Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic Structure Datasets for Machine Learning Potentials: Application to Moment Tensor Potentials of Magnesium and its Defects

We present a physically motivated strategy for the construction of training sets for transferable machine learning interatomic potentials. It is based on a systematic exploration of all possible space groups in random crystal structures, together with deformations of cell shape, size, and atomic positions. The resulting potentials turn out to be unbiased and generically applicable to studies of bulk defects without including any defect structures in the training set or employing any additional Active Learning. Using this approach we construct transferable potentials for pure Magnesium that reproduce the properties of hexagonal closed packed (hcp) and body centered cubic (bcc) polymorphs very well. In the process we investigate how diﬀerent types of training structures impact the properties and the predictive power of the resulting potential.
Smart Mat Used for Prevention of Hospital-Acquired Pressure Injuries

This work develops a smart mat for monitoring body positions. We use Velostat as a force sensor resistance (FSR) to construct a sensor matrix over the mat to receive the pressure distribution of the patient's body, and then upload the processed distribution information to the PC for data visualization through Arduino. Data visualization on the PC side is compiled through Python language to realize the functions of patient body pressure distribution monitoring, long-term pressure alarm and posture prediction. The purpose of this work is to relieve the work stress on medical staff caused by pressure injuries during the treatment and care of patients during the pandemic. This paper includes the literature review on similar previous works and combines the test results to design the structure and circuit of the smart mat.
ControlBurn: Nonlinear Feature Selection with Sparse Tree Ensembles

ControlBurn is a Python package to construct feature-sparse tree ensembles that support nonlinear feature selection and interpretable machine learning. The algorithms in this package first build large tree ensembles that prioritize basis functions with few features and then select a feature-sparse subset of these basis functions using a weighted lasso optimization criterion. The package includes visualizations to analyze the features selected by the ensemble and their impact on predictions. Hence ControlBurn offers the accuracy and flexibility of tree-ensemble models and the interpretability of sparse generalized additive models. ControlBurn is scalable and flexible: for example, it can use warm-start continuation to compute the regularization path (prediction error for any number of selected features) for a dataset with tens of thousands of samples and hundreds of features in seconds. For larger datasets, the runtime scales linearly in the number of samples and features (up to a log factor), and the package support acceleration using sketching. Moreover, the ControlBurn framework accommodates feature costs, feature groupings, and $\ell_0$-based regularizers. The package is user-friendly and open-source: its documentation and source code appear on https://pypi.org/project/ControlBurn/ and https://github.com/udellgroup/controlburn/.
Learning-based Autonomous Channel Access in the Presence of Hidden Terminals

We consider the problem of autonomous channel access (AutoCA), where a group of terminals tries to discover a communication strategy with an access point (AP) via a common wireless channel in a distributed fashion. Due to the irregular topology and the limited communication range of terminals, a practical challenge for AutoCA is the hidden terminal problem, which is notorious in wireless networks for deteriorating the throughput and delay performances. To meet the challenge, this paper presents a new multi-agent deep reinforcement learning paradigm, dubbed MADRL-HT, tailored for AutoCA in the presence of hidden terminals. MADRL-HT exploits topological insights and transforms the observation space of each terminal into a scalable form independent of the number of terminals. To compensate for the partial observability, we put forth a look-back mechanism such that the terminals can infer behaviors of their hidden terminals from the carrier sensed channel states as well as feedback from the AP. A window-based global reward function is proposed, whereby the terminals are instructed to maximize the system throughput while balancing the terminals' transmission opportunities over the course of learning. Extensive numerical experiments verified the superior performance of our solution benchmarked against the legacy carrier-sense multiple access with collision avoidance (CSMA/CA) protocol.
Pixel-level Correspondence for Self-Supervised Learning from Video

While self-supervised learning has enabled effective representation learning in the absence of labels, for vision, video remains a relatively untapped source of supervision. To address this, we propose Pixel-level Correspondence (PiCo), a method for dense contrastive learning from video. By tracking points with optical flow, we obtain a correspondence map which can be used to match local features at different points in time. We validate PiCo on standard benchmarks, outperforming self-supervised baselines on multiple dense prediction tasks, without compromising performance on image classification.
Fast High-Fidelity Gates for Galvanically-Coupled Fluxonium Qubits Using Strong Flux Modulation

Long coherence times, large anharmonicity and robust charge-noise insensitivity render ﬂuxonium qubits an interesting alternative to transmons. Recent experiments have demonstrated record coherence times for low-frequency ﬂuxonia. Here, we propose a galvanic-coupling scheme with ﬂux-tunable XX coupling. To implement a high-ﬁdelity entangling √ i SWAP gate, we modulate the strength of this coupling and devise variable-time identity gates to synchronize required single-qubit operations. Both types of gates are implemented using strong ac ﬂux drives, lasting for only a few drive periods. We employ a theoretical framework capable of capturing qubit dynamics beyond the rotating-wave approximation (RWA) as required for such strong drives. We predict an open-system ﬁdelity of F > 0 . 999 for the √ i SWAP gate under realistic conditions.
Probing the response of metals to low-frequency s-polarized evanescent fields

An experimental test for the response function of metals to the low-frequency s-polarized evanescent waves is proposed by measuring the lateral component of the magnetic field of an oscillating magnetic dipole spaced above a thick metallic plate. This suggestion is motivated by the fact that the Lifshitz theory using the Drude response function is in contradiction with high-precision measurements of the Casimir force performed at separations exceeding 150 nm. Analytic expressions for the lateral components of the magnetic field, which are fully determined by the s-polarized evanescent waves, are reported in terms of the reflection coefficients of the plate metal. Numerical computations are performed for the reasonable values of the experimental parameters for different models of the dielectric response. The resulting fields differ by the orders of magnitude depending on whether the Drude or plasma response function is used in computations. Thus, the measurement of the magnetic field will allow to discriminate between these two options. Possible applications of the obtained results are discussed.
Quantum chaos and thermalization in the two-mode Dicke model

We discuss the onset of quantum chaos and thermalization in the two-mode Dicke model, which describes the dipolar interaction between an ensemble of spins and two bosonic modes. The two-mode Dicke model exhibits normal to superradiant quantum phase transition with spontaneous breaking either of a discrete or continuous symmetry. We study the behaviour of the fidelity out-of-time-order correlator (FOTOC) derived from the Loschmidt echo signal in the quantum phases of the model. We show that the exponential growth of the FOTOC in the beginning of the time evolution cannot be related to a classical unstable point in the general case. Furthermore, we find that the collective spin observable in the two-mode Dicke model quickly saturates to its long-time average value, and shows very good agreement between its diagonal ensemble average and microcanonical average even for a small number of spins. We show that the temporal fluctuations of the expectation value of the collective spin observable around its average are small and decrease with the effective system size, which leads to thermalization of the spin system.
One for All: Simultaneous Metric and Preference Learning over Multiple Users

This paper investigates simultaneous preference and metric learning from a crowd of respondents. A set of items represented by $d$-dimensional feature vectors and paired comparisons of the form ``item $i$ is preferable to item $j$'' made by each user is given. Our model jointly learns a distance metric that characterizes the crowd's general measure of item similarities along with a latent ideal point for each user reflecting their individual preferences. This model has the flexibility to capture individual preferences, while enjoying a metric learning sample cost that is amortized over the crowd. We first study this problem in a noiseless, continuous response setting (i.e., responses equal to differences of item distances) to understand the fundamental limits of learning. Next, we establish prediction error guarantees for noisy, binary measurements such as may be collected from human respondents, and show how the sample complexity improves when the underlying metric is low-rank. Finally, we establish recovery guarantees under assumptions on the response distribution. We demonstrate the performance of our model on both simulated data and on a dataset of color preference judgements across a large number of users.
HTRON: Efficient Outdoor Navigation with Sparse Rewards via Heavy Tailed Adaptive Reinforce Algorithm

We present a novel approach to improve the performance of deep reinforcement learning (DRL) based outdoor robot navigation systems. Most, existing DRL methods are based on carefully designed dense reward functions that learn the efficient behavior in an environment. We circumvent this issue by working only with sparse rewards (which are easy to design), and propose a novel adaptive Heavy-Tailed Reinforce algorithm for Outdoor Navigation called HTRON. Our main idea is to utilize heavy-tailed policy parametrizations which implicitly induce exploration in sparse reward settings. We evaluate the performance of HTRON against Reinforce, PPO and TRPO algorithms in three different outdoor scenarios: goal-reaching, obstacle avoidance, and uneven terrain navigation. We observe in average an increase of 34.41% in terms of success rate, a 15.15% decrease in the average time steps taken to reach the goal, and a 24.9% decrease in the elevation cost compared to the navigation policies obtained by the other methods. Further, we demonstrate that our algorithm can be transferred directly into a Clearpath Husky robot to perform outdoor terrain navigation in real-world scenarios.
Rhythm and form in music: a complex systems approach

“Music is similar to language in that it is a temporal succession of articulated sounds that are more than just sound. They say something, often something humane. The higher the species of music, the more forcefully they say it. The succession of sounds is related to logic; there is a right and a wrong. But what is said cannot be abstracted from the music; it does not form a system of signs. ” ABSTRACT There has been an everlasting discussion around the concept of form in music. This work is motivated by such debate by using a complex systems framework in which we study the form as an emergent property of rhythm. Such a framework corresponds with the traditional notion of musical form and allows us to generalize this concept to more general shapes and structures in music. We develop the three following metrics of the rhythmic complexity of a musical piece and its parts: 1) the rhythmic heterogeneity , based on the permutation entropy [2], where high values indicate a wide variety of rhythmic patterns; 2) the syncopation , based on the distribution of on-beat onsets, where high values indicate a high proportion of off-the-beat notes; and 3) the component extractor , based on the communities of a visibility graph of the rhythmic figures over time [3], where we identify structural components that constitute the piece at a (to be explained) perceptual level. With the same parameters, our metrics are comparable within a piece or between pieces.
Aerobatic Trajectory Generation for a VTOL Fixed-Wing Aircraft Using Differential Flatness

This article proposes a novel algorithm for aerobatic trajectory generation for a vertical take-off and landing (VTOL) tailsitter flying wing aircraft. The algorithm differs from existing approaches for fixed-wing trajectory generation, as it considers a realistic six-degree-of-freedom (6-DOF) flight dynamics model, including aerodynamic equations. Using a global dynamics model enables the generation of aerobatics trajectories that exploit the entire flight envelope, allowing agile maneuvering through the stall regime, sideways uncoordinated flight, inverted flight, etc. The method uses the differential flatness property of the global tailsitter flying wing dynamics, which is derived in this work. By performing snap minimization in the differentially flat output space, a computationally efficient algorithm, suitable for online motion planning, is obtained. The algorithm is demonstrated in extensive flight experiments encompassing six aerobatic maneuvers, a time-optimal drone racing trajectory, and an airshowlike aerobatic sequence for three tailsitter aircraft.
SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera

With information from multiple input modalities, sensor fusion-based algorithms usually out-perform their single-modality counterparts in robotics. Camera and LIDAR, with complementary semantic and depth information, are the typical choices for detection tasks in complicated driving environments. For most camera-LIDAR fusion algorithms, however, the calibration of the sensor suite will greatly impact the performance. More specifically, the detection algorithm usually requires an accurate geometric relationship among multiple sensors as the input, and it is often assumed that the contents from these sensors are captured at the same time. Preparing such sensor suites involves carefully designed calibration rigs and accurate synchronization mechanisms, and the preparation process is usually done offline. In this work, a segmentation-based framework is proposed to jointly estimate the geometrical and temporal parameters in the calibration of a camera-LIDAR suite. A semantic segmentation mask is first applied to both sensor modalities, and the calibration parameters are optimized through pixel-wise bidirectional loss. We specifically incorporated the velocity information from optical flow for temporal parameters. Since supervision is only performed at the segmentation level, no calibration label is needed within the framework. The proposed algorithm is tested on the KITTI dataset, and the result shows an accurate real-time calibration of both geometric and temporal parameters.
Beyond Transfer Learning: Co-finetuning for Action Localisation

Transfer learning is the predominant paradigm for training deep networks on small target datasets. Models are typically pretrained on large ``upstream'' datasets for classification, as such labels are easy to collect, and then finetuned on ``downstream'' tasks such as action localisation, which are smaller due to their finer-grained annotations. In this paper, we question this approach, and propose co-finetuning -- simultaneously training a single model on multiple ``upstream'' and ``downstream'' tasks. We demonstrate that co-finetuning outperforms traditional transfer learning when using the same total amount of data, and also show how we can easily extend our approach to multiple ``upstream'' datasets to further improve performance. In particular, co-finetuning significantly improves the performance on rare classes in our downstream task, as it has a regularising effect, and enables the network to learn feature representations that transfer between different datasets. Finally, we observe how co-finetuning with public, video classification datasets, we are able to achieve state-of-the-art results for spatio-temporal action localisation on the challenging AVA and AVA-Kinetics datasets, outperforming recent works which develop intricate models.
Emergence of biological transportation networks as a self-regulated process

We study self-regulating processes modeling biological transportation networks. Firstly, we write the formal $L^2$-gradient flow for the symmetric tensor valued diffusivity $D$ of a broad class of entropy dissipations associated with a purely diffusive model. The introduction of a prescribed electric potential leads to the Fokker-Planck equation, for whose entropy dissipations we also investigate the formal $L^2$-gradient flow. We derive an integral formula for the second variation of the dissipation functional, proving convexity (in dependence of diffusivity tensor) for a quadratic entropy density modeling Joule heating. Finally, we couple in the Poisson equation for the electric potential obtaining the Poisson-Nernst-Planck system. The formal gradient flow of the associated entropy loss functional is derived, giving an evolution equation for $D$ coupled with two auxiliary elliptic PDEs.
Tightening discretization-based MILP models for the pooling problem using upper bounds on bilinear terms

Discretization-based methods have been proposed for solving nonconvex optimization problems with bilinear terms such as the pooling problem. These methods convert the original nonconvex optimization problems into mixed-integer linear programs (MILPs). In this paper we study tightening methods for these MILP models for the pooling problem, and derive valid constraints using upper bounds on bilinear terms. Computational results demonstrate the effectiveness of our methods in terms of reducing solution time.
The Spiderweb Protocluster is Being Magnetized by Its Central Radio Jet

We present deep broadband radio polarization observations of the Spiderweb radio galaxy (J1140-2629) in a galaxy protocluster at z = 2.16. These yield the most detailed polarimetric maps yet made of a high-redshift radio galaxy. The intrinsic polarization angles and Faraday rotation measures (RMs) reveal coherent magnetic fields spanning the ∼60 kpc length of the jets, while ∼50% fractional polarizations indicate these fields are well ordered. Source-frame ∣RM∣ values of ∼1000 rad m−2 are typical, and values up to ∼11,100 rad m−2 are observed. The Faraday-rotating gas cannot be well mixed with the synchrotron-emitting gas, or stronger-than-observed depolarization would occur. Nevertheless, an observed spatial coincidence between a localized ∣RM∣ enhancement of ∼1100 rad m−2 , a bright knot of Lyα emission, and a deviation of the radio jet provide direct evidence for vigorous jet-gas interaction. We detect a large-scale RM gradient totaling ∼1000 s rad m−2 across the width of the jet, suggesting a net clockwise (as viewed from the active galactic nuclei) toroidal magnetic field component exists at tens-of-kiloparsec scales, which we speculate may be associated with the operation of a Poynting–Robertson cosmic battery. We conclude the RMs are mainly generated in a sheath of hot gas around the radio jet, rather than the ambient foreground protocluster gas. The estimated magnetic field strength decreases by successive orders of magnitude going from the jet hotspots (∼90 μG) to the jet sheath (∼10 μG) to the ambient intracluster medium (∼1 μG). Synthesizing our results, we propose that the Spiderweb radio galaxy is actively magnetizing its surrounding protocluster environment, with possible implications for theories of the origin and evolution of cosmic magnetic fields.
UDRN: Unified Dimensional Reduction Neural Network for Feature Selection and Feature Projection

Dimensional reduction (DR) maps high-dimensional data into a lower dimensions latent space with minimized defined optimization objectives. The two independent branches of DR are feature selection (FS) and feature projection (FP). FS focuses on selecting a critical subset of dimensions but risks destroying the data distribution (structure). On the other hand, FP combines all the input features into lower dimensions space, aiming to maintain the data structure, but lacks interpretability and sparsity. Moreover, FS and FP are traditionally incompatible categories and have not been unified into an amicable framework. Therefore, we consider that the ideal DR approach combines both FS and FP into a unified end-to-end manifold learning framework, simultaneously performing fundamental feature discovery while maintaining the intrinsic relationships between data samples in the latent space. This paper proposes a unified framework named Unified Dimensional Reduction Network (UDRN) to integrate FS and FP in an end-to-end way. Furthermore, a novel network framework is designed to implement FS and FP tasks separately using a stacked feature selection network and feature projection network. In addition, a stronger manifold assumption and a novel loss function are proposed. Furthermore, the loss function can leverage the priors of data augmentation to enhance the generalization ability of the proposed UDRN. Finally, comprehensive experimental results on four image and four biological datasets, including very high-dimensional data, demonstrate the advantages of DRN over existing methods (FS, FP, and FS&FP pipeline), especially in downstream tasks such as classification and visualization.
DSTEA: Dialogue State Tracking with Entity Adaptive Pre-training

Dialogue state tracking (DST) is a core sub-module of a dialogue system, which aims to extract the appropriate belief state (domain-slot-value) from a system and user utterances. Most previous studies have attempted to improve performance by increasing the size of the pre-trained model or using additional features such as graph relations. In this study, we propose dialogue state tracking with entity adaptive pre-training (DSTEA), a system in which key entities in a sentence are more intensively trained by the encoder of the DST model. DSTEA extracts important entities from input dialogues in four ways, and then applies selective knowledge masking to train the model effectively. Although DSTEA conducts only pre-training without directly infusing additional knowledge to the DST model, it achieved better performance than the best-known benchmark models on MultiWOZ 2.0, 2.1, and 2.2. The effectiveness of DSTEA was veriﬁed through various comparative experiments with regard to the entity type and different adaptive settings.