Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation

The process of designing costmaps for off-road driving tasks is often a challenging and engineering-intensive task. Recent work in costmap design for off-road driving focuses on training deep neural networks to predict costmaps from sensory observations using corpora of expert driving data. However, such approaches are generally subject to over-confident mis-predictions and are rarely evaluated in-the-loop on physical hardware. We present an inverse reinforcement learning-based method of efficiently training deep cost functions that are uncertainty-aware. We do so by leveraging recent advances in highly parallel model-predictive control and robotic risk estimation. In addition to demonstrating improvement at reproducing expert trajectories, we also evaluate the efficacy of these methods in challenging off-road navigation scenarios. We observe that our method significantly outperforms a geometric baseline, resulting in 44% improvement in expert path reconstruction and 57% fewer interventions in practice. We also observe that varying the risk tolerance of the vehicle results in qualitatively different navigation behaviors, especially with respect to higher-risk scenarios such as slopes and tall grass.33More detailed algorithms and additional visualizations are provided in the appendix Appendix (appendix link: tinyurl.com/mtkj63e8)
Directed path models of adsorbing and pulled copolymers

In this paper we examine the phases of a directed path model of a copolymer attached to a surface under the influence of a pulling force. The simplest model of an adsorbing directed polymer, attached at the one end to a surface and pulled from the surface by the other end, is reviewed—its phase diagram includes free, adsorbed and ballistic phases. In contrast to this model, we consider an adsorbing directed block copolymer attached at both endpoints to the adsorbing surface, and pulled away from the surface at its middle vertex. The phase diagram of this model includes ballistic, adsorbed and free phases, as well as a quasi-ballistic phase which has characteristics of both an adsorbed and a ballistic phase. We also generalize our methods to a model of directed block star copolymers and show that such directed copolymers have numerous adsorbed and quasi-ballistic phases. The phase transitions in this system are continuous in some cases and first order in others.
Fast and Noise-Resilient Magnetic Field Mapping on a Low-Cost UAV Using Gaussian Process Regression

This study presents a comprehensive approach to mapping local magnetic field anomalies with robustness to magnetic noise from an unmanned aerial vehicle (UAV). The UAV collects magnetic field measurements, which are used to generate a local magnetic field map through Gaussian process regression (GPR). The research identifies two categories of magnetic noise originating from the UAV’s electronics, adversely affecting map precision. First, this paper delineates a zero-mean noise arising from high-frequency motor commands issued by the UAV’s flight controller. To mitigate this noise, the study proposes adjusting a specific gain in the vehicle’s PID controller. Next, our research reveals that the UAV generates a time-varying magnetic bias that fluctuates throughout experimental trials. To address this issue, a novel compromise mapping technique is introduced, enabling the map to learn these time-varying biases with data collected from multiple flights. The compromise map circumvents excessive computational demands without sacrificing mapping accuracy by constraining the number of prediction points used for regression. A comparative analysis of the magnetic field maps’ accuracy and the spatial density of observations employed in map construction is then conducted. This examination serves as a guideline for best practices when designing trajectories for local magnetic field mapping. Furthermore, the study presents a novel consistency metric intended to determine whether predictions from a GPR magnetic field map should be retained or discarded during state estimation. Empirical evidence from over 120 flight tests substantiates the efficacy of the proposed methodologies. The data are made publicly accessible to facilitate future research endeavors.
Minute-cadence observations of the LAMOST fields with the TMTS – III. Statistical study of the flare stars from the first two years

Tsinghua University-Ma Huateng Telescopes for Survey (TMTS) aims to detect fast-evolving transients in the Universe, which has led to the discovery of thousands of short-period variables and eclipsing binaries since 2020. In this paper, we present the observed properties of 125 flare stars identified by TMTS within the first two years, with an attempt to constrain their eruption physics. As expected, most of these flares were recorded in late-type red stars with GBP − GRP >2.0 mag; however, the flares associated with bluer stars tend to be on average more energetic and have broader profiles. The peak flux (Fpeak) of the flare is found to depend strongly on the equivalent duration (ED) of the energy release, i.e. Fpeak∝ED0.72 ± 0.04, which is consistent with results derived from the Kepler and Evryscope samples. This relation is likely to be related to the magnetic loop emission, while, for the more popular non-thermal electron heating model, a specific time evolution may be required to generate this relation. We notice that flares produced by hotter stars have a flatter Fpeak - ED relation compared to that from cooler stars. This is related to the statistical discrepancy in light-curve shape of flare events with different colours. In spectra from LAMOST, we find that flare stars have apparently stronger H α emission than inactive stars, especially at the low-temperature end, suggesting that chromospheric activity plays an important role in producing flares. On the other hand, the subclass with frequent flares is found to show H α emission of similar strength in its spectra to that recorded with only a single flare but similar effective temperature, implying that chromospheric activity may not be the only trigger for eruptions.
Online Learning in Dynamically Changing Environments

We study the problem of online learning and online regret minimization when samples are drawn from a general unknown non-stationary process. We introduce the concept of a dynamic changing process with cost $K$, where the conditional marginals of the process can vary arbitrarily, but that the number of different conditional marginals is bounded by $K$ over $T$ rounds. For such processes we prove a tight (upto $\sqrt{\log T}$ factor) bound $O(\sqrt{KT\cdot\mathsf{VC}(\mathcal{H})\log T})$ for the expected worst case regret of any finite VC-dimensional class $\mathcal{H}$ under absolute loss (i.e., the expected miss-classification loss). We then improve this bound for general mixable losses, by establishing a tight (up to $\log^3 T$ factor) regret bound $O(K\cdot\mathsf{VC}(\mathcal{H})\log^3 T)$. We extend these results to general smooth adversary processes with unknown reference measure by showing a sub-linear regret bound for $1$-dimensional threshold functions under a general bounded convex loss. Our results can be viewed as a first step towards regret analysis with non-stationary samples in the distribution blind (universal) regime. This also brings a new viewpoint that shifts the study of complexity of the hypothesis classes to the study of the complexity of processes generating data.
Does Vision Accelerate Hierarchical Generalization of Neural Language Learners?

Neural language models (LMs) are arguably less data-efficient than humans -- why does this gap occur? In this study, we hypothesize that this gap stems from the learners' accessibility to modalities other than text, specifically, vision. We conducted two complementary experiments (using noisy, realistic data and a simplified, artificial one) toward the advantage of vision in the syntactic generalization of LMs. Our results showed that vision accelerated a proper linguistic generalization in the simplified, artificial setting, but LMs struggled with the noisy, realistic setting. These mixed results indicate several possibilities, e.g., vision can potentially boost language acquisition, but learners' additional visual/linguistic prior knowledge should be needed to robustly make use of raw images for efficient language acquisition.
Off-the-Grid MARL: a Framework for Dataset Generation with Baselines for Cooperative Offline Multi-Agent Reinforcement Learning

Being able to harness the power of large, static datasets for developing autonomous multi-agent systems could unlock enormous value for real-world applications. Many important industrial systems are multi-agent in nature and are difﬁcult to model using bespoke simulators. However, in industry, distributed system processes can often be recorded during operation, and large quantities of demonstrative data can be stored. Ofﬂine multi-agent reinforcement learning (MARL) provides a promising paradigm for building effective online controllers from static datasets. However, ofﬂine MARL is still in its infancy, and, therefore, lacks standardised benchmarks, baselines and evaluation protocols typically found in more mature sub-ﬁelds of RL. This deﬁciency makes it difﬁcult for the community to sensibly measure progress. In this work, we aim to ﬁll this gap by releasing off-the-grid MARL (OG-MARL) : a framework for generating ofﬂine MARL datasets and algorithms. We release an initial set of datasets and base-lines for cooperative ofﬂine MARL, created using the framework, along with a standardised evaluation protocol. Our datasets provide settings that are characteristic of real-world systems, including complex dynamics, non-stationarity, partial observability, suboptimality and sparse rewards, and are generated from popular online MARL benchmarks. We hope that OG-MARL will serve the community and help steer progress in ofﬂine MARL, while also providing an easy entry point for researchers new to the ﬁeld.
Non-Hermiticity induces localization: Good and bad resonances in power-law random banded matrices

The power-law random banded matrix (PLRBM) is a paradigmatic ensemble to study the Anderson localization transition (AT). In $d$-dimension the PLRBM are random matrices with algebraic decaying off-diagonal elements $H_{\vec{n}\vec{m}}\sim 1/|\vec{n}-\vec{m}|^\alpha$, having AT at $\alpha=d$. In this work, we investigate the fate of the PLRBM to non-Hermiticity. We consider the case where the random on-site diagonal potential takes complex values, mimicking an open system, subject to random gain-loss terms. We provide an analytical understanding of the model by generalizing the Anderson-Levitov resonance counting technique to the non-Hermitian case. This generalization identifies two competing mechanisms due to non-Hermiticity: one favoring localization and the other delocalization. The competition between the two gives rise to AT at $d/2\le \alpha\le d$. The value of the critical $\alpha$ depends on the strength of the on-site potential, reminiscent of Hermitian disordered short-range models in $d>2$. Within the localized phase, the wave functions are algebraically localized with an exponent $\alpha$ even for $\alpha
Atomic Electronic Structure Calculations with Hermite Interpolating Polynomials

We have recently described the implementation of atomic electronic structure calculations within the finite element method with numerical radial basis functions of the form χμ(r) = r–1Bμ(r), where high-order Lagrange interpolating polynomials (LIPs) were used as the shape functions Bμ(r). In this work, we discuss how χμ(r) can be evaluated in a stable manner at small r and also revisit the choice of the shape functions Bμ(r). Three kinds of shape functions are considered: in addition to the continuous LIPs, we consider the analytical implementation of first-order Hermite interpolating polynomials (HIPs) that are continuous, as well as numerical implementations of n-th order ( continuous) HIPs that are expressed in terms of an underlying high-order LIP basis. Furnished with the new implementation, we demonstrate that the first-order HIPs are reliable even with large numbers of nodes and that they also work with nonuniform element grids, affording even better results in atomic electronic structure calculations than LIPs with the same total number of basis functions. We demonstrate that discontinuities can be observed in the spin-σ local kinetic energy τσ in small LIP basis sets, while HIP basis sets do not suffer from such issues; however, either set can be used to reach the complete basis set limit with smooth τσ. Moreover, we discuss the implications of HIPs on calculations with meta-GGA functionals with a number of recent meta-GGA functionals, and we find most Minnesota functionals to be ill-behaved. We also examine the potential usefulness of the explicit control over the derivative in HIPs for forming numerical atomic orbital basis sets, but we find that confining potentials are still likely a better option.
Analyzing Leakage of Personally Identifiable Information in Language Models

Language Models (LMs) have been shown to leak information about training data through sentence-level membership inference and reconstruction attacks. Understanding the risk of LMs leaking Personally Identifiable Information (PII) has received less attention, which can be attributed to the false assumption that dataset curation techniques such as scrubbing are sufficient to prevent PII leakage. Scrubbing techniques reduce but do not prevent the risk of PII leakage: in practice scrubbing is imperfect and must balance the trade-off between minimizing disclosure and preserving the utility of the dataset. On the other hand, it is unclear to which extent algorithmic defenses such as differential privacy, designed to guarantee sentence-or user-level privacy, prevent PII disclosure. In this work, we introduce rigorous game-based definitions for three types of PII leakage via black-box extraction, inference, and reconstruction attacks with only API access to an LM. We empirically evaluate the attacks against GPT-2 models fine-tuned with and without defenses in three domains: case law, health care, and e-mails. Our main contributions are (i) novel attacks that can extract up to 10× more PII sequences than existing attacks, (ii) showing that sentence-level differential privacy reduces the risk of PII disclosure but still leaks about 3% of PII sequences, and (iii) a subtle connection between record-level membership inference and PII reconstruction. Code to reproduce all experiments in the paper is available at https://github.com/microsoft/analysing_pii_leakage.
How to select predictive models for causal inference?

As predictive models -- e.g., from machine learning -- give likely outcomes, they may be used to reason on the effect of an intervention, a causal-inference task. The increasing complexity of health data has opened the door to a plethora of models, but also the Pandora box of model selection: which of these models yield the most valid causal estimates? Here we highlight that classic machine-learning model selection does not select the best outcome models for causal inference. Indeed, causal model selection should control both outcome errors for each individual, treated or not treated, whereas only one outcome is observed. Theoretically, simple risks used in machine learning do not control causal effects when treated and non-treated population differ too much. More elaborate risks build proxies of the causal error using ``nuisance'' re-weighting to compute it on the observed data. But does computing these nuisance adds noise to model selection? Drawing from an extensive empirical study, we outline a good causal model-selection procedure: using the so-called $R\text{-risk}$; using flexible estimators to compute the nuisance models on the train set; and splitting out 10\% of the data to compute risks.
Improved Knowledge Distillation for Pre-trained Language Models via Knowledge Selection

Knowledge distillation addresses the problem of transferring knowledge from a teacher model to a student model. In this process, we typically have multiple types of knowledge extracted from the teacher model. The problem is to make full use of them to train the student model. Our preliminary study shows that: (1) not all of the knowledge is necessary for learning a good student model, and (2) knowledge distillation can benefit from certain knowledge at different training steps. In response to these, we propose an actor-critic approach to selecting appropriate knowledge to transfer during the process of knowledge distillation. In addition, we offer a refinement of the training algorithm to ease the computational burden. Experimental results on the GLUE datasets show that our method outperforms several strong knowledge distillation baselines significantly.
Delayed Feedback in Kernel Bandits

Black box optimisation of an unknown function from expensive and noisy evaluations is a ubiquitous problem in machine learning, academic research and industrial production. An abstraction of the problem can be formulated as a kernel based bandit problem (also known as Bayesian optimisation), where a learner aims at optimising a kernelized function through sequential noisy observations. The existing work predominantly assumes feedback is immediately available; an assumption which fails in many real world situations, including recommendation systems, clinical trials and hyperparameter tuning. We consider a kernel bandit problem under stochastically delayed feedback, and propose an algorithm with $\tilde{\mathcal{O}}(\sqrt{\Gamma_k(T)T}+\mathbb{E}[\tau])$ regret, where $T$ is the number of time steps, $\Gamma_k(T)$ is the maximum information gain of the kernel with $T$ observations, and $\tau$ is the delay random variable. This represents a significant improvement over the state of the art regret bound of $\tilde{\mathcal{O}}(\Gamma_k(T)\sqrt{T}+\mathbb{E}[\tau]\Gamma_k(T))$ reported in Verma et al. (2022). In particular, for very non-smooth kernels, the information gain grows almost linearly in time, trivializing the existing results. We also validate our theoretical results with simulations.
XCRYPT: Accelerating Lattice-Based Cryptography With Memristor Crossbar Arrays

This article makes a case for accelerating lattice-based postquantum cryptography with memristor-based crossbars. We map the polynomial multiplications in a representative algorithm, SABER, and show that analog dot products can yield 1.7–32.5× performance and energy efficiency improvement compared to recent hardware proposals. We introduce several additional techniques to address the bottlenecks in this initial design. First, we show that software techniques used in SABER that are effective on central processing unit platforms are unhelpful in crossbars. Relying on simpler algorithms further improves our efficiency by 1.3–3.6×. Second, modular arithmetic in SABER offers an opportunity to drop most significant bits, enabling techniques that exploit a few variable-precision analog-to-digital converters (ADCs) and yielding up to 1.8× higher efficiency. Third, to further reduce ADC pressure, we propose a simple analog shift-and-add technique, demonstrating a 1.3–6.3× improvement. Overall, the Xbar-based accelerator for postquantum cryptography (called XCRYPT) achieves 3–15× higher efficiency over the initial design and highlights the importance of algorithm–accelerator co-design.
MS-DETR: Multispectral Pedestrian Detection Transformer with Loosely Coupled Fusion and Modality-Balanced Optimization

Multispectral pedestrian detection is an important task for many around-the-clock applications, since the visible and thermal modalities can provide complementary information especially under low light conditions. Most of the available multispectral pedestrian detectors are based on non-end-to-end detectors, while in this paper, we propose MultiSpectral pedestrian DEtection TRansformer (MS-DETR), an end-to-end multispectral pedestrian detector, which extends DETR into the field of multi-modal detection. MS-DETR consists of two modality-specific backbones and Transformer encoders, followed by a multi-modal Transformer decoder, and the visible and thermal features are fused in the multi-modal Transformer decoder. To well resist the misalignment between multi-modal images, we design a loosely coupled fusion strategy by sparsely sampling some keypoints from multi-modal features independently and fusing them with adaptively learned attention weights. Moreover, based on the insight that not only different modalities, but also different pedestrian instances tend to have different confidence scores to final detection, we further propose an instance-aware modality-balanced optimization strategy, which preserves visible and thermal decoder branches and aligns their predicted slots through an instance-wise dynamic loss. Our end-to-end MS-DETR shows superior performance on the challenging KAIST, CVC-14 and LLVIP benchmark datasets. The source code is available at https://github.com/YinghuiXing/MS-DETR .
Structure and Kinematics of Sh2-138—A Distant Hub-filament System in the Outer Galactic Plane

We present a molecular line study of the Sh2-138 (IRAS 22308+5812) hub-filament system with the aim of investigating its structure and kinematics. Archival CO molecular line data from the Canadian Galactic Plane Survey (CO(J = 1–0)) for the wider region (∼50′ × 50′) and the James Clerk Maxwell Telescope (CO(3–2), 13CO(3–2), and C18O(3–2)) for the central portion (∼5′ × 5′) have been utilized. Analysis of the CO(1–0) spectra for the extended region in conjunction with the identification of the hub and filament using a column density map and the getsf tool, respectively, reveals a complex structure with the spectral extraction for the central position displaying multiple velocity components. Based on the Herschel 70 μm warm dust emission, one of the filaments in the extended region was inferred to be associated with active star formation, and is host to a Bolocam 1.1 mm clump of ∼1606 M ☉. An integrated intensity map of 13CO(3–2) emission, constructed from clumps detected at above 5σ in position–position–velocity space, reveals three filamentary structures (labeled the western filament (W-f), southwestern filament (SW-f), and southeast filament (SE-f)) in the central portion. Velocity gradients observed in 13CO(3–2) position–velocity slices point to longitudinal gas flow along the filaments into the central region. Filaments W-f, SW-f, and SE-f were calculated to have observed line masses of ∼32, 33.5, and 50 M ☉ pc−1, respectively. The cloud was found to be dominated by supersonic and nonthermal motions, with high Mach numbers (≳3) and a low thermal-to-nonthermal pressure ratio (∼0.01–0.1).
Width and Depth Limits Commute in Residual Networks

We show that taking the width and depth to infinity in a deep neural network with skip connections, when branches are scaled by $1/\sqrt{depth}$ (the only nontrivial scaling), result in the same covariance structure no matter how that limit is taken. This explains why the standard infinite-width-then-depth approach provides practical insights even for networks with depth of the same order as width. We also demonstrate that the pre-activations, in this case, have Gaussian distributions which has direct applications in Bayesian deep learning. We conduct extensive simulations that show an excellent match with our theoretical findings.