Miracle-dz/UniFAR-FTUs huggingface dataset:
Pure gauge theory for the gravitational spin connection

The gravitational spin connection appears in gravity as a non-Abelian gauge field for the Lorentz group $SO(3,1)$, which is non-compact. The action for General Relativity is linear in the field strength associated to the spin connection, and its equation of motion corresponds to the standard metricity constraint. Consequently, the zero-torsion spin connection is never realized as an independent degree of freedom and is determined by the vierbein field. In this work, we take a different perspective and consider a pure Yang-Mills theory for the spin connection coupled to Dirac fermions, resulting in the former being a dynamical field. After discussing various approaches towards managing the pathologies associated with non-compact gauge theories, we compute the tree-level amplitude for fermion scattering via a spin connection exchange. In contrast to integrating out torsion in the presence of fermions, the model induces a chiral four-Fermi like term that involves a right-right current interaction, which is not present in the Standard Model.
BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving Scenarios

Existing LiDAR-based 3D object detection methods for autonomous driving scenarios mainly adopt the training-from-scratch paradigm. Unfortunately, this paradigm heavily relies on large-scale labeled data, whose collection can be expensive and time-consuming. Self-supervised pre-training is an effective and desirable way to alleviate this dependence on extensive annotated data. In this work, we present BEV-MAE, an efficient masked autoencoder pre-training framework for LiDAR-based 3D object detection in autonomous driving. Specifically, we propose a bird's eye view (BEV) guided masking strategy to guide the 3D encoder learning feature representation in a BEV perspective and avoid complex decoder design during pre-training. Furthermore, we introduce a learnable point token to maintain a consistent receptive field size of the 3D encoder with fine-tuning for masked point cloud inputs. Based on the property of outdoor point clouds in autonomous driving scenarios, i.e., the point clouds of distant objects are more sparse, we propose point density prediction to enable the 3D encoder to learn location information, which is essential for object detection. Experimental results show that BEV-MAE surpasses prior state-of-the-art self-supervised methods and achieves a favorably pre-training efficiency. Furthermore, based on TransFusion-L, BEV-MAE achieves new state-of-the-art LiDAR-based 3D object detection results, with 73.6 NDS and 69.6 mAP on the nuScenes benchmark. The source code will be released at https://github.com/VDIGPKU/BEV-MAE.
Is ProtoPNet Really Explainable? Evaluating and Improving the Interpretability of Prototypes

ProtoPNet and its follow-up variants (ProtoPNets) have attracted broad research interest for their intrinsic inter-pretability from prototypes and comparable accuracy to non-interpretable counterparts. However, it has been recently found that the interpretability of prototypes can be corrupted due to the semantic gap between similarity in latent space and that in input space. In this work, we make the ﬁrst attempt to quantitatively evaluate the interpretability of prototype-based explanations, rather than solely qualitative evaluations by some visualization examples, which can be easily misled by cherry picks. To this end, we propose two evaluation metrics, termed consistency score and stability score, to evaluate the explanation consistency cross images and the explanation robustness against perturbations, both of which are essential for explanations taken into practice. Furthermore, we propose a shallow-deep feature alignment (SDFA) module and a score aggregation (SA) module to improve the interpretability of pro-totypes. We conduct systematical evaluation experiments and substantial discussions to uncover the interpretability of existing ProtoPNets. Experiments demonstrate that our method achieves signiﬁcantly superior performance to the state-of-the-arts, under both the conventional qualitative evaluations and the proposed quantitative evaluations, in both accuracy and interpretability. Codes are available at https://github.com/hqhQAQ/EvalProtoPNet .
Tensor-Based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations

Self-attentive transformer models have recently been shown to solve the next item recommendation task very efficiently. The learned attention weights capture sequential dynamics in user behavior and generalize well. Motivated by the special structure of learned parameter space, we question if it is possible to mimic it with an alternative and more lightweight approach. We develop a new tensor factorization-based model that ingrains the structural knowledge about sequential data within the learning process. We demonstrate how certain properties of a self-attention network can be reproduced with our approach based on special Hankel matrix representation. The resulting model has a shallow linear architecture. Remarkably, it achieves significant speedups in training time over its neural counterpart and performs competitively in terms of the quality of recommendations.
Holographic quantum scars

We discuss a construction of quantum many-body scars in the context of holography. We consider two-dimensional conformal field theories and use their dynamical symmetries, naturally realized through the Virasoro algebra, to construct scarred states. By studying their Loschmidt amplitude, we evaluate the states’ periodic properties. A geometrical interpretation allows us to compute the expectation value of the stress tensor and entanglement entropy of these scarred states. We show that their holographic dual is related by a diffeomorphism to empty AdS, even for energies above the black hole threshold. We also demonstrate that expectation values in the scarred states are generally non-thermal and that their entanglement entropy grows with the energy as \log(E)log(E) in contrast to \sqrt{E}E for the typical (bulk) states. Furthermore, we identify fixed points on the CFT plane associated with divergent or vanishing entanglement entropy in the limit where the scarred states have infinite energy.
Repulsive Fixpoints of Analytic Functions with Applications to Complex Dynamics

Let G be a family of functions analytic in a domain D in the complex plane. It is proved that G is a normal family, provided that for each f∈G, there exists k = k(f) > 1 such that the kth iterate fk has no repulsive fixpoint in D. A new proof of a result of Bergweiler and Terglane concerning the dynamics of entire functions is also given.
Focal-PETR: Embracing Foreground for Efficient Multi-Camera 3D Object Detection

The dominant multi-camera 3D detection paradigm is based on explicit 3D feature construction, which requires complicated indexing of local image-view features via 3D-to-2D projection. Other methods implicitly introduce geometric positional encoding and perform global attention (e.g., PETR) to build the relationship between image tokens and 3D objects. The 3D-to-2D perspective inconsistency and global attention lead to a weak correlation between foreground tokens and queries, resulting in slow convergence. We propose Focal-PETR with instance-guided supervision and spatial alignment module to adaptively focus object queries on discriminative foreground regions. Focal-PETR additionally introduces a down-sampling strategy to reduce the consumption of global attention. Our model achieves leading performance on the large-scale nuScenes benchmark and a superior speed of 30 FPS on a single RTX3090 GPU. Extensive experiments show that our method outperforms PETR while consuming 3x fewer training hours. The code is made publicly available.
Canonical form of $C^*$-algebra of eikonals related to the metric graph

The eikonal algebra $\mathfrak E$ of the metric graph $\Omega$ is an operator $C^*$--algebra defined by the dynamical system which describes the propagation of waves generated by sources supported in the boundary vertices of $\Omega$. This paper describes the canonical block form of the algebra $\mathfrak E$ of an arbitrary compact connected metric graph. Passing to this form is equivalent to constructing a functional model which realizes $\mathfrak E$ as an algebra of continuous matrix-valued functions on its spectrum $\widehat{\mathfrak{E}}$. The results are intended to be used in the inverse problem of reconstruction of the graph by spectral and dynamical boundary data. Bibliography: 28 items.
Joint Counting, Detection and Re-Identification for Multi-Object Tracking

The recent trend in 2D multiple object tracking (MOT) is jointly solving detection and tracking, where object detection and appearance feature (or motion) are learned simultaneously. Despite competitive performance, in crowded scenes, joint detection and tracking usually fail to find accurate object associations due to missed or false detections. In this paper, we jointly model counting, detection and re-identification in an end-to-end framework, named CountingMOT, tailored for crowded scenes. By imposing mutual object-count constraints between detection and counting, the CountingMOT tries to find a balance between object detection and crowd density map estimation, which can help it to recover missed detections or reject false detections. Our approach is an attempt to bridge the gap of object detection, counting, and re-Identification. This is in contrast to prior MOT methods that either ignore the crowd density and thus are prone to failure in crowded scenes,or depend on local correlations to build a graphical relationship for matching targets. The proposed MOT tracker can perform online and real-time tracking, and achieves the state-of-the-art results on public benchmarks MOT16 (MOTA of 79.7), MOT17 (MOTA of 81.3%) and MOT20 (MOTA of 78.9%).
Hybrid Censored Quantile Regression Forest to Assess the Heterogeneous Effects

In many applications, heterogeneous treatment effects on a censored response variable are of primary interest, and it is natural to evaluate the effects at different quantiles (e.g., median). The large number of potential effect modifiers, the unknown structure of the treatment effects, and the presence of right censoring pose significant challenges. In this paper, we develop a hybrid forest approach called Hybrid Censored Quantile Regression Forest (HCQRF) to assess the heterogeneous effects varying with high-dimensional variables. The hybrid estimation approach takes advantage of the random forests and the censored quantile regression. We propose a doubly-weighted estimation procedure that consists of a redistribution-of-mass weight to handle censoring and an adaptive nearest neighbor weight derived from the forest to handle high-dimensional effect functions. We propose a variable importance decomposition to measure the impact of a variable on the treatment effect function. Extensive simulation studies demonstrate the efficacy and stability of HCQRF. The result of the simulation study also convinces us of the effectiveness of the variable importance decomposition. We apply HCQRF to a clinical trial of colorectal cancer. We achieve insightful estimations of the treatment effect and meaningful variable importance results. The result of the variable importance also confirms the necessity of the decomposition.
Wireless earbuds for low-cost hearing screening

We present the first wireless earbud hardware that can perform hearing screening by detecting otoacoustic emissions. The conventional wisdom has been that detecting otoacoustic emissions, which are the faint sounds generated by the cochlea, requires sensitive and expensive acoustic hardware. Thus, medical devices for hearing screening cost thousands of dollars and are inaccessible in low and middle income countries. We show that by designing wireless ear-buds using low-cost acoustic hardware and combining them with wireless sensing algorithms, we can reliably identify otoacoustic emissions and perform hearing screening. Our algorithms combine frequency modulated chirps with wideband pulses emitted from a low-cost speaker to reliably separate otoacoustic emissions from in-ear reflections and echoes. We conducted a clinical study with 50 ears across two healthcare sites. Our study shows that the low-cost earbuds detect hearing loss with 100% sensitivity and 89.7% specificity, which is comparable to the performance of a $8000 medical device. By developing low-cost and open-source wearable technology, our work may help address global health inequities in hearing screening by democratizing these medical devices. Open-source hardware and code can be found here: https://github.com/uw-x/OAEbuds
Tempered Realism about the Force of Selection

Darwinians are realists about the force of selection, but there has been surprisingly little discussion about what form this realism should take. Arguments about the units of selection in general and genic selectionism in particular reveal two realist assumptions: (1) for any selection process, there is a uniquely correct identification of the operative selective forces and the level at which each impinges; and (2) selective forces must satisfy the Pareto-style requirement of probabilistic causation. I argue that both assumptions are false; we must temper realism about the force of selection and revise the way we think about probabilistic causation.
Integrating multi-type aberrations from DNA and RNA through dynamic mapping gene space for subtype-specific breast cancer driver discovery

Driver event discovery is a crucial demand for breast cancer diagnosis and therapy. In particular, discovering subtype-specificity of drivers can prompt the personalized biomarker discovery and precision treatment of cancer patients. Still, most of the existing computational driver discovery studies mainly exploit the information from DNA aberrations and gene interactions. Notably, cancer driver events would occur due to not only DNA aberrations but also RNA alternations, but integrating multi-type aberrations from both DNA and RNA is still a challenging task for breast cancer drivers. On the one hand, the data formats of different aberration types also differ from each other, known as data format incompatibility. On the other hand, different types of aberrations demonstrate distinct patterns across samples, known as aberration type heterogeneity. To promote the integrated analysis of subtype-specific breast cancer drivers, we design a “splicing-and-fusing” framework to address the issues of data format incompatibility and aberration type heterogeneity simultaneously. To overcome the data format incompatibility, the “splicing-step” employs a knowledge graph structure to connect multi-type aberrations from the DNA and RNA data into a unified formation. To tackle the aberration type heterogeneity, the “fusing-step” adopts a dynamic mapping gene space integration approach to represent the multi-type information by vectorized profiles. The experiments also demonstrate the advantages of our approach in both the integration of multi-type aberrations from DNA and RNA and the discovery of subtype-specific breast cancer drivers. In summary, our “splicing-and-fusing” framework with knowledge graph connection and dynamic mapping gene space fusion of multi-type aberrations data from DNA and RNA can successfully discover potential breast cancer drivers with subtype-specificity indication.
Verifiably Safe Reinforcement Learning with Probabilistic Guarantees via Temporal Logic

Reinforcement Learning (RL) can solve complex tasks but does not intrinsically provide any guarantees on system behavior. For real-world systems that fulﬁll safety-critical tasks, such guarantees on safety speciﬁcations are necessary. To bridge this gap, we propose a veriﬁably safe RL procedure with probabilistic guarantees. First, our approach probabilistically veriﬁes a candidate controller with respect to a temporal logic speciﬁcation, while randomizing the controller’s inputs within a bounded set. Then, we use RL to improve the performance of this probabilistically veriﬁed, i.e. safe, controller and explore in the same bounded set around the controller’s input as was randomized over in the veriﬁcation step. Finally, we calculate probabilistic safety guarantees with respect to temporal logic speciﬁcations for the learned agent. Our approach is efﬁcient for continuous action and state spaces and separates safety veriﬁcation and performance improvement into two independent steps. We evaluate our approach on a safe evasion task where a robot has to evade a dynamic obstacle in a speciﬁc manner while trying to reach a goal. The results show that our veriﬁably safe RL approach leads to efﬁcient learning and performance improvements while maintaining safety speciﬁcations.
Instrumental Variables in Causal Inference and Machine Learning: A Survey

Causal inference is the process of using assumptions, study designs, and estimation strategies to draw conclusions about the causal relationships between variables based on data. This allows researchers to better understand the underlying mechanisms at work in complex systems and make more informed decisions. In many settings, we may not fully observe all the confounders that affect both the treatment and outcome variables, complicating the estimation of causal effects. To address this problem, a growing literature in both causal inference and machine learning proposes to use Instrumental Variables (IV). This paper serves as the first effort to systematically and comprehensively introduce and discuss the IV methods and their applications in both causal inference and machine learning. First, we provide the formal definition of IVs and discuss the identification problem of IV regression methods under different assumptions. Second, we categorize the existing work on IV methods into three streams according to the focus on the proposed methods, including two-stage least squares with IVs, control function with IVs, and evaluation of IVs. For each stream, we present both the classical causal inference methods, and recent developments in the machine learning literature. Then, we introduce a variety of applications of IV methods in real-world scenarios and provide a summary of the available datasets and algorithms. Finally, we summarize the literature, discuss the open problems and suggest promising future research directions for IV methods and their applications. We also develop a toolkit of IVs methods reviewed in this survey at https://github.com/causal-machine-learning-lab/mliv.
Fluctuation-induced first-order phase transitions in the Kitaev-like 
d4
 honeycomb magnet

We study numerically a bosonic analog of the Kitaev honeycomb model, which is a minimal model for $4d^4/5d^4$ quantum magnets with honeycomb lattice geometry. We construct its phase diagram by a combination of Landau theory analysis and quantum Monte Carlo simulations. In particular, we show that the phase boundaries between the paramagnetic state and magnetically ordered states are generically fluctuation-induced first order phase transitions. Our results are potentially applicable to Ru$^{4+}$- and Ir$^{5+}$-based honeycomb magnets.
Laser blow-off injected impurity particle confinement times in JET and Tore Supra

Impurities are injected into JET and Tore Supra by the laser blow-off ablation technique for a variety of experimental situations. The impurity confinement times τp, which reflect the impurity transport, have been measured by fitting exponential curves to the decays of the central brightnesses. Two databases are built up, including ohmic and L mode discharges, with the aim of determining a τp scaling law common to both devices. Different monomial scaling laws are tested and the best one is chosen on the basis of minimization of the standard deviation of the individual regressions. Moreover, the impurity confinement times are compared with the energy confinement times τE for the same data. The energy confinement times are larger than the impurity confinement times, the average ratio tau E/τp being approximately 2.5
A Unified Knowledge Graph Service for Developing Domain Language Models in AI Software

—Natural Language Processing (NLP) is one of the core techniques in AI software. As AI is being applied to more and more domains, how to efﬁciently develop high-quality domain-speciﬁc language models becomes a critical question in AI software engineering. Existing domain-speciﬁc language model development processes mostly focus on learning a domain-speciﬁc pre-trained language model (PLM); when training the domain task-speciﬁc language model based on PLM, only a direct (and often unsatisfactory) ﬁne-tuning strategy is adopted commonly. By enhancing the task-speciﬁc training procedure with domain knowledge graphs, we propose KnowledgeDA , a uniﬁed and low-code domain language model development service. Given domain-speciﬁc task texts input by a user, KnowledgeDA can automatically generate a domain-speciﬁc language model following three steps: (i) localize domain knowledge entities in texts via an embedding-similarity approach; (ii) generate augmented samples by retrieving replaceable domain entity pairs from two views of both knowledge graph and training data; (iii) select high-quality augmented samples for ﬁne-tuning via conﬁdence-based assessment. We implement a prototype of KnowledgeDA to learn language models for two domains, healthcare and software development . Experiments on ﬁve domain-speciﬁc NLP tasks verify the effectiveness and generalizability of KnowledgeDA .
Ramp Metering to Maximize Freeway Throughput Under Vehicle Safety Constraints

We consider Ramp Metering (RM) at the microscopic level subject to vehicle following safety constraints for a freeway with arbitrary number of on- and off-ramps. The arrival times of vehicles to the on-ramps, as well as their destinations are modeled by exogenous stochastic processes. Once a vehicle is released from an on-ramp, it accelerates towards the free flow speed if it is not obstructed by another vehicle; once it gets close to another vehicle, it adopts a safe gap vehicle following behavior. The vehicle exits the freeway once it reaches its destination off-ramp. We design traffic-responsive RM policies that maximize the throughput. For a given routing matrix, the throughput of a RM policy is characterized by the set of on-ramp arrival rates for which the expected queue size at all the on-ramps remain bounded. The proposed RM policies work in synchronous cycles during which an on-ramp does not release more vehicles than its queue size at the beginning of the cycle. Moreover, all the policies operate under vehicle following safety constraints, where new vehicles are released only if there is sufficient gap between vehicles on the mainline at the moment of release. We provide three mechanisms under which each on-ramp: (i) pauses release for a time interval at the end of a cycle, or (ii) adjusts the release rate during a cycle, or (iii) adopts a conservative safe gap criterion for release during a cycle. All the proposed policies are reactive, meaning that they only require real-time traffic measurements without the need for demand prediction. The throughput of these policies is characterized by studying stochastic stability of the induced Markov chains, and is proven to be maximized when the merging speed at all the on-ramps equals the free flow speed. Simulations are provided to illustrate the performance of our policies and compare with a well-known RM policy from the literature.