Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Graph Revealers

Sparse graph recovery methods work well where the data follows their assumptions but often they are not designed for doing downstream probabilistic queries. This limits their adoption to only identifying connections among the input variables. On the other hand, the Probabilistic Graphical Models (PGMs) assume an underlying base graph between variables and learns a distribution over them. PGM design choices are carefully made such that the inference \&sampling algorithms are efficient. This brings in certain restrictions and often simplifying assumptions. In this work, we propose Neural Graph Revealers (NGRs), that are an attempt to efficiently merge the sparse graph recovery methods with PGMs into a single flow. The problem setting consists of an input data X with D features and M samples and the task is to recover a sparse graph showing connection between the features and learn a probability distribution over the D at the same time. NGRs view the neural networks as a `glass box' or more specifically as a multitask learning framework. We introduce `Graph-constrained path norm' that NGRs leverage to learn a graphical model that captures complex non-linear functional dependencies between the features in the form of an undirected sparse graph. Furthermore, NGRs can handle multimodal inputs like images, text, categorical data, embeddings etc. which is not straightforward to incorporate in the existing methods. We show experimental results of doing sparse graph recovery and probabilistic inference on data from Gaussian graphical models and a multimodal infant mortality dataset by Centers for Disease Control and Prevention.
Red giants in open clusters - XIV. Mean radial velocities for 1309 stars and 166 open clusters

Context. Radial velocities have proved to be an efficient method for membership determination if there are at least 2 or 3 red giants in a cluster. They are necessary for galactic studies, but are still missing for many open clusters. Aims. We present the final catalogues of a long-term observing programme performed with the two coravel spectrovelocimeters for red giants in open clusters. The main aims were to detect spectroscopic binaries and determine their orbital parameters, determine the membership, and compute mean velocities for the stars and open clusters. Methods. We computed weighted mean radial velocities for 1309 stars from 10 517 individual observations, including the systemic radial velocities from spectroscopic orbits and for cepheids. Results. The final results are contained in three catalogues collecting 10 517 individual radial velocities, mean radial velocities for 1309 red giants, and mean radial velocities for 166 open clusters among which there are 57 new determinations. We identified 891 members and 418 non-members. We discovered a total of 288 spectroscopic binaries, among which 57 are classified as nonmembers. In addition 27 stars were judged to be variable in radial velocities and they are all red supergiants. Conclusions. The present material, combined with recent absolute proper motions, will permit various investigation of the galactic distribution and space motions of a large sample of open clusters. However, the distance estimates still remain the weakest part of the necessary data. This paper is the last one in this series devoted to the study of red giants in open clusters based on radial velocities obtained with the coravel instruments.
Parallel Gradient Distribution in Unconstrained Optimization

A parallel version is proposed for a fundamental theorem of serial unconstrained optimization. The parallel theorem allows each of $k$ parallel processors to use simultaneously a different algorithm, such as a descent, Newton, quasi-Newton, or conjugate gradient algorithm. Each processor can perform one or many steps of a serial algorithm on a portion of the gradient of the objective function assigned to it, independently of the other processors. Eventually a synchronization step is performed which, for differentiable convex functions, consists of taking a strong convex combination of the $k$ points found by the $k$ processors. A more general synchronization step, applicable to convex as well as nonconvex functions, consists of taking the best point found by the $k$ processors or any point that is better. The fundamental result that we establish is that any accumulation point of the parallel algorithm is stationary for the nonconvex case and is a global solution for the convex case. Computational testing on the Thinking Machines CM-5 multiprocessor indicates a speedup of the order of the number of processors employed.
Abundance Ratios of OH/CO and HCO+/CO as Probes of the Cosmic-Ray Ionization Rate in Diffuse Clouds

The cosmic-ray ionization rate (CRIR, ζ 2) is one of the key parameters controlling the formation and destruction of various molecules in molecular clouds. However, the current most commonly used CRIR tracers, such as H3+ , OH+, and H2O+, are hard to detect and require the presence of background massive stars for absorption measurements. In this work, we propose an alternative method to infer the CRIR in diffuse clouds using the abundance ratios of OH/CO and HCO+/CO. We have analyzed the response of chemical abundances of CO, OH, and HCO+ on various environmental parameters of the interstellar medium in diffuse clouds and found that their abundances are proportional to ζ 2. Our analytic expressions give an excellent calculation of the abundance of OH for ζ 2 ≤ 10−15 s−1, which are potentially useful for modeling chemistry in hydrodynamical simulations. The abundances of OH and HCO+ were found to monotonically decrease with increasing density, while the CO abundance shows the opposite trend. With high-sensitivity absorption transitions of both CO (1–0) and (2–1) lines from Atacama Large Millimeter/submillimeter Array, we have derived the H2 number densities ( nH2 ) toward 4 lines of sight; assuming a kinetic temperature of T k = 50 K, we find a range of (0.14 ± 0.03–1.2 ± 0.1) × 102 cm−3. By comparing the observed and modeled HCO+/CO ratios, we find that ζ 2 in our diffuse gas sample is in the range of 1.0−1.0+14.8 × 10 −16–2.5−2.4+1.4 × 10−15 s−1. This is ∼2 times higher than the average value measured at higher extinction, supporting an attenuation of CRs as suggested by theoretical models.
LMSeg: Language-guided Multi-dataset Segmentation

It's a meaningful and attractive topic to build a general and inclusive segmentation model that can recognize more categories in various scenarios. A straightforward way is to combine the existing fragmented segmentation datasets and train a multi-dataset network. However, there are two major issues with multi-dataset segmentation: (1) the inconsistent taxonomy demands manual reconciliation to construct a unified taxonomy; (2) the inflexible one-hot common taxonomy causes time-consuming model retraining and defective supervision of unlabeled categories. In this paper, we investigate the multi-dataset segmentation and propose a scalable Language-guided Multi-dataset Segmentation framework, dubbed LMSeg, which supports both semantic and panoptic segmentation. Specifically, we introduce a pre-trained text encoder to map the category names to a text embedding space as a unified taxonomy, instead of using inflexible one-hot label. The model dynamically aligns the segment queries with the category embeddings. Instead of relabeling each dataset with the unified taxonomy, a category-guided decoding module is designed to dynamically guide predictions to each datasets taxonomy. Furthermore, we adopt a dataset-aware augmentation strategy that assigns each dataset a specific image augmentation pipeline, which can suit the properties of images from different datasets. Extensive experiments demonstrate that our method achieves significant improvements on four semantic and three panoptic segmentation datasets, and the ablation study evaluates the effectiveness of each component.
Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts

In reliable decision-making systems based on machine learning, models have to be robust to distributional shifts or provide the uncertainty of their predictions. In node-level problems of graph learning, distributional shifts can be especially complex since the samples are interdependent. To evaluate the performance of graph models, it is important to test them on diverse and meaningful distributional shifts. However, most graph benchmarks considering distributional shifts for node-level problems focus mainly on node features, while structural properties are also essential for graph problems. In this work, we propose a general approach for inducing diverse distributional shifts based on graph structure. We use this approach to create data splits according to several structural node properties: popularity, locality, and density. In our experiments, we thoroughly evaluate the proposed distributional shifts and show that they can be quite challenging for existing graph models. We also reveal that simple models often outperform more sophisticated methods on the considered structural shifts. Finally, our experiments provide evidence that there is a trade-off between the quality of learned representations for the base classification task under structural distributional shift and the ability to separate the nodes from different distributions using these representations.
LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints

Trajectory prediction for autonomous driving must continuously reason the motion stochasticity of road agents and comply with scene constraints. Existing methods typically rely on one-stage trajectory prediction models, which condition future trajectories on observed trajectories combined with fused scene information. However, they often struggle with complex scene constraints, such as those encountered at intersections. To this end, we present a novel method, called LAformer. It uses a temporally dense lane-aware estimation module to select only the top highly potential lane segments in an HD map, which effectively and continuously aligns motion dynamics with scene information, reducing the representation requirements for the subsequent attention-based decoder by filtering out irrelevant lane segments. Additionally, unlike one-stage prediction models, LAformer utilizes predictions from the first stage as anchor trajectories and adds a second-stage motion refinement module to further explore temporal consistency across the complete time horizon. Extensive experiments on Argoverse 1 and nuScenes demonstrate that LAformer achieves excellent performance for multimodal trajectory prediction.
Suppression of irradiation effects in gold-doped silicon detectors

Two sets of silicon detectors were irradiated with 1 MeV neutrons to different fluences and then characterized. The first batch were ordinary p - i - n photodiodes fabricated from high-resistivity silicon, while the second batch were gold-doped power diodes fabricated from silicon material initially of low resistivity . The increase in reverse leakage current after irradiation was found to be more in the former case than in the latter. The fluence dependence of the capacitance was much more pronounced in the p - i - n diodes than in the gold-doped diodes. Furthermore, photo current generation by optical means was less in the gold doped devices. All these results suggest that gold doping in silicon somewhat suppresses the effects of neutron irradiation.
Random forests for binary geospatial data

Binary geospatial data is commonly analyzed with generalized linear mixed models, specified with a linear fixed covariate effect and a Gaussian Process (GP)-distributed spatial random effect, relating to the response via a link function. The assumption of linear covariate effects is severely restrictive. Random Forests (RF) are increasingly being used for non-linear modeling of spatial data, but current extensions of RF for binary spatial data depart the mixed model setup, relinquishing inference on the fixed effects and other advantages of using GP. We propose RF-GP, using Random Forests for estimating the non-linear covariate effect and Gaussian Processes for modeling the spatial random effects directly within the generalized mixed model framework. We observe and exploit equivalence of Gini impurity measure and least squares loss to propose an extension of RF for binary data that accounts for the spatial dependence. We then propose a novel link inversion algorithm that leverages the properties of GP to estimate the covariate effects and offer spatial predictions. RF-GP outperforms existing RF methods for estimation and prediction in both simulated and real-world data. We establish consistency of RF-GP for a general class of $\beta$-mixing binary processes that includes common choices like spatial Mat\'ern GP and autoregressive processes.
A comprehensive model for the modal dynamics of vertical-cavity surface-emitting lasers

We present a quasi-3-D dynamic model of vertical-cavity surface-emitting lasers (VCSELs). The interdependent processes of carrier transport, heat generation and dissipation, and optical fields are solved self-consistently for each point in time and space. An effective index model is adopted for the evaluation of the optical fields in the complex layer structure. The inclusion of a temperature- and carrier-density-dependent refractive index, and its time dependence, allows us to study the evolution of the transverse optical field distributions under dynamic conditions. The model is applied to a typical index-guided structure with a 7-/spl mu/m oxide aperture. A direct comparison is made using "cold" cavity modes, which is a normal technique when modeling the dynamics of VCSELs. Significant discrepancies are demonstrated both at smalland large-signal modulation, which indicates the need of a more sophisticated model for accurately predicting and understanding the geometry-dependent modal evolution.
Effects of Thickness Effect and Rapid Thermal Annealing on pH Sensing Characteristics of Thin HfO2 Films Formed by Atomic Layer Deposition

In this article, thin hafnium oxide (HfO2) films deposited by atomic layer deposition (ALD) were investigated as a sensing layer on an electrolyte–insulator–semiconductor (EIS) structure for pH sensor applications. Compared with sputtering, ALD provides the possibility of shrinking the thickness of the HfO2 sensing layer down to 3.5 nm with a low drift coefficient (<0.2 mV/h). To increase the pH response, an additional rapid thermal annealing was performed on a 3.5-nm-thick ALD-HfO2 film. After annealing at 900 °C, the pH sensitivity could be effectively increased to that near the Nernstian response (59.6 mV/pH), and the drift coefficient (<1 mV/h) and hysteresis width (4.26 mV) are still sufficient. On the basis of the results of atomic force microscopy (AFM) analysis, the increase in surface roughness is possibly the reason for the increase in pH response after annealing. For compatibility with advanced complementary metal–oxide–semiconductor (CMOS) technology, the thin ALD-HfO2 film is a promising candidate for pH sensor fabrications.
Deep Imbalanced Time-series Forecasting via Local Discrepancy Density

Time-series forecasting models often encounter abrupt changes in a given period of time which generally occur due to unexpected or unknown events. Despite their scarce occurrences in the training set, abrupt changes incur loss that significantly contributes to the total loss. Therefore, they act as noisy training samples and prevent the model from learning generalizable patterns, namely the normal states. Based on our findings, we propose a reweighting framework that down-weights the losses incurred by abrupt changes and up-weights those by normal states. For the reweighting framework, we first define a measurement termed Local Discrepancy (LD) which measures the degree of abruptness of a change in a given period of time. Since a training set is mostly composed of normal states, we then consider how frequently the temporal changes appear in the training set based on LD. Our reweighting framework is applicable to existing time-series forecasting models regardless of the architectures. Through extensive experiments on 12 time-series forecasting models over eight datasets with various in-output sequence lengths, we demonstrate that applying our reweighting framework reduces MSE by 10.1% on average and by up to 18.6% in the state-of-the-art model.
Robot Behavior-Tree-Based Task Generation with Large Language Models

Nowadays, the behavior tree is gaining popularity as a representation for robot tasks due to its modularity and reusability. Designing behavior-tree tasks manually is time-consuming for robot end-users, thus there is a need for investigating automatic behavior-tree-based task generation. Prior behavior-tree-based task generation approaches focus on fixed primitive tasks and lack generalizability to new task domains. To cope with this issue, we propose a novel behavior-tree-based task generation approach that utilizes state-of-the-art large language models. We propose a Phase-Step prompt design that enables a hierarchical-structured robot task generation and further integrate it with behavior-tree-embedding-based search to set up the appropriate prompt. In this way, we enable an automatic and cross-domain behavior-tree task generation. Our behavior-tree-based task generation approach does not require a set of pre-defined primitive tasks. End-users only need to describe an abstract desired task and our proposed approach can swiftly generate the corresponding behavior tree. A full-process case study is provided to demonstrate our proposed approach. An ablation study is conducted to evaluate the effectiveness of our Phase-Step prompts. Assessment on Phase-Step prompts and the limitation of large language models are presented and discussed.
Priorities for more effective tech regulation

Ample research has demonstrated that compliance with data protection principles remains limited on the web and mobile. For example, almost none of the apps on the Google Play Store fulfil the minimum requirements regarding consent under EU and UK law, while most of them share tracking data with companies like Google/Alphabet and Facebook/Meta and would likely need to seek consent from their users. Indeed, recent privacy efforts and enforcement by Apple have had - in some regards - a more pronounced effect on apps' data practices than the EU's ambitious General Data Protection Regulation (GDPR). Given the current mismatch between the law on the books and data practices in reality, iterative changes to current legal practice will not be enough to meaningfully tame egregious data practices. Hence, this technical report proposes a range of priorities for academia, regulators and the interested public in order to move beyond the status quo.
New diamond anvil cell for single-crystal analysis

A new diamond anvil cell was devised for the single-crystal structure analyses under high pressure. The cell is characterized by large single-crystal diamond plates, which are used for the windows and fixed on the table planes of diamond anvils. The diamond plate windows have great advantages for single-crystal diffractometry, such as low x-ray absorption, much higher pressures up to 50 GPa, no powder rings from the window, and wide 2θ-angle (2θ<70°). Great progress of the structure analyses could be made by the combination with synchrotron radiation (SR). The short wavelength of λ=0.407 72 A (E=30.388 keV) of SR source with 8 GeV 100 mA at SPring-8 was used for the diffraction intensity measurement of SiO2 stishovite (rutile type). These advantages could measure the intensities of much higher order reflections and the precise electron density distribution as a function of pressure was discussed.
Efficient Ensemble Architecture for Multimodal Acoustic and Textual Embeddings in Punctuation Restoration using Time-Delay Neural Networks

Punctuation restoration plays an essential role in the post-processing procedure of automatic speech recognition, but model efﬁciency is a key requirement for this task. To that end, we present EfﬁcientPunct, an ensemble method with a multimodal time-delay neural network that out-performs the current best model by 1.0 F1 points, using less than a tenth of its parameters to process embeddings. We streamline a speech recognizer to efﬁciently output hidden layer latent vectors as acoustic embeddings for punctuation restoration, as well as BERT to extract meaningful text embeddings. By using forced alignment and temporal convolutions, we eliminate the need for multi-head attention-based fusion, greatly increasing computational efﬁciency but also raising performance. Efﬁcient-Punct sets a new state of the art, in terms of both performance and efﬁciency, with an ensemble that weights BERT’s purely language-based predictions slightly more than the multimodal network’s predictions.
Internet Explorer: Targeted Representation Learning on the Open Web

Modern vision models typically rely on fine-tuning general-purpose models pre-trained on large, static datasets. These general-purpose models only capture the knowledge within their pre-training datasets, which are tiny, out-of-date snapshots of the Internet -- where billions of images are uploaded each day. We suggest an alternate approach: rather than hoping our static datasets transfer to our desired tasks after large-scale pre-training, we propose dynamically utilizing the Internet to quickly train a small-scale model that does extremely well on the task at hand. Our approach, called Internet Explorer, explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset. It cycles between searching for images on the Internet with text queries, self-supervised training on downloaded images, determining which images were useful, and prioritizing what to search for next. We evaluate Internet Explorer across several datasets and show that it outperforms or matches CLIP oracle performance by using just a single GPU desktop to actively query the Internet for 30--40 hours. Results, visualizations, and videos at https://internet-explorer-ssl.github. io/
SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks

As the size of large language models continue to scale, so does the computational resources required to run it. Spiking Neural Networks (SNNs) have emerged as an energy-efficient approach to deep learning that leverage sparse and event-driven activations to reduce the computational overhead associated with model inference. While they have become competitive with non-spiking models on many computer vision tasks, SNNs have also proven to be more challenging to train. As a result, their performance lags behind modern deep learning, and we are yet to see the effectiveness of SNNs in language generation. In this paper, inspired by the Receptance Weighted Key Value (RWKV) language model, we successfully implement `SpikeGPT', a generative language model with binary, event-driven spiking activation units. We train the proposed model on two model variants: 45M and 216M parameters. To the best of our knowledge, SpikeGPT is the largest backpropagation-trained SNN model to date, rendering it suitable for both the generation and comprehension of natural language. We achieve this by modifying the transformer block to replace multi-head self attention to reduce quadratic computational complexity O(N^2) to linear complexity O(N) with increasing sequence length. Input tokens are instead streamed in sequentially to our attention mechanism (as with typical SNNs). Our preliminary experiments show that SpikeGPT remains competitive with non-spiking models on tested benchmarks, while maintaining 20x fewer operations when processed on neuromorphic hardware that can leverage sparse, event-driven activations.