Miracle-dz/UniFAR-FTUs huggingface dataset:
Higher-order electron-phonon interactions and their effect on the thermal properties of 2D Dirac crystals

To understand the essential properties of Dirac crystals, such as their thermal conductivity, we require models that consider the interaction between Dirac electrons and dispersive acoustic phonons. The exceptionally high thermal conductivity in 2D Dirac crystals is attributed to near-ideal phonon quantum gases, while undesired limitations arise from electron-phonon (e-ph) interactions which have been shown to limit the thermal conductivity up to several microns away. The e-ph thermal conductivity is directly linked to the phonon scattering rate. Conventional calculations overlook phonons with short-dispersive wavelengths, rendering them inadequate for analyzing 2D Dirac crystals. The phonon scattering rate is typically calculated up to the first-order magnitude, considering 3-particle interactions involving the decay of an electron and phonon (EP-E*) to create a new electron. However, processes involving the decay of an electron and the creation of a new electron and phonon (E-E*P*) are neglected. In this study, we present an accurate expression for the phonon scattering rate and e-ph thermal conductivity in 2D Dirac crystals, accounting for short-dispersive wavelength phonons. We demonstrate the significance of the E-E*P* process even at room temperature in calculating the phonon scattering rate and e-ph thermal conductivity, particularly for first-order e-ph interactions. Furthermore, we emphasize the importance of incorporating second-order e-ph interactions, specifically the EP-E*P* interaction involving the decay of an electron and phonon and the creation of a new electron-phonon pair, to accurately determine the phonon scattering rate and e-ph thermal conductivity at high temperatures and low Fermi energies. This 4-particle interaction process plays a crucial role in characterizing these properties effectively.
Collective Behavior in a Local Causal Model of Quantum Theory

The non-localities in quantum theory (QT) (the most famous example is expressed in the violation of Bell’s inequality in experiments) impede the construction of a local causal model of QT including quantum field theory (QFT). The laws of collective behavior may be considered to be types of non-local laws: laws that apply to the collection of system components as a whole. The article presents a proposal for the treatment of the non-localities that exist in QT/QFT by the concepts of collective behavior. The basic components of the collective behavior are the spatial elements of the causal model of QT/QFT proposed by the author.
A mean-field formulation for multi-period asset-liability mean-variance portfolio selection with probability constraints

This paper is concerned with studying an optimal multi-period asset-liability mean-variance portfolio selection with probability constraints using mean-field formulation without embedding technique. We strictly derive its analytical optimal strategy and efficient frontier. Numerical examples shed light on efficiency and accuracy of our method when dealing with this class of multi-period non-separable mean-variance portfolio selection problems.
Embodied Lifelong Learning for Task and Motion Planning

A robot deployed in a home over long stretches of time faces a true lifelong learning problem. As it seeks to provide assistance to its users, the robot should leverage any accumulated experience to improve its own knowledge to become a more proficient assistant. We formalize this setting with a novel lifelong learning problem formulation in the context of learning for task and motion planning (TAMP). Exploiting the modularity of TAMP systems, we develop a generative mixture model that produces candidate continuous parameters for a planner. Whereas most existing lifelong learning approaches determine a priori how data is shared across task models, our approach learns shared and non-shared models and determines which to use online during planning based on auxiliary tasks that serve as a proxy for each model's understanding of a state. Our method exhibits substantial improvements in planning success on simulated 2D domains and on several problems from the BEHAVIOR benchmark.
Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compression

Large Deep Neural Networks (DNNs) are the backbone of today's artificial intelligence due to their ability to make accurate predictions when being trained on huge datasets. With advancing technologies, such as the Internet of Things, interpreting large quantities of data generated by sensors is becoming an increasingly important task. However, in many applications not only the predictive performance but also the energy consumption of deep learning models is of major interest. This paper investigates the efficient deployment of deep learning models on resource-constrained microcontroller architectures via network compression. We present a methodology for the systematic exploration of different DNN pruning, quantization, and deployment strategies, targeting different ARM Cortex-M based low-power systems. The exploration allows to analyze trade-offs between key metrics such as accuracy, memory consumption, execution time, and power consumption. We discuss experimental results on three different DNN architectures and show that we can compress them to below 10\% of their original parameter count before their predictive quality decreases. This also allows us to deploy and evaluate them on Cortex-M based microcontrollers.
Combinations of parabolically geometrically finite groups and their geometry

In this paper, we study the class of parabolically geometrically finite (PGF) subgroups of mapping class groups, introduced by Dowdall--Durham--Leininger--Sisto. We prove a combination theorem for graphs of PGF groups (and other generalizations) by utilizing subsurface projection to obtain control on the geometry of fundamental groups of graphs of PGF groups, generalizing and strengthening methods of Leininger--Reid. From this result, we construct new examples of PGF groups and provide methods for how to apply the combination theorem in practice. We also show that PGF groups are undistorted in their corresponding mapping class group.
Planar Disjoint Paths, Treewidth, and Kernels

In the PLANAR DISJOINT PATHS problem, one is given an undirected planar graph with a set of k vertex pairs $\left(s_{i}, t_{i}\right)$ and the task is to find k pairwise vertex-disjoint paths such that the i-th path connects $s_{i}$ to $t_{i}$. We study the problem through the lens of kernelization, aiming at efficiently reducing the input size in terms of a parameter. We show that PLANAR DISJOINT PATHS does not admit a polynomial kernel when parameterized by k unless coNP $\subseteq \mathrm{NP} /$ poly, resolving an open problem by [Bodlaender, Thomassé, Yeo, ESA’09]. Moreover, we rule out the existence of a polynomial Turing kernel unless the WKhierarchy collapses. Our reduction carries over to the setting of edge-disjoint paths, where the kernelization status remained open even in general graphs. On the positive side, we present a polynomial kernel for PLANAR DISJOINT PATHS parameterized by $k+\mathrm{tw}$, where tw denotes the treewidth of the input graph. As a consequence of both our results, we rule out the possibility of a polynomialtime (Turing) treewidth reduction to $t w=k^{\mathcal{O}(1)}$ under the same assumptions. To the best of our knowledge, this is the first hardness result of this kind. Finally, combining our kernel with the known techniques [Adler, Kolliopoulos, Krause, Lokshtanov, Saurabh, Thilikos, JCTB’17; Schrijver, SICOMP’94] yields an alternative (and arguably simpler) proof that PLANAR DISJOINT PATHS can be solved in time $2^{\mathcal{O}\left(k^{2}\right)} \cdot n^{\mathcal{O}(1)}$, matching the result of [Lokshtanov, Misra, Pilipczuk, Saurabh, Zehavi, STOC’20].
A well-balanced discontinuous Galerkin method for the first-order Z4 formulation of the Einstein-Euler system

In this paper we develop a new well-balanced discontinuous Galerkin (DG) finite element scheme with subcell finite volume (FV) limiter for the numerical solution of the Einstein--Euler equations of general relativity based on a first order hyperbolic reformulation of the Z4 formalism. The first order Z4 system, which is composed of 59 equations, is analyzed and proven to be strongly hyperbolic for a general metric. The well-balancing is achieved for arbitrary but a priori known equilibria by subtracting a discrete version of the equilibrium solution from the discretized time-dependent PDE system. Special care has also been taken in the design of the numerical viscosity so that the well-balancing property is achieved. As for the treatment of low density matter, e.g. when simulating massive compact objects like neutron stars surrounded by vacuum, we have introduced a new filter in the conversion from the conserved to the primitive variables, preventing superluminal velocities when the density drops below a certain threshold, and being potentially also very useful for the numerical investigation of highly rarefied relativistic astrophysical flows. Thanks to these improvements, all standard tests of numerical relativity are successfully reproduced, reaching three achievements: (i) we are able to obtain stable long term simulations of stationary black holes, including Kerr black holes with extreme spin, which after an initial perturbation return perfectly back to the equilibrium solution up to machine precision; (ii) a (standard) TOV star under perturbation is evolved in pure vacuum ($\rho$=$p$=0) up to t=1000 with no need to introduce any artificial atmosphere around the star; and, (iii) we solve the head on collision of two punctures black holes, that was previously considered un--tractable within the Z4 formalism.
Ensemble learning for blending gridded satellite and gauge-measured precipitation data

Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependent variables. Alongside this, it is increasingly recognised in many fields that combinations of algorithms through ensemble learning can lead to substantial predictive performance improvements. Still, a sufficient number of ensemble learners for improving the accuracy of satellite precipitation products and their large-scale comparison are currently missing from the literature. In this study, we work towards filling in this specific gap by proposing 11 new ensemble learners in the field and by extensively comparing them. We apply the ensemble learners to monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets that span over a 15-year period and over the entire contiguous United States (CONUS). We also use gauge-measured precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). The ensemble learners combine the predictions of six machine learning regression algorithms (base learners), namely the multivariate adaptive regression splines (MARS), multivariate adaptive polynomial splines (poly-MARS), random forests (RF), gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and Bayesian regularized neural networks (BRNN), and each of them is based on a different combiner. The combiners include the equal-weight combiner, the median combiner, two best learners and seven variants of a sophisticated stacking method. The latter stacks a regression algorithm on top of the base learners to combine their independent predictions. Its seven variants are defined by seven different regression algorithms, specifically the linear regression (LR) algorithm and the six algorithms also used as base learners. The results suggest that sophisticated stacking performs significantly better than the base learners, especially when applied using the LR algorithm. It also beats the simpler combination methods.
Conical refraction: fundamentals and applications

In 1832 Hamilton predicted that a collimated light beam propagating through a biaxial crystal parallel to one of its optical axes refracts as a slanted cone within the crystal and emerges as a hollow light cylinder, this optical effect being named as conical refraction (CR). The diffractive solution of CR presented by Belsky and Khapalyuk in 1978 and the corresponding re‐formulation carried out by Berry in 2004 rekindled this old and almost forgotten phenomenon. In this article, we review the CR phenomenon following different approaches that allow understanding light propagation in biaxial crystals, including the case of multiple crystals in cascade. We then focus on the description of the singular properties of the CR beams, presenting some examples such as optical bottle beams and beams carrying orbital angular momentum. All these features are used to introduce some of the most appealing applications of CR in the fields of optical trapping, free‐space optical communications, polarization metrology, super‐resolution imaging, two‐photon polymerization, and lasers.
LimSim: A Long-Term Interactive Multi-Scenario Traffic Simulator

With the growing popularity of digital twin and autonomous driving in transportation, the demand for simulation systems capable of generating high-fidelity and reliable scenarios is increasing. Existing simulation systems suffer from a lack of support for different types of scenarios, and the vehicle models used in these systems are too simplistic. Thus, such systems fail to represent driving styles and multi-vehicle interactions, and struggle to handle corner cases in the dataset. In this paper, we propose LimSim, the Long-term Interactive Multi-scenario traffic Simulator, which aims to provide a long-term continuous simulation capability under the urban road network. LimSim can simulate fine-grained dynamic scenarios and focus on the diverse interactions between multiple vehicles in the traffic flow. This paper provides a detailed introduction to the framework and features of the LimSim, and demonstrates its performance through case studies and experiments. The source code of LimSim is available on GitHub: github.com/PJLab-ADG/LimSim.
LVLane: Deep Learning for Lane Detection and Classification in Challenging Conditions

Lane detection plays a pivotal role in the field of autonomous vehicles and advanced driving assistant systems (ADAS). Despite advances from image processing to deep learning-based models, algorithm performance is highly dependent on training data matching the local challenges such as extreme lighting conditions, partially visible lane markings, and sparse lane markings like Botts' dots. To address this, we present an end-to-end lane detection and classification system based on deep learning methodologies. In our study, we introduce a unique dataset meticulously curated to encompass scenarios that pose significant challenges for state-of-the-art (SOTA) lane localization models. Moreover, we propose a CNN-based classification branch, seamlessly integrated with the detector, facilitating the identification of distinct lane types. This architecture enables informed lane-changing decisions and em-powers more resilient ADAS capabilities. We also investigate the effect of using mixed precision training and testing on different models and batch sizes. Experimental evaluations conducted on the widely-used TuSimple dataset, Caltech Lane dataset, and our LVLane dataset demonstrate the effectiveness of our model in accurately detecting and classifying lanes amidst challenging scenarios. Our method achieves state-of-the-art classification results on the TuSimple dataset. The code of the work can be found on www.github. com/zillur-av/LVLane.
Ageing Analysis of Embedded SRAM on a Large-Scale Testbed Using Machine Learning

Ageing detection and failure prediction are essential in many Internet of Things (IoT) deployments, which operate huge quantities of embedded devices unattended in the field for years. In this paper, we present a large-scale empirical analysis of natural SRAM wear-out using 154 boards from a generalpurpose testbed. Starting from SRAM initialization bias, which each node can easily collect at startup, we apply various metrics for feature extraction and experiment with common machine learning methods to predict the age of operation for this node. Our findings indicate that even though ageing impacts are subtle, our indicators can well estimate usage times with an R2 score of 0.77 and a mean error of 24% using regressors, and with an Fl score above 0.6 for classifiers applying a six-months resolution.
Learn-As-you-GO (LAGO) Trials: Optimizing Treatments and Preventing Trial Failure Through Ongoing Learning

It is well known that changing the intervention package while a trial is ongoing does not lead to valid inference using standard statistical methods. However, it is often necessary to adapt, tailor, or tweak a complex intervention package in public health implementation trials, especially when the intervention package does not have the desired effect. This article presents conditions under which the resulting analyses remain valid even when the intervention package is adapted while a trial is ongoing. Our results on such Learn-As-you-GO (LAGO) studies extend the theory of LAGO for binary outcomes following a logistic regression model (Nevo, Lok and Spiegelman, 2021) to LAGO for continuous outcomes under flexible conditional mean model. We derive point and interval estimators of the intervention effects and ensure the validity of hypothesis tests for an overall intervention effect. We develop a confidence set for the optimal intervention package, which achieves a pre-specified mean outcome while minimizing cost, and confidence bands for the mean outcome under all intervention package compositions. This work will be useful for the design and analysis of large-scale intervention trials where the intervention package is adapted, tailored, or tweaked while the trial is ongoing.
Normalized solutions for a fractional Choquard-type equation with exponential critical growth in $\mathbb{R}$

In this paper, we study the following fractional Choquard-type equation with prescribed mass \begin{align*} \begin{cases} (-\Delta)^{1/2}u=\lambda u +(I_\mu*F(u))f(u),\ \ \mbox{in}\ \mathbb{R}, \displaystyle\int_{\mathbb{R}}|u|^2 \mathrm{d}x=a^2, \end{cases} \end{align*} where $(-\Delta)^{1/2}$ denotes the $1/2$-Laplacian operator, $a>0$, $\lambda\in \mathbb{R}$, $I_\mu(x)=\frac{{1}}{{|x|^\mu}}$ with $\mu\in(0,1)$, $F(u)$ is the primitive function of $f(u)$, and $f$ is a continuous function with exponential critical growth in the sense of the Trudinger-Moser inequality. By using a minimax principle based on the homotopy stable family, we obtain that there is at least one normalized ground state solution to the above equation.
Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events

Large language models (LLMs), such as GPT-4, have demonstrated remarkable capabilities across a wide range of tasks, including health applications. In this paper, we study how LLMs can be used to scale biomedical knowledge curation. We find that while LLMs already possess decent competency in structuring biomedical text, by distillation into a task-specific student model through self-supervised learning, substantial gains can be attained over out-of-box LLMs, with additional advantages such as cost, efficiency, and white-box model access. We conduct a case study on adverse drug event (ADE) extraction, which is an important area for improving care. On standard ADE extraction evaluation, a GPT-3.5 distilled PubMedBERT model attained comparable accuracy as supervised state-of-the-art models without using any labeled data. Despite being over 1,000 times smaller, the distilled model outperformed its teacher GPT-3.5 by over 6 absolute points in F1 and GPT-4 by over 5 absolute points. Ablation studies on distillation model choice (e.g., PubMedBERT vs BioGPT) and ADE extraction architecture shed light on best practice for biomedical knowledge extraction. Similar gains were attained by distillation for other standard biomedical knowledge extraction tasks such as gene-disease associations and protected health information, further illustrating the promise of this approach.
Sharpness and well-conditioning of nonsmooth convex formulations in statistical signal recovery

We study a sample complexity vs. conditioning tradeoff in modern signal recovery problems where convex optimization problems are built from sampled observations. We begin by introducing a set of condition numbers related to sharpness in $\ell_p$ or Schatten-p norms ($p\in[1,2]$) based on nonsmooth reformulations of a class of convex optimization problems, including sparse recovery, low-rank matrix sensing, covariance estimation, and (abstract) phase retrieval. In each of the recovery tasks, we show that the condition numbers become dimension independent constants once the sample size exceeds some constant multiple of the recovery threshold. Structurally, this result ensures that the inaccuracy in the recovered signal due to both observation noise and optimization error is well-controlled. Algorithmically, such a result ensures that a new first-order method for solving the class of sharp convex functions in a given $\ell_p$ or Schatten-p norm, when applied to the nonsmooth formulations, achieves nearly-dimension-independent linear convergence.
Decomposing Finite Languages

The paper completely characterizes the primality of acyclic DFAs, where a DFA $\mathcal{A}$ is prime if there do not exist DFAs $\mathcal{A}_1,\dots,\mathcal{A}_t$ with $\mathcal{L}(\mathcal{A}) = \bigcap_{i=1}^{t} \mathcal{L}({\mathcal{A}_i})$ such that each $\mathcal{A}_i$ has strictly less states than the minimal DFA recognizing the same language as $\mathcal{A}$. A regular language is prime if its minimal DFA is prime. Thus, this result also characterizes the primality of finite languages. Further, the $\mathsf{NL}$-completeness of the corresponding decision problem $\mathsf{PrimeDFA}_{\text{fin}}$ is proven. The paper also characterizes the primality of acyclic DFAs under two different notions of compositionality, union and union-intersection compositionality. Additionally, the paper introduces the notion of S-primality, where a DFA $\mathcal{A}$ is S-prime if there do not exist DFAs $\mathcal{A}_1,\dots,\mathcal{A}_t$ with $\mathcal{L}(\mathcal{A}) = \bigcap_{i=1}^{t} \mathcal{L}(\mathcal{A}_i)$ such that each $\mathcal{A}_i$ has strictly less states than $\mathcal{A}$ itself. It is proven that the problem of deciding S-primality for a given DFA is $\mathsf{NL}$-hard. To do so, the $\mathsf{NL}$-completeness of $\mathsf{2MinimalDFA}$, the basic problem of deciding minimality for a DFA with at most two letters, is proven.