterministic machine learning models can prevent the unsafe behavior but can fail when the predictions are imperfect. In this situation, a probabilistic learning method that reasons about the uncertainty of its predictions can help provide robust safety margins. In this work, we use a Gaussian process to model the projection of the residual dynamics onto a control barrier function. We propose a novel optimization procedure to generate safe controls that can guarantee safety with high probability. The safety filter is provided with the ability to reason about the uncertainty of the predictions from the GP. We show the efficacy of this method through experiments on Segway and Quadrotor simulations. Our proposed probabilistic approach is able to reduce the number of safety violations significantly as compared to the deterministic approach with a neural network.
Learning with distributional inverters

We generalize the"indirect learning"technique of Furst et. al., 1991 to reduce from learning a concept class over a samplable distribution $\mu$ to learning the same concept class over the uniform distribution. The reduction succeeds when the sampler for $\mu$ is both contained in the target concept class and efficiently invertible in the sense of Impagliazzo&Luby, 1989. We give two applications. - We show that AC0[q] is learnable over any succinctly-described product distribution. AC0[q] is the class of constant-depth Boolean circuits of polynomial size with AND, OR, NOT, and counting modulo $q$ gates of unbounded fanins. Our algorithm runs in randomized quasi-polynomial time and uses membership queries. - If there is a strongly useful natural property in the sense of Razborov&Rudich 1997 -- an efficient algorithm that can distinguish between random strings and strings of non-trivial circuit complexity -- then general polynomial-sized Boolean circuits are learnable over any efficiently samplable distribution in randomized polynomial time, given membership queries to the target function
MC-DGCNN: A Novel DNN Architecture for Multi-Category Point Set Classification

Point set classiﬁcation aims to build a representation learning model that distinguishes between spatial and categorical conﬁgurations of point set data. This problem is soci-etally important since in many applications domains such as immunology, and microbial ecology. This problem is challenging since the interactions between diﬀerent categories of points are not always equal; as a result, the representation learning model must selectively learn the most relevant multi-categorical relationships. The related works are limited (1) in learning the importance of diﬀerent multi-categorical relationships, especially for high-order interactions, and (2) do not fully exploit the spatial distribution of points beyond simply measuring relative distance or applying a feed-forward neural network to coordinates. To overcome these limitations, we leverage the dynamic graph convolutional neural network (DGCNN) architecture to design a novel multi-category DGCNN (MC-DGCNN), contributing location representation and point pair attention layers for multi-categorical point set classiﬁcation. MC-DGCNN has the ability to identify the categorical importance of each point pair and extends this to N-way spatial relationships, while still preserving all the properties and beneﬁts of DGCNN (e.g., diﬀerentiability). Experimental results show that the proposed architecture is computationally eﬃcient and signiﬁcantly outperforms current deep learning architectures on real-world datasets.
S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation

Emotion recognition in conversation (ERC) has attracted much attention in recent years for its necessity in widespread applications. With the development of graph neural network (GNN), recent state-of-the-art ERC models mostly use GNN to embed the intrinsic structure information of a conversation into the utterance features. In this paper, we propose a novel GNN-based model for ERC, namely S+PAGE, to better capture the speaker and position-aware conversation structure information. Specifically, we add the relative positional encoding and speaker dependency encoding in the representations of edge weights and edge types respectively to acquire a more reasonable aggregation algorithm for ERC. Besides, a two-stream conversational Transformer is presented to extract both the self and inter-speaker contextual features for each utterance. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison, whose results demonstrate the superiority of our model.
Improving the Efficiency of Off-Policy Reinforcement Learning by Accounting for Past Decisions

Off-policy learning from multistep returns is crucial for sample-efficient reinforcement learning, particularly in the experience replay setting now commonly used with deep neural networks. Classically, off-policy estimation bias is corrected in a per-decision manner: past temporal-difference errors are reweighted by the instantaneous Importance Sampling (IS) ratio (via eligibility traces) after each action. Many important off-policy algorithms such as Tree Backup and Retrace rely on this mechanism along with differing protocols for truncating (“cutting”) the ratios (“traces”) to counteract the excessive variance of the IS estimator. Unfortunately, cutting traces on a per-decision basis is not necessarily efficient; once a trace has been cut according to local information, the effect cannot be reversed later, potentially resulting in the premature truncation of estimated returns and slower learning. In the interest of motivating efficient off-policy algorithms, we propose a multistep operator that permits arbitrary past-dependent traces. We prove that our operator is convergent for policy evaluation, and for optimal control when targeting greedy-in-the-limit policies. Our theorems establish the first convergence guarantees for many existing algorithms including Truncated IS, Non-Markov Retrace, and history-dependent TD(λ). Our theoretical results also provide guidance for the development of new algorithms that jointly consider multiple past decisions for better credit assignment and faster learning.
Towards a Richer Specification of the Exploration/Exploitation Trade-off: Hidden Knowledge-based Aspects and Empirical Results for a Set of Large R&D-Performing Firms

In this paper we describe a richer framework characterizing the trade-off between exploration and exploitation with respect firm performance. We devise a model that complements the notion of organizational learning as a process of inferential learning from the past with an explicit incorporation of a knowledge/information-related theory. Based on this and an international panel data set of large R&D performing firms, we can show empirically that a firm’s innovation activities affect its growth perspectives and its asset base differently, depending on the degree of exploitation/exploration. We also show that competitors’ R&D has important diverging effects on other firms which again depend on the degree of exploitation/exploration. Finally, we demonstrate the mediating role of environmental risk. We therefore argue that the trade-off between exploration and exploitation has (at least) three constituent dimensions: an internal dimension relating to performance in terms of increasing sales growth and preservation of the asset base, an external competitive dimension, and a contingency dimension relating to environmental factors such as risk. We conclude that the trade-off between exploration and exploitation can only be fully understood, if all three components are taken into account simultaneously.
Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization

Adversarial training (AT) is a widely recognized defense mechanism to gain the robustness of deep neural networks against adversarial attacks. It is built on min-max optimization (MMO), where the minimizer (i.e., defender) seeks a robust model to minimize the worst-case training loss in the presence of adversarial examples crafted by the maximizer (i.e., attacker). However, the conventional MMO method makes AT hard to scale. Thus, Fast-AT (Wong et al., 2020) and other recent algorithms attempt to simplify MMO by replacing its maximization step with the single gradient sign-based attack generation step. Although easy to implement, Fast-AT lacks theoretical guarantees, and its empirical performance is unsatisfactory due to the issue of robust catastrophic overfitting when training with strong adversaries. In this paper, we advance Fast-AT from the fresh perspective of bi-level optimization (BLO). We first show that the commonly-used Fast-AT is equivalent to using a stochastic gradient algorithm to solve a linearized BLO problem involving a sign operation. However, the discrete nature of the sign operation makes it difficult to understand the algorithm performance. Inspired by BLO, we design and analyze a new set of robust training algorithms termed Fast Bi-level AT (Fast-BAT), which effectively defends sign-based projected gradient descent (PGD) attacks without using any gradient sign method or explicit robust regularization. In practice, we show our method yields substantial robustness improvements over baselines across multiple models and datasets. Codes are available at https://github.com/OPTML-Group/Fast-BAT.
Diffusion models for atomic scale electron currents in semiconductor, p-n junction

While semiconductor electronics is at heart of modern world, and now uses 5nm or smaller processes of single atoms, it seems there are missing models of actual electron currents in these scales which could help with more conscious design of future electronics. This article proposes such practical methodology allowing to model approximated electron flows in semiconductor, nonlinear Ohm law in p-n junction, and hopefully more complex systems e.g. built of transistors. It assumes electron hopping between atoms using Maximal Entropy Random Walk based diffusion chosen accordingly to (Jaynes) maximal entropy principle, this way leading to the same stationary probability density as quantum models. Due to Anderson-like localization in nonhomogeneous lattice of semiconductor, electrons are imprisoned in entopic wells, e.g. requiring to exceed a potential barrier for conductance.
Wave-in-Deck Loads for an Intricate Pile-Supported Pier and Variation With Deck Clearance

Many deck structures are located at elevations low enough to be impacted by large waves. However, due to the highly complex and impulsive nature of the interactions between wave crests and intricate deck structures, establishing reliable estimates of extreme pressures and forces for use in design remains challenging. In this paper, results from an extensive set of three-dimensional scale model tests conducted to support the design of a large pile-supported pier (or jetty) are presented and discussed. Relationships between maximum wave-in-deck loads and the deck clearance (air gap) are presented and discussed. Results from numerical simulations of the wave-structure interaction process obtained using the three-dimensional CFD software FLOW-3D® are also presented and discussed. Finally, some initial comparisons between the numerical and physical modelling are also included. This paper provides new insights concerning the character and magnitude of the hydrodynamic pressures and loads exerted on intricate pile-supported deck structures due to impact by non-linear shallow-water waves, and the relationships between the hydrodynamic forcing and the deck clearance or air gap.Copyright © 2013 by ASME and Her Majesty the Queen in Right of Canada
EIFFeL: Ensuring Integrity for Federated Learning

Federated learning (FL) enables clients to collaborate with a server to train a machine learning model. To ensure privacy, the server performs secure aggregation of updates from the clients. Unfortunately, this prevents verification of the well-formedness (integrity) of the updates as the updates are masked. Consequently, malformed updates designed to poison the model can be injected without detection. In this paper, we formalize the problem of ensuring both update privacy and integrity in FL and present a new system, EIFFeL, that enables secure aggregation of verified updates. EIFFeL is a general framework that can enforce arbitrary integrity checks and remove malformed updates from the aggregate, without violating privacy. Our empirical evaluation demonstrates the practicality of EIFFeL. For instance, with 100 clients and 10% poisoning, EIFFeL can train an MNIST classification model to the same accuracy as that of a non-poisoned federated learner in just 2.4s per iteration.
InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images

During the computed tomography (CT) imaging process, metallic implants within patients always cause harmful artifacts, which adversely degrade the visual quality of reconstructed CT images and negatively a ﬀ ect the subsequent clinical diagnosis. For the metal artifact reduction (MAR) task, current deep learning based methods have achieved promising performance. However, most of them share two main common limitations: 1) the CT physical imaging geometry constraint is not comprehensively incorporated into deep network structures; 2) the entire framework has weak interpretability for the speciﬁc MAR task; hence, the role of every network module is di ﬃ cult to be evaluated. To alleviate these issues, in the paper, we construct a novel interpretable dual domain network, termed InDuDoNet + , into which CT imaging process is ﬁnely embedded. Concretely, we derive a joint spatial and Radon domain reconstruction model and propose an optimization algorithm with only simple operators for solving it. By unfolding the iterative steps involved in the proposed algorithm into the corresponding network modules, we easily build the InDuDoNet + with clear interpretability. Furthermore, we analyze the CT values among di ﬀ erent tissues, and merge the prior observations into a prior network for our InDuDoNet + , which signiﬁcantly improve its generalization performance. Comprehensive experiments on synthesized data and clinical data substantiate the superiority of the proposed methods as well as the superior generalization performance beyond the current state-of-the-art (SOTA) MAR methods. Code is available at https://github.com/hongwang01/InDuDoNet_plus .
Long-term Photometric and Low-resolution Spectroscopic Analysis of Five Contact Binaries

A photometric and spectroscopic investigation is performed on five W Ursae Majoris eclipsing binaries J015818.6+260247 (hereinafter as J0158b), J073248.4+405538 (hereinafter as J0732), J101330.8+494846 (hereinafter as J1013), J132439.8+130747 (hereinafter as J1324), and J152450.7+245943 (hereinafter as J1524). The photometric data are collected with the help of the 1.3 m Devasthal Fast Optical Telescope, the 1.04 m Sampurnanand Telescope, and the Transiting Exoplanet Survey Satellite space mission. The low-resolution spectra of the 4 m Large Sky Area Multi-Object Fiber Spectroscopic Telescope are used for spectroscopic analysis. The orbital period change of these systems is determined using our photometric data and previously available photometric data from different surveys. The orbital period of J1013 and J1524 is changing at a rate of −2.552 (±0.249) × 10−7 days yr−1 and −6.792( ±0.952) × 10−8 days yr−1, respectively, while others do not show any orbital period change. The orbital period change of J1013 and J1524 corresponds to a mass transfer rate of 2.199 × 10−7 and 6.151 × 10−8 M ⊙ yr−1 from the primary to the secondary component in these systems. It is likely that angular momentum loss via magnetic braking may also be responsible for the observed orbital period change in the case of J1524. All systems have a mass ratio lower than 0.5, except J0158b with a mass ratio of 0.71. All the systems are shallow-type contact binaries. J0158b and J1524 are subtype A while others are subtype W. The H α emission line region is compared with template spectra prepared using two inactive stars with the help of the STARMOD program. The J0158, J1324, and J1524 systems show excess emission in the residual spectra after subtraction of the template.