Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonsymmorphic-Symmetry-Enforced Non-Hermitian Skin Effects

Crystal symmetries play an essential role in band structures of non-Hermitian Hamiltonian. In this letter, we propose a non-Hermitian skin effect (NHSE) enforced by nonsymmorphic symmetries. We show that the nonsymmorphic-symmetry-enforced NHSE inevitably occurs if a two-dimensional non-Hermitian system satisfies certain conditions characterized by nonsymmorphic symmetries. This NHSE can occur even in the presence of time-reversal symmetry. The nonsymmorphic-symmetry-enforced NHSE always occurs simultaneously with the closing of the point gap at zero energy. We also show that such a NHSE can occur in specific three-dimensional space groups with nonsymmorphic symmetries.
One-Shot Learning of Visual Path Navigation for Autonomous Vehicles

Autonomous driving presents many challenges due to the large number of scenarios the autonomous vehicle (AV) may encounter. End-to-end deep learning models are comparatively simplistic models that can handle a broad set of scenarios. However, end-to-end models require large amounts of diverse data to perform well. This paper presents a novel deep neural network that performs image-to-steering path navigation that helps with the data problem by adding one-shot learning to the system. Presented with a previously unseen path, the vehicle can drive the path autonomously after being shown the path once and without model retraining. In fact, the full path is not needed and images of the road junctions is sufficient. In-vehicle testing and offline testing are used to verify the performance of the proposed navigation and to compare different candidate architectures.
Berezinskii-Kosterlitz-Thouless-like localization-localization transitions in disordered two-dimensional quantized quadrupole insulators

Anderson localization transitions are usually referred to as quantum phase transitions from delocalized states to localized states in disordered systems. Here we report an unconventional ``Anderson localization transition'' in two-dimensional quantized quadrupole insulators. Such transitions are from symmetry-protected topological corner states to disorder-induced normal Anderson localized states that can be localized in the bulk, as well as at corners and edges. We show that these localization-localization transitions (transitions between two different localized states) can happen in both Hermitian and non-Hermitian quantized quadrupole insulators and investigate their criticality by finite-size scaling analysis of the corner density. The scaling analysis suggests that the correlation length of the phase transition, on the Anderson insulator side and near critical disorder $W_c$, diverges as $\xi(W)\propto \exp[\alpha/\sqrt{|W-W_c|}]$, a typical feature of Berezinskii-Kosterlitz-Thouless transitions. A map from the quantized quadrupole model to the quantum two-dimensional $XY$ model motivates why the localization-localization transitions are Berezinskii-Kosterlitz-Thouless type.
Graph-Based Matrix Completion Applied to Weather Data

Low-rank matrix completion is the task of recovering unknown entries of a matrix by assuming that the true matrix admits a good low-rank approximation. Sometimes additional information about the variables is known, and incorporating this information into a matrix completion model can lead to a better completion quality. We consider the situation where information between the column/row entities of the matrix is available as a weighted graph. In this framework, we address the problem of completing missing entries in air temperature data recorded by weather stations. We construct test sets by holding back data at locations that mimic real-life gaps in weather data. On such test sets, we show that adequate spatial and temporal graphs can significantly improve the accuracy of the completion obtained by graph-regularized low-rank matrix completion methods.
The MacWilliams Identity for the Hermitian Rank Metric

Error-correcting codes have an important role in data storage and transmission and in cryptography, particularly in the post-quantum era. Hermitian matrices over finite fields and equipped with the rank metric have the potential to offer enhanced security with greater efficiency in encryption and decryption. One crucial tool for evaluating the error-correcting capabilities of a code is its weight distribution and the MacWilliams Theorem has long been used to identify this structure of new codes from their known duals. Earlier papers have developed the MacWilliams Theorem for certain classes of matrices in the form of a functional transformation, developed using $q$-algebra, character theory and Generalised Krawtchouk polynomials, which is easy to apply and also allows for moments of the weight distribution to be found. In this paper, recent work by Kai-Uwe Schmidt on the properties of codes based on Hermitian matrices such as bounds on their size and the eigenvalues of their association scheme is extended by introducing a negative-$q$ algebra to establish a MacWilliams Theorem in this form together with some of its associated moments. The similarities in this approach and in the paper for the Skew-Rank metric by Friedlander et al. have been emphasised to facilitate future generalisation to any translation scheme.
Language to Rewards for Robotic Skill Synthesis

Large language models (LLMs) have demonstrated exciting progress in acquiring diverse new capabilities through in-context learning, ranging from logical reasoning to code-writing. Robotics researchers have also explored using LLMs to advance the capabilities of robotic control. However, since low-level robot actions are hardware-dependent and underrepresented in LLM training corpora, existing efforts in applying LLMs to robotics have largely treated LLMs as semantic planners or relied on human-engineered control primitives to interface with the robot. On the other hand, reward functions are shown to be flexible representations that can be optimized for control policies to achieve diverse tasks, while their semantic richness makes them suitable to be specified by LLMs. In this work, we introduce a new paradigm that harnesses this realization by utilizing LLMs to define reward parameters that can be optimized and accomplish variety of robotic tasks. Using reward as the intermediate interface generated by LLMs, we can effectively bridge the gap between high-level language instructions or corrections to low-level robot actions. Meanwhile, combining this with a real-time optimizer, MuJoCo MPC, empowers an interactive behavior creation experience where users can immediately observe the results and provide feedback to the system. To systematically evaluate the performance of our proposed method, we designed a total of 17 tasks for a simulated quadruped robot and a dexterous manipulator robot. We demonstrate that our proposed method reliably tackles 90% of the designed tasks, while a baseline using primitive skills as the interface with Code-as-policies achieves 50% of the tasks. We further validated our method on a real robot arm where complex manipulation skills such as non-prehensile pushing emerge through our interactive system.
Improved Measurements of the IXPE Crab Polarization

X-ray polarization from the Imaging X-ray Polarimetry Explorer (IXPE) provides an important new probe of the geometry of the pulsar emission zone and of particle acceleration in the surrounding pulsar wind nebula. However, with IXPE's modest ∼20″–30″ spatial resolution, separation of the pulsar signal from the nebula is a challenge. Conventional analysis defines an “off” phase window as pure nebular emission and subtracts its polarization to isolate the phase-varying pulsar (on-off fitting). We present a more sensitive scheme that uses external measurements of the nebula structure and the pulsar’s light curve to isolate their contributions to the phase- and spatially-varying polarization via least-squares regression (simultaneous fitting). Tests with simulation data show ∼30% improvement in pulse phase polarization uncertainties, decreased background systematics, and substantially improved nebular polarization maps. Applying simultaneous fitting to early IXPE Crab data extracts additional phase bins with significant polarization. These bins show interesting departures from the well-known optical polarization sweeps, although additional exposure will be needed for precise model confrontation.
360TripleView: 360-Degree Video View Management System Driven by Convergence Value of Viewing Preferences

360-degree video has become increasingly popular in content consumption. However, finding the viewing direction for important content within each frame poses a significant challenge. Existing approaches rely on either viewer input or algorithmic determination to select the viewing direction, but neither mode consistently outperforms the other in terms of content-importance. In this paper, we propose 360TripleView, the first view management system for 360-degree video that automatically infers and utilizes the better view mode for each frame, ultimately providing viewers with higher content-importance views. Through extensive experiments and a user study, we demonstrate that 360TripleView achieves over 90% accuracy in inferring the better mode and significantly enhances content-importance compared to existing methods.
Floaters No More: Radiance Field Gradient Scaling for Improved Near-Camera Training

NeRF acquisition typically requires careful choice of near planes for the different cameras or suffers from background collapse, creating floating artifacts on the edges of the captured scene. The key insight of this work is that background collapse is caused by a higher density of samples in regions near cameras. As a result of this sampling imbalance, near-camera volumes receive significantly more gradients, leading to incorrect density buildup. We propose a gradient scaling approach to counter-balance this sampling imbalance, removing the need for near planes, while preventing background collapse. Our method can be implemented in a few lines, does not induce any significant overhead, and is compatible with most NeRF implementations.
M giants with IGRINS. II. Chemical evolution of fluorine at high metallicities

The origin and evolution of fluorine in the Milky Way galaxy is still in debate. In particular, the increase of the [F/Fe] in metal-rich stars found from near-IR HF-lines is challenging to explain theoretically. We determine the fluorine abundances from 50 M giants in the solar neighbourhood spanning a broad range of metallicities (-0.9<[Fe/H]<0.25 dex). These stars are cool enough to have an array of HF lines in the K band. We observed the stars with the IGRINS and investigate each of ten HF molecular lines in detail. Based on a detailed line-by-line analysis of ten HF lines, we find that the R19, R18 and R16 lines should primarily be used for abundance analysis. The R15, R14 and R13 lines can also be used, but the trends based on these lines show increasing dependencies with the stellar parameters. The strongest HF lines, namely R12, R11, R9 and R7 should be avoided since the abundances from them show significant trends with the stellar parameters, and a high sensitivity to variations in the microturbulence, especially for coolest metal-rich stars. This leads to a huge scatter and high fluorine abundances for supersolar metallicity stars, not seen in the trends from the weaker lines for the same stars. When estimating the final mean fluorine abundance trend versus metallicity, we neglect the fluorine abundances from the four strongest lines (R7, R9, R11 and R12) for all stars and use only those derived from R16, R18, and R19 for the coolest metal-rich stars. We confirm the flat trend of [F/Fe] found in other studies in the metallicity range of -1.0<[Fe/H]<0.0. We also find a slight enhancement at supersolar metallicities (0<[Fe/H]<0.15) but we cannot confirm the upward trend seen at [Fe/H]>0.25. We need more observations of M giants at super solar metallicities with a spectrometer like IGRINS to confirm if the metal-rich fluorine abundance upturn is real or not.
Entanglement in living systems

Many organisms exhibit branching morphologies that twist around each other and become entangled. Entanglement occurs when different objects interlock with each other, creating complex and often irreversible configurations. This physical phenomenon is well-studied in non-living materials, such as granular matter, polymers, and wires, where it has been shown that entanglement is highly sensitive to the geometry of the component parts. However, entanglement is not yet well understood in living systems, despite its presence in many organisms. In fact, recent work has shown that entanglement can evolve rapidly, and play a crucial role in the evolution of tough, macroscopic multicellular groups. Here, through a combination of experiments, simulations, and numerical analyses, we show that growth generically facilitates entanglement for a broad range of geometries. We find that experimentally grown entangled branches can be difficult or even impossible to disassemble through translation and rotation of rigid components, suggesting that there are many configurations of branches that growth can access that agitation cannot. We use simulations to show that branching trees readily grow into entangled configurations. In contrast to non-growing entangled materials, these trees entangle for a broad range of branch geometries. We thus propose that entanglement via growth is largely insensitive to the geometry of branched-trees, but instead will depend sensitively on time scales, ultimately achieving an entangled state once sufficient growth has occurred. We test this hypothesis in experiments with snowflake yeast, a model system of undifferentiated, branched multicellularity, showing that lengthening the time of growth leads to entanglement, and that entanglement via growth can occur for a wide range of geometries. Taken together, our work demonstrates that entanglement is more readily achieved in living systems than in their non-living counterparts, providing a widely-accessible and powerful mechanism for the evolution of novel biological material properties.
A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation

This paper presents a solution to the cross-domain adaptation problem for 2D surgical image segmentation, explicitly considering the privacy protection of distributed datasets belonging to different centers. Deep learning architectures in medical image analysis necessitate extensive training data for better generalization. However, obtaining sufficient diagnostic and surgical data is still challenging, mainly due to the inherent cost of data curation and the need of experts for data annotation. Moreover, increased privacy and legal compliance concerns can make data sharing across clinical sites or regions difficult. Another ubiquitous challenge the medical datasets face is inevitable domain shifts among the collected data at the different centers. To this end, we propose a Client-server deep federated architecture for cross-domain adaptation. A server hosts a set of immutable parameters common to both the source and target domains. The clients consist of the respective domain-specific parameters and make requests to the server while learning their parameters and inferencing. We evaluate our framework in two benchmark datasets, demonstrating applicability in computer-assisted interventions for endoscopic polyp segmentation and diagnostic skin lesion detection and analysis. Our extensive quantitative and qualitative experiments demonstrate the superiority of the proposed method compared to competitive baseline and state-of-the-art methods. Codes are available at: https://github.com/thetna/distributed-da
JWST Peers into the Class I Protostar TMC1A: Atomic Jet and Spatially Resolved Dissociative Shock Region

Outflows and winds launched from young stars play a crucial role in the evolution of protostars and the early stages of planet formation. However, the specific details of the mechanism behind these phenomena, including how they affect the protoplanetary disk structure, are still debated. We present JWST NIRSpec integral field unit observations of atomic and H2 lines from 1 to 5.1 μm toward the low-mass protostar TMC1A. For the first time, a collimated atomic jet is detected from TMC1A in the [Fe ii] line at 1.644 μm along with corresponding extended H2 2.12 μm emission. Toward the protostar, we detected spectrally broad H i and He i emissions with velocities up to 300 km s−1 that can be explained by a combination of protostellar accretion and a wide-angle wind. The 2 μm continuum dust emission, H i, He i, and O i all show emission from the illuminated outflow cavity wall and scattered line emission. These observations demonstrate the potential of JWST to characterize and reveal new information about the hot inner regions of nearby protostars; in this case, a previously undetected atomic wind and ionized jet in a well-known outflow.
The role of feature correlation in conceptual processing: Evidence from the activation of false features

• We tested young and mature participants to investigate the influence of the strength of correlation of a false feature with the features in the presented concept. We used slower and more speeded versions to test for the consistency of that influence. • The strength of the correlation predicted rejection RTs and errors rates for both young and mature participants in both feature verification tasks. The results indicate that all features that reliably co-occur with other features are activated and that this occurs not just within the features of the presented concept. • This is evidence for spreading activation between features that is strong enough to interfere with participants’ ability to reject false features. The degree of spreading activation is determined by the co-occurrence of features which is the key factor underlying correlation, providing support for distributed feature based models.
OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models

Diffusion probabilistic models (DPMs) are a new class of generative models that have achieved state-of-the-art generation quality in various domains. Despite the promise, one major drawback of DPMs is the slow generation speed due to the large number of neural network evaluations required in the generation process. In this paper, we reveal an overlooked dimension -- model schedule -- for optimizing the trade-off between generation quality and speed. More specifically, we observe that small models, though having worse generation quality when used alone, could outperform large models in certain generation steps. Therefore, unlike the traditional way of using a single model, using different models in different generation steps in a carefully designed \emph{model schedule} could potentially improve generation quality and speed \emph{simultaneously}. We design OMS-DPM, a predictor-based search algorithm, to optimize the model schedule given an arbitrary generation time budget and a set of pre-trained models. We demonstrate that OMS-DPM can find model schedules that improve generation quality and speed than prior state-of-the-art methods across CIFAR-10, CelebA, ImageNet, and LSUN datasets. When applied to the public checkpoints of the Stable Diffusion model, we are able to accelerate the sampling by 2$\times$ while maintaining the generation quality.
MMASD: A Multimodal Dataset for Autism Intervention Analysis

Autism spectrum disorder (ASD) is a developmental disorder characterized by significant impairments in social communication and difficulties perceiving and presenting communication signals. Machine learning techniques have been widely used to facilitate autism studies and assessments. However, computational models are primarily concentrated on very specific analysis and validated on private, non-public datasets in the autism community, which limits comparisons across models due to privacy-preserving data-sharing complications. This work presents a novel open source privacy-preserving dataset, MMASD as a MultiModal ASD benchmark dataset, collected from play therapy interventions for children with autism. The MMASD includes data from 32 children with ASD, and 1,315 data samples segmented from more than 100 hours of intervention recordings. To promote the privacy of children while offering public access, each sample consists of four privacy-preserving modalities, some of which are derived from original videos: (1) optical flow, (2) 2D skeleton, (3) 3D skeleton, and (4) clinician ASD evaluation scores of children. MMASD aims to assist researchers and therapists in understanding children’s cognitive status, monitoring their progress during therapy, and customizing the treatment plan accordingly. It also inspires downstream social tasks such as action quality assessment and interpersonal synchrony estimation. The dataset is publicly accessible via the MMASD project website.
Autothermotaxis of volatile drops

When a drop of a volatile liquid is deposited on a uniformly heated wettable, thermally conducting substrate, one expects to see it spread into a thin film and evaporate. Contrary to this intuition, due to thermal Marangoni contraction the deposited drop contracts into a spherical-cap-shaped puddle, with a finite apparent contact angle. Strikingly, this contracted droplet, above a threshold temperature, well below the boiling point of the liquid, starts to spontaneously move on the substrate in an apparently erratic way. We describe and quantify this self-propulsion of the volatile drop. It arises due to spontaneous symmetry breaking of thermal-Marangoni convection, which is induced by the non-uniform evaporation of the droplet. Using infra-red imaging, we reveal the characteristic interfacial flow patterns associated with the Marangoni convection in the evaporating drop. A scaling relation describes the correlation between the moving velocity of the drop and the apparent contact angle, both of which increase with the substrate temperature.
Evaluation and Optimization of Gradient Compression for Distributed Deep Learning

To accelerate distributed training, many gradient compression methods have been proposed to alleviate the communication bottleneck in synchronous stochastic gradient descent (S-SGD), but their efficacy in real-world applications still remains unclear. In this work, we first evaluate the efficiency of three representative compression methods (quantization with Sign-SGD, sparsification with Top-k SGD, and low-rank with Power-SGD) on a 32-GPU cluster. The results show that they cannot always outperform well-optimized S-SGD or even worse due to their incompatibility with three key system optimization techniques (all-reduce, pipelining, and tensor fusion) in S-SGD. To this end, we propose a novel gradient compression method, called alternate compressed Power-SGD (ACP-SGD), which alternately compresses and communicates low-rank matrices. ACP-SGD not only significantly reduces the communication volume, but also enjoys the three system optimizations like S-SGD. Compared with Power-SGD, the optimized ACP-SGD can largely reduce the compression and communication overheads, while achieving similar model accuracy. In our experiments, ACP-SGD achieves an average of 4.06× and 1.43× speedups over S-SGD and Power-SGD, respectively, and it consistently outperforms other baselines across different setups (from 8 GPUs to 64 GPUs and from 1Gb/s Ethernet to 100Gb/s InfiniBand).