Miracle-dz/UniFAR-FTUs huggingface dataset:
Geodesic ball packings generated by rotations and monotonicity behavior of their densities in $\mathbf{H}^2\!\times\!\mathbf{R}$ space

After having investigated several types of geodesic ball packings in $\SXR$ space, in this paper we study the locally optimal geodesic simply and multiply transitive ball packings with equal balls to the space groups generated by rotations in $\HXR$ geometry which space can be derived by the direct product of the hyperbolic plane $\bH^2$ and the real line $\bR$. Moreover, we develop a procedure to determine the densities of the above locally densest geodesic ball packing configurations. Additionally, we examine the monotonicity properties of the densities within infinite series of the considered space groups. E. Moln\'ar showed, that the homogeneous 3-spaces have a unified interpretation in the projective 3-sphere $\mathcal{PS}^3(\bV^4,\BV_4, \mathbf{R})$. In our work, we use this projective model of $\HXR$ and apply this to the visualization of locally optimal ball arrangements.
Summary of the DISPLACE Challenge 2023 - DIarization of SPeaker and LAnguage in Conversational Environments

In multi-lingual societies, where multiple languages are spoken in a small geographic vicinity, informal conversations often involve mix of languages. Existing speech technologies may be inefficient in extracting information from such conversations, where the speech data is rich in diversity with multiple languages and speakers. The DISPLACE (DIarization of SPeaker and LAnguage in Conversational Environments) challenge constitutes an open-call for evaluating and bench-marking the speaker and language diarization technologies on this challenging condition. The challenge entailed two tracks: Track-1 focused on speaker diarization (SD) in multilingual situations while, Track-2 addressed the language diarization (LD) in a multi-speaker scenario. Both the tracks were evaluated using the same underlying audio data. To facilitate this evaluation, a real-world dataset featuring multilingual, multi-speaker conversational far-field speech was recorded and distributed. Furthermore, a baseline system was made available for both SD and LD task which mimicked the state-of-art in these tasks. The challenge garnered a total of $42$ world-wide registrations and received a total of $19$ combined submissions for Track-1 and Track-2. This paper describes the challenge, details of the datasets, tasks, and the baseline system. Additionally, the paper provides a concise overview of the submitted systems in both tracks, with an emphasis given to the top performing systems. The paper also presents insights and future perspectives for SD and LD tasks, focusing on the key challenges that the systems need to overcome before wide-spread commercial deployment on such conversations.
Temporal Action-aware Network with Sequence Regularization for Phase Recognition

To assist surgeons in the operating theatre, surgical phase recognition is critical for developing computer-assisted surgical systems, which requires comprehensive understanding of surgical videos. Although existing studies made great progress, there are still two significant limitations worthy of improvement. First, due to the compromise of resource consumption, framewise visual features are extracted by 2D networks and disregard spatial and temporal knowledge of surgical actions, which hinders subsequent inter-frame modeling for phase prediction. Second, these works simply utilize ordinary classification loss with one-hot phase labels to optimize the phase predictions, and cannot fully explore surgical videos under inadequate supervision. To overcome these two limitations, we propose a Surgical Temporal Action-aware Network with sequence Regularization, named STAR-Net, to recognize surgical phases more accurately from input videos. Specifically, we propose an efficient multi-scale surgical temporal action (MS-STA) module, which integrates visual features with spatial and temporal knowledge of surgical actions at the cost of 2D networks. Moreover, we devise the dual-classifier sequence regularization (DSR) to facilitate the training of STAR-Net by the sequence guidance of an auxiliary classifier with a smaller capacity. Our STAR-Net with MS-STA and DSR can exploit visual features of surgical actions with effective regularization, thereby leading to the superior performance of surgical phase recognition. Extensive experiments on a large-scale gastrectomy surgery dataset and the public Cholec80 benchmark prove that our STAR-Net significantly outperforms state-of-the-arts of surgical phase recognition.
Non-conservation of the orbital moment of Bloch electrons in an electric field

The orbital moment (OM) of Bloch electrons has witnessed a surge in interest in out-of--equilibrium systems. Here, by developing a quantum kinetic theory, we show that the OM is in general not conserved in an electric field. The force moment produces a torque on the OM, which is determined by the quantum geometric tensor and the group velocities of Bloch bands. The torque vanishes in two-band systems with particle-hole symmetry, but is nonzero otherwise. For tilted massive Dirac fermions the torque is determined by the magnitude and direction of the tilt.
\AE\ codes

Diatomic molecular codes [{arXiv:1911.00099}] are designed to encode quantum information in the orientation of a diatomic molecule, allowing error correction from small torques and changes in angular momentum. Here, we directly study noise native to atomic and molecular platforms -- spontaneous emission, stray electromagnetic fields, and Raman scattering -- and derive simple necessary and sufficient conditions for codes to protect against such noise. We identify existing and develop new absorption-emission (\AE) codes that are more practical than molecular codes, require lower average momentum, can directly protect against photonic processes up to arbitrary order, and are applicable to a broader set of atomic and molecular systems.
Thermodynamic Matrix Exponentials and Thermodynamic Parallelism

Thermodynamic computing exploits fluctuations and dissipation in physical systems to efficiently solve various mathematical problems. For example, it was recently shown that certain linear algebra problems can be solved thermodynamically, leading to an asymptotic speedup scaling with the matrix dimension. The origin of this"thermodynamic advantage"has not yet been fully explained, and it is not clear what other problems might benefit from it. Here we provide a new thermodynamic algorithm for exponentiating a real matrix, with applications in simulating linear dynamical systems. We describe a simple electrical circuit involving coupled oscillators, whose thermal equilibration can implement our algorithm. We also show that this algorithm also provides an asymptotic speedup that is linear in the dimension. Finally, we introduce the concept of thermodynamic parallelism to explain this speedup, stating that thermodynamic noise provides a resource leading to effective parallelization of computations, and we hypothesize this as a mechanism to explain thermodynamic advantage more generally.
Type A Partially-Symmetric Macdonald Polynomials

We construct type A partially-symmetric Macdonald polynomials $P_{(\lambda \mid \gamma)}$, where $\lambda \in \mathbb{Z}_{\geq 0}^{n-k}$ is a partition and $\gamma \in \mathbb{Z}_{\geq 0}^k$ is a composition. These are polynomials which are symmetric in the first $n-k$ variables, but not necessarily in the final $k$ variables. We establish their stability and an integral form defined using Young diagram statistics. Finally, we build Pieri-type rules for degree 1 products $x_j P_{(\lambda \mid \gamma)}$ for $j>n-k$ and $e_1[x_1, \dotsc, x_{n-k}] P_{(\lambda \mid \gamma)}$, along with substantial combinatorial simplification of the $e_1$ multiplication. The $P_{(\lambda \mid \gamma)}$ are the same as the $m$-symmetric Macdonald polynomials defined by Lapointe up to a change of variables.
Representations of the super-Yangian of type $B(n,m)$

We are concerned with finite-dimensional irreducible representations of the Yangians associated with the orthosymplectic Lie superalgebras ${\frak osp}_{2n+1|2m}$. Every such representation is highest weight and we use embedding theorems and odd reflections of Yangian type to derive necessary conditions for an irreducible highest weight representation to be finite-dimensional. We conjecture that these conditions are also sufficient. We prove the conjecture in the case $n=1$ and arbitrary $m\geqslant 1$.
Insider trading with penalties, entropy and quadratic BSDEs

Kyle model in continuous time where the insider may be subject to legal penalties is considered. In equilibrium the insider internalises this legal risk by trading less aggressively. The equilibrium is characterised via the solution of a backward stochastic differential equation (BSDE) whose terminal condition is determined as the fixed point of a non-linear operator in equilibrium. The insider's expected penalties in equilibrium is non-monotone in the fee structure and is given by the relative entropy of the law of a particular h-transformation of Brownian motion.
Board-to-Board: Evaluating Moonboard Grade Prediction Generalization

Bouldering is a sport where athletes aim to climb up an obstacle using a set of defined holds called a route. Typically routes are assigned a grade to inform climbers of its difficulty and allow them to more easily track their progression. However, the variation in individual climbers technical and physical attributes and many nuances of an individual route make grading a difficult and often biased task. In this work, we apply classical and deep-learning modelling techniques to the 2016, 2017 and 2019 Moonboard datasets, achieving state of the art grade prediction performance with 0.87 MAE and 1.12 RMSE. We achieve this performance on a feature-set that does not require decomposing routes into individual moves, which is a method common in literature and introduces bias. We also demonstrate the generalization capability of this model between editions and introduce a novel vision-based method of grade prediction. While the generalization performance of these techniques is below human level performance currently, we propose these methods as a basis for future work. Such a tool could be implemented in pre-existing mobile applications and would allow climbers to better track their progress and assess new routes with reduced bias.
SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces

Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency heavily relies on individual training data obtained during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we present SSVEP-DAN, the first dedicated neural network model designed for aligning SSVEP data across different domains, which can encompass various sessions, subjects, or devices. Our experimental results across multiple cross-domain scenarios demonstrate SSVEP-DAN's capability to transform existing source SSVEP data into supplementary calibration data, significantly enhancing SSVEP decoding accuracy in scenarios with limited calibration data. We envision SSVEP-DAN as a catalyst for practical SSVEP-based BCI applications with minimal calibration. The source codes in this work are available at: https://github.com/CECNL/SSVEP-DAN.
minimax: Efficient Baselines for Autocurricula in JAX

Unsupervised environment design (UED) is a form of automatic curriculum learning for training robust decision-making agents to zero-shot transfer into unseen environments. Such autocurricula have received much interest from the RL community. However, UED experiments, based on CPU rollouts and GPU model updates, have often required several weeks of training. This compute requirement is a major obstacle to rapid innovation for the field. This work introduces the minimax library for UED training on accelerated hardware. Using JAX to implement fully-tensorized environments and autocurriculum algorithms, minimax allows the entire training loop to be compiled for hardware acceleration. To provide a petri dish for rapid experimentation, minimax includes a tensorized grid-world based on MiniGrid, in addition to reusable abstractions for conducting autocurricula in procedurally-generated environments. With these components, minimax provides strong UED baselines, including new parallelized variants, which achieve over 120$\times$ speedups in wall time compared to previous implementations when training with equal batch sizes. The minimax library is available under the Apache 2.0 license at https://github.com/facebookresearch/minimax.
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches

Adversarial attacks are a major deterrent towards the reliable use of machine learning models. A powerful type of adversarial attacks is the patch-based attack, wherein the adversarial perturbations modify localized patches or specific areas within the images to deceive the trained machine learning model. In this paper, we introduce Outlier Detection and Dimension Reduction (ODDR), a holistic defense mechanism designed to effectively mitigate patch-based adversarial attacks. In our approach, we posit that input features corresponding to adversarial patches, whether naturalistic or otherwise, deviate from the inherent distribution of the remaining image sample and can be identified as outliers or anomalies. ODDR employs a three-stage pipeline: Fragmentation, Segregation, and Neutralization, providing a model-agnostic solution applicable to both image classification and object detection tasks. The Fragmentation stage parses the samples into chunks for the subsequent Segregation process. Here, outlier detection techniques identify and segregate the anomalous features associated with adversarial perturbations. The Neutralization stage utilizes dimension reduction methods on the outliers to mitigate the impact of adversarial perturbations without sacrificing pertinent information necessary for the machine learning task. Extensive testing on benchmark datasets and state-of-the-art adversarial patches demonstrates the effectiveness of ODDR. Results indicate robust accuracies matching and lying within a small range of clean accuracies (1%-3% for classification and 3%-5% for object detection), with only a marginal compromise of 1%-2% in performance on clean samples, thereby significantly outperforming other defenses.
A unified consensus-based parallel ADMM algorithm for high-dimensional regression with combined regularizations

The parallel alternating direction method of multipliers (ADMM) algorithm is widely recognized for its effectiveness in handling large-scale datasets stored in a distributed manner, making it a popular choice for solving statistical learning models. However, there is currently limited research on parallel algorithms specifically designed for high-dimensional regression with combined (composite) regularization terms. These terms, such as elastic-net, sparse group lasso, sparse fused lasso, and their nonconvex variants, have gained significant attention in various fields due to their ability to incorporate prior information and promote sparsity within specific groups or fused variables. The scarcity of parallel algorithms for combined regularizations can be attributed to the inherent nonsmoothness and complexity of these terms, as well as the absence of closed-form solutions for certain proximal operators associated with them. In this paper, we propose a unified constrained optimization formulation based on the consensus problem for these types of convex and nonconvex regression problems and derive the corresponding parallel ADMM algorithms. Furthermore, we prove that the proposed algorithm not only has global convergence but also exhibits linear convergence rate. Extensive simulation experiments, along with a financial example, serve to demonstrate the reliability, stability, and scalability of our algorithm. The R package for implementing the proposed algorithms can be obtained at https://github.com/xfwu1016/CPADMM.
Not Just Training, Also Testing: High School Youths' Perspective-Taking through Peer Testing Machine Learning-Powered Applications

Most attention in K-12 artificial intelligence and machine learning (AI/ML) education has been given to having youths train models, with much less attention to the equally important testing of models when creating machine learning applications. Testing ML applications allows for the evaluation of models against predictions and can help creators of applications identify and address failure and edge cases that could negatively impact user experiences. We investigate how testing each other's projects supported youths to take perspective about functionality, performance, and potential issues in their own projects. We analyzed testing worksheets, audio and video recordings collected during a two week workshop in which 11 high school youths created physical computing projects that included (audio, pose, and image) ML classifiers. We found that through peer-testing youths reflected on the size of their training datasets, the diversity of their training data, the design of their classes and the contexts in which they produced training data. We discuss future directions for research on peer-testing in AI/ML education and current limitations for these kinds of activities.
Geometric Construction of non-linear Sigma models with Q-ball/Q-kink solutions

Non-linear Sigma models involving U(1) symmetry group are studied using a geometrical formalism. In this type of models, Q-balls and Q-Kinks solutions are found. The geometrical framework described in this article allows the identification of the necessary conditions on the metric and the potential to guarantee the existence of these Q-balls and Q-Kinks. Using this procedure, Sigma models where both types of solutions coexist, have been identified. Only the internal rotational frequency distinguishes which one of these defects will arise.
Attacking Motion Planners Using Adversarial Perception Errors

Autonomous driving (AD) systems are often built and tested in a modular fashion, where the performance of different modules is measured using task-specific metrics. These metrics should be chosen so as to capture the downstream impact of each module and the performance of the system as a whole. For example, high perception quality should enable prediction and planning to be performed safely. Even though this is true in general, we show here that it is possible to construct planner inputs that score very highly on various perception quality metrics but still lead to planning failures. In an analogy to adversarial attacks on image classifiers, we call such inputs \textbf{adversarial perception errors} and show they can be systematically constructed using a simple boundary-attack algorithm. We demonstrate the effectiveness of this algorithm by finding attacks for two different black-box planners in several urban and highway driving scenarios using the CARLA simulator. Finally, we analyse the properties of these attacks and show that they are isolated in the input space of the planner, and discuss their implications for AD system deployment and testing.
FedDRO: Federated Compositional Optimization for Distributionally Robust Learning

Recently, compositional optimization (CO) has gained popularity because of its applications in distributionally robust optimization (DRO) and many other machine learning problems. Large-scale and distributed availability of data demands the development of efficient federated learning (FL) algorithms for solving CO problems. Developing FL algorithms for CO is particularly challenging because of the compositional nature of the objective. Moreover, current state-of-the-art methods to solve such problems rely on large batch gradients (depending on the solution accuracy) not feasible for most practical settings. To address these challenges, in this work, we propose efficient FedAvg-type algorithms for solving non-convex CO in the FL setting. We first establish that vanilla FedAvg is not suitable to solve distributed CO problems because of the data heterogeneity in the compositional objective at each client which leads to the amplification of bias in the local compositional gradient estimates. To this end, we propose a novel FL framework FedDRO that utilizes the DRO problem structure to design a communication strategy that allows FedAvg to control the bias in the estimation of the compositional gradient. A key novelty of our work is to develop solution accuracy-independent algorithms that do not require large batch gradients (and function evaluations) for solving federated CO problems. We establish $\mathcal{O}(\epsilon^{-2})$ sample and $\mathcal{O}(\epsilon^{-3/2})$ communication complexity in the FL setting while achieving linear speedup with the number of clients. We corroborate our theoretical findings with empirical studies on large-scale DRO problems.
Structural Characteristics of the Dien Bien Phu Strike Slip Fault Zone and Its Regional Tectonic Implication

The Dien Bien Phu (DBP) fault zone is one of the most seismically active fault systems in the Indochina, extending over a distance of some 150 km from the border between Yunnan, China and Vietnam through NW Vietnam into Laos. The faults are characterized by strike-slip and oblique-slip, with dipping angle of 60°~70° to the west in the northern part and 70°~°80 (even to 90°) in the southern part of the fault zone. Three Neogene basins resulting from various mechanisms formed along the fault zone, e.g. Chan Nua, Lai Chau, and Dien Bien Phu basins. Field investigation and interpretation to the EMT images in the DBP fault zone reveals two stages of tectonic development of the fault zone: an early stage characterized by dextral and reverse-dextral strike-slip faulting and a late stage characterized by sinistral strike-slip faulting. Various offset degrees of sinistral slip, judged from the features of water system on both sides of the fault, suggest a relatively stronger activity of the western block that is so-called active block, compared to the eastern one. Based on the ETM image analysis and field survey in Jinping, Southern Yunnan, China and Laichau, Vietnam, we found that the northern DBP fault zone may be linked with the Sanjiahe fault zone, which is similar to the DBP fault zone in structural characteristics and evolution, and even approaches the Ailao Shan-Red River shear zone. Geometrically and kinematically, the DBP fault and the recently dextral Red River fault zone form a conjugate shear due to a stress field of EW extension and NS compression.
Image Transformation for IoT Time-Series Data: A Review

In the era of the Internet of Things (IoT), where smartphones, built-in systems, wireless sensors, and nearly every smart device connect through local networks or the internet, billions of smart things communicate with each other and generate vast amounts of time-series data. As IoT time-series data is high-dimensional and high-frequency, time-series classification or regression has been a challenging issue in IoT. Recently, deep learning algorithms have demonstrated superior performance results in time-series data classification in many smart and intelligent IoT applications. However, it is hard to explore the hidden dynamic patterns and trends in time-series. Recent studies show that transforming IoT data into images improves the performance of the learning model. In this paper, we present a review of these studies which use image transformation/encoding techniques in IoT domain. We examine the studies according to their encoding techniques, data types, and application areas. Lastly, we emphasize the challenges and future dimensions of image transformation.