Miracle-dz/UniFAR-FTUs huggingface dataset:
Operating characteristics and extensions of the false discovery rate procedure

Summary. We investigate the operating characteristics of the Benjamini–Hochberg false discovery rate procedure for multiple testing. This is a distribution‐free method that controls the expected fraction of falsely rejected null hypotheses among those rejected. The paper provides a framework for understanding more about this procedure. We first study the asymptotic properties of the `deciding point' D that determines the critical p‐value. From this, we obtain explicit asymptotic expressions for a particular risk function. We introduce the dual notion of false non‐rejections and we consider a risk function that combines the false discovery rate and false non‐rejections. We also consider the optimal procedure with respect to a measure of conditional risk.
Mini-Magnetospheric Plasma Propulsion: Tapping the energy of the solar wind for spacecraft propulsion

Mini-Magnetospheric Plasma Propulsion is a potentially revolutionary plasma propulsion concept that could enable spacecraft to travel out of the solar system at unprecedented speeds of 50 to 80 km s -1 or could enable travel between the planets for low power requirements of ∼ 1 kW per 100 kg of payload and 0.5 kg fuel consumption per day for acceleration periods of several days to a few weeks. The high efficiency and specific impulse attained by the system are due to its utilization of ambient energy, in this case the energy of the solar wind, to provide the enhanced thrust. Coupling to the solar wind is produced through a large-scale magnetic bubble or mini-magnetosphere generated by the injection of plasma into the magnetic field supported by solenoid coils on the spacecraft. This inflation is driven by electromagnetic processes, so that the material and deployment problems associated with mechanical sails are eliminated.
Comparing Trajectory and Vision Modalities for Verb Representation

Three-dimensional trajectories, or the 3D position and rotation of objects over time, have been shown to encode key aspects of verb semantics (e.g., the meanings of roll vs. slide). However, most multimodal models in NLP use 2D images as representations of the world. Given the importance of 3D space in formal models of verb semantics, we expect that these 2D images would result in impoverished representations that fail to capture nuanced differences in meaning. This paper tests this hypothesis directly in controlled experiments. We train self-supervised image and trajectory encoders, and then evaluate them on the extent to which each learns to differentiate verb concepts. Contrary to our initial expectations, we find that 2D visual modalities perform similarly well to 3D trajectories. While further work should be conducted on this question, our initial findings challenge the conventional wisdom that richer environment representations necessarily translate into better representation learning for language.
Learning to Transfer In‐Hand Manipulations Using a Greedy Shape Curriculum

In‐hand object manipulation is challenging to simulate due to complex contact dynamics, non‐repetitive finger gaits, and the need to indirectly control unactuated objects. Further adapting a successful manipulation skill to new objects with different shapes and physical properties is a similarly challenging problem. In this work, we show that natural and robust in‐hand manipulation of simple objects in a dynamic simulation can be learned from a high quality motion capture example via deep reinforcement learning with careful designs of the imitation learning problem. We apply our approach on both single‐handed and two‐handed dexterous manipulations of diverse object shapes and motions. We then demonstrate further adaptation of the example motion to a more complex shape through curriculum learning on intermediate shapes morphed between the source and target object. While a naive curriculum of progressive morphs often falls short, we propose a simple greedy curriculum search algorithm that can successfully apply to a range of objects such as a teapot, bunny, bottle, train, and elephant.
Few-shot Multimodal Multitask Multilingual Learning

While few-shot learning as a transfer learning paradigm has gained significant traction for scenarios with limited data, it has primarily been explored in the context of building unimodal and unilingual models. Furthermore, a significant part of the existing literature in the domain of few-shot multitask learning perform in-context learning which requires manually generated prompts as the input, yielding varying outcomes depending on the level of manual prompt-engineering. In addition, in-context learning suffers from substantial computational, memory, and storage costs which eventually leads to high inference latency because it involves running all of the prompt's examples through the model every time a prediction is made. In contrast, methods based on the transfer learning via the fine-tuning paradigm avoid the aforementioned issues at a one-time cost of fine-tuning weights on a per-task basis. However, such methods lack exposure to few-shot multimodal multitask learning. In this paper, we propose few-shot learning for a multimodal multitask multilingual (FM3) setting by adapting pre-trained vision and language models using task-specific hypernetworks and contrastively fine-tuning them to enable few-shot learning. FM3's architecture combines the best of both worlds of in-context and fine-tuning based learning and consists of three major components: (i) multimodal contrastive fine-tuning to enable few-shot learning, (ii) hypernetwork task adaptation to perform multitask learning, and (iii) task-specific output heads to cater to a plethora of diverse tasks. FM3 learns the most prominent tasks in the vision and language domains along with their intersections, namely visual entailment (VE), visual question answering (VQA), and natural language understanding (NLU) tasks such as neural entity recognition (NER) and the GLUE benchmark including QNLI, MNLI, QQP, and SST-2.
Structured barycentric forms for interpolation-based data-driven reduced modeling of second-order systems

An essential tool in data-driven modeling of dynamical systems from frequency response measurements is the barycentric form of the underlying rational transfer function. In this work, we propose structured barycentric forms for modeling dynamical systems with second-order time derivatives using their frequency domain input-output data. By imposing a set of interpolation conditions, the systems' transfer functions are rewritten in different barycentric forms using different parametrizations. Loewner-like algorithms are developed for the explicit computation of second-order systems from data based on the developed barycentric forms. Numerical experiments show the performance of these new structured data driven modeling methods compared to other interpolation-based data-driven modeling techniques from the literature.
Integrating Image Features with Convolutional Sequence-to-sequence Network for Multilingual Visual Question Answering

Visual Question Answering (VQA) is a task that requires computers to give correct answers for the input questions based on the images. This task can be solved by humans with ease but is a challenge for computers. The VLSP2022-EVJVQA shared task carries the Visual Question Answering task in the multilingual domain on a newly released dataset: UIT-EVJVQA, in which the questions and answers are written in three different languages: English, Vietnamese and Japanese. We approached the challenge as a sequence-to-sequence learning task, in which we integrated hints from pre-trained state-of-the-art VQA models and image features with Convolutional Sequence-to-Sequence network to generate the desired answers. Our results obtained up to 0.3442 by F1 score on the public test set, 0.4210 on the private test set, and placed 3rd in the competition.
AptSim2Real: Approximately-Paired Sim-to-Real Image Translation

Advancements in graphics technology has increased the use of simulated data for training machine learning models. However, the simulated data often differs from real-world data, creating a distribution gap that can decrease the efficacy of models trained on simulation data in real-world applications. To mitigate this gap, sim-to-real domain transfer modifies simulated images to better match real-world data, enabling the effective use of simulation data in model training. Sim-to-real transfer utilizes image translation methods, which are divided into two main categories: paired and unpaired image-to-image translation. Paired image translation requires a perfect pixel match, making it difficult to apply in practice due to the lack of pixel-wise correspondence between simulation and real-world data. Unpaired image translation, while more suitable for sim-to-real transfer, is still challenging to learn for complex natural scenes. To address these challenges, we propose a third category: approximately-paired sim-to-real translation, where the source and target images do not need to be exactly paired. Our approximately-paired method, AptSim2Real, exploits the fact that simulators can generate scenes loosely resembling real-world scenes in terms of lighting, environment, and composition. Our novel training strategy results in significant qualitative and quantitative improvements, with up to a 24% improvement in FID score compared to the state-of-the-art unpaired image-translation methods.
RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration

Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale registration methods are rarely explored. Challenges mainly arise from the huge point number, complex distribution, and outliers of outdoor LiDAR scans. In addition, most existing registration works generally adopt a two-stage paradigm: They first find correspondences by extracting discriminative local features and then leverage estimators (eg. RANSAC) to filter outliers, which are highly dependent on well-designed descriptors and post-processing choices. To address these problems, we propose an end-to-end transformer network (RegFormer) for large-scale point cloud alignment without any further post-processing. Specifically, a projection-aware hierarchical transformer is proposed to capture long-range dependencies and filter outliers by extracting point features globally. Our transformer has linear complexity, which guarantees high efficiency even for large-scale scenes. Furthermore, to effectively reduce mismatches, a bijective association transformer is designed for regressing the initial transformation. Extensive experiments on KITTI and NuScenes datasets demonstrate that our RegFormer achieves competitive performance in terms of both accuracy and efficiency. Codes are available at https://github.com/IRMVLab/RegFormer.
Self-Supervised Representations for Singing Voice Conversion

A singing voice conversion model converts a song in the voice of an arbitrary source singer to the voice of a target singer. Recently, methods that leverage self-supervised audio representations such as HuBERT and Wav2Vec 2.0 have helped further the state-of-the-art. Though these methods produce more natural and melodic singing outputs, they often rely on confusion and disentanglement losses to render the self-supervised representations speaker and pitch-invariant. In this paper, we circumvent disentanglement training and propose a new model that leverages ASR fine-tuned self-supervised representations as inputs to a HiFi-GAN neural vocoder for singing voice conversion. We experiment with different f0 encoding schemes and show that an f0 harmonic generation module that uses a parallel bank of transposed convolutions (PBTC) alongside ASR fine-tuned Wav2Vec 2.0 features results in the best singing voice conversion quality. Additionally, the model is capable of making a spoken voice sing. We also show that a simple f0 shifting scheme during inference helps retain singer identity and bolsters the performance of our singing voice conversion model. Our results are backed up by extensive MOS studies that compare different ablations and baselines.
Evaluating Transformer Models and Human Behaviors on Chinese Character Naming

Abstract Neural network models have been proposed to explain the grapheme-phoneme mapping process in humans for many alphabet languages. These models not only successfully learned the correspondence of the letter strings and their pronunciation, but also captured human behavior in nonce word naming tasks. How would the neural models perform for a non-alphabet language (e.g., Chinese) unknown character task? How well would the model capture human behavior? In this study, we first collect human speakers’ answers on unknown Character naming tasks and then evaluate a set of transformer models by comparing their performance with human behaviors on an unknown Chinese character naming task. We found that the models and humans behaved very similarly, that they had similar accuracy distribution for each character, and had a substantial overlap in answers. In addition, the models’ answers are highly correlated with humans’ answers. These results suggested that the transformer models can capture humans’ character naming behavior well.1
Task-Oriented Explainable Semantic Communication Based on Semantic Triplets

Task-oriented semantic communication has garnered increasing attention due to its ability to reduce the amount of transmitted data without sacrificing task performance. While existing works have focused on designing advanced semantic communication architectures and efficient semantic codecs, challenges such as explainable and efficient semantic representation methods, and knowledge-based semantic compression algorithms have yet to be explored. These challenges have hindered the widespread adoption of semantic communication. To address these challenges, this paper proposes a novel Semantic Communication system based on Semantic Triplets (SCST), in which semantics are represented through explainable semantic triplets. A novel semantic extraction method is proposed to convert transmitted text into semantic triplets, where syntactic dependency analysis is introduced to improve semantic completeness. Furthermore, a semantic filtering method is designed to compress duplicate and task-irrelevant semantic triplets, making use of prior knowledge from the knowledge base. The filtered semantic triplets are then encoded and transmitted wirelessly to complete intelligent tasks at the receiver. To verify the effectiveness of the proposed SCST, it is applied to sentiment analysis and question-answering tasks, with semantic encoders and decoders designed for each task. Experimental results show that the proposed SCST achieves at least 43.5% and 52% accuracy gains, respectively, compared to traditional communication method. These results demonstrate that the proposed SCST can significantly improve semantic communication performance.
Boundary elements with equilibrium satisfaction—a consistent formulation for dynamic problems considering non‐linear effects

This paper presents a boundary element (BE) formulation with complete dynamic equilibrium satisfaction with respect to the co‐ordinate axis directions and moments, including inertial forces. The new procedure is quite general and very easy to implement into BE existing codes. All the required expressions for both static and dynamic formulations are shown in the text and two dynamic examples, which take into account linear and non‐linear material behaviour, are presented at the end of the paper, showing the potentialities of the proposed methodology. Copyright © 2005 John Wiley & Sons, Ltd.
Unveiling the chemical fingerprint of phosphorus-rich stars. I. In the infrared region of APOGEE-2

The origin of phosphorus, one of the essential elements for life on Earth, is currently unknown. Prevalent models of Galactic chemical evolution (GCE) underestimate the amount of P compared to observations. The recently discovered P-rich ([P/Fe]>1 dex) and metal-poor giants further challenge current theories on stellar nucleosynthesis. Since the observed stars are low-mass giants, our primary goal is to find clues on their progenitor. By increasing the number of known P-rich stars, we aim to narrow down a reliable chemical abundance pattern and to place robust constraints on the responsible nucleosynthetic mechanism. In the long term, identifying the progenitor of the P-rich stars may contribute to the search for the source of P in our Galaxy. We performed a detailed chemical abundance analysis based on the H-band spectra from APOGEE-2 (DR17). Employing the BACCHUS code, we measured the abundances of 13 elements in the sample, which is mainly composed of a recent collection of Si-enhanced giants. We also analyzed the orbital motions and compared the abundance results to possible nucleosynthetic formation scenarios, and also to detailed GCE models. We enlarged the sample of confirmed P-rich stars from 16 to 78 giants, which represents the largest sample of P-rich stars to date. Significant enhancements in O, Al, Si and Ce, as well as systematic correlations among the elements, unveil the chemical fingerprint of the P-rich stars. The high Mg and C+N found in some of the P-rich stars with respect to P-normal stars is not confirmed over the full sample. Strikingly, the strong over-abundance in the $\alpha$-element Si is accompanied by normal Ca and S abundances. Our analysis of the orbital motion showed that the P-rich stars do not belong to a specific sub-population. In addition, we confirm that the majority of the sample stars are not part of binary systems.
The Carbon Star DY Persei May Be a Cool R Coronae Borealis Variable

Optical and near-IR photometry suggests that the carbon star DY Persei exhibits fadings similar to those of R Coronae Borealis (RCB) variables. Photometric surveys of the Galaxy and Magellanic Clouds uncovered new DY Per variables with infrared photometry identifying them with cool carbon stars, perhaps, with an unusual tendency to shed mass. In an attempt to resolve DY Per’s identity crisis—a cool carbon giant or a cool RCB variable? —we analyze a high-resolution IGRINS H&K-band spectrum of DY Per. The CO first-overtone bands in the K band of DY Per show a high abundance of 18O such that 16O/18O = 4 ± 1, a ratio sharply at odds with published results for regular cool carbon giants with 16O/18O ∼ 1000 but this exceptionally low ratio is characteristic of RCB variables and HdC stars. This similarity suggests that DY Per indeed may be a cool RCB variable. Current opinion considers RCB variables to result from the merger of a He onto a CO white dwarf; observed abundances of these H-deficient stars including the exceptionally low 16O/18O ratios are in fair accord with predicted compositions for white dwarf merger products. An H-deficiency for DY Per is not directly observable but is suggested from the strength of an HF line and an assumption that F may be overabundant, as observed and predicted for RCB stars.
Distribution-restrained Softmax Loss for the Model Robustness

Recently, the robustness of deep learning models has received widespread attention, and various methods for improving model robustness have been proposed, including adversarial training, model architecture modification, design of loss functions, certified defenses, and so on. However, the principle of the robustness to attacks is still not fully understood, also the related research is still not sufficient. Here, we have identified a significant factor that affects the robustness of models: the distribution characteristics of softmax values for non-real label samples. We found that the results after an attack are highly correlated with the distribution characteristics, and thus we proposed a loss function to suppress the distribution diversity of softmax. A large number of experiments have shown that our method can improve robustness without significant time consumption.
Preventing Dimensional Collapse of Incomplete Multi-View Clustering via Direct Contrastive Learning

Incomplete multi-view clustering (IMVC) is an unsupervised approach, among which IMVC via contrastive learning has received attention due to its excellent performance. The previous methods have the following problems: 1) Over-reliance on additional projection heads when solving the dimensional collapse problem in which latent features are only valid in lower-dimensional subspaces during clustering. However, many parameters in the projection heads are unnecessary. 2) The recovered view contain inconsistent private information and useless private information will mislead the learning of common semantics due to consistent learning and reconstruction learning on the same feature. To address the above issues, we propose a novel incomplete multi-view contrastive clustering framework. This framework directly optimizes the latent feature subspace, utilizes the learned feature vectors and their sub-vectors for reconstruction learning and consistency learning, thereby effectively avoiding dimensional collapse without relying on projection heads. Since reconstruction loss and contrastive loss are performed on different features, the adverse effect of useless private information is reduced. For the incomplete data, the missing information is recovered by the cross-view prediction mechanism and the inconsistent information from different views is discarded by the minimum conditional entropy to further avoid the influence of private information. Extensive experimental results of the method on 5 public datasets show that the method achieves state-of-the-art clustering results.
Deep Reinforcement Learning for Localizability-Enhanced Navigation in Dynamic Human Environments

Reliable localization is crucial for autonomous robots to navigate efficiently and safely. Some navigation methods can plan paths with high localizability (which describes the capability of acquiring reliable localization). By following these paths, the robot can access the sensor streams that facilitate more accurate location estimation results by the localization algorithms. However, most of these methods require prior knowledge and struggle to adapt to unseen scenarios or dynamic changes. To overcome these limitations, we propose a novel approach for localizability-enhanced navigation via deep reinforcement learning in dynamic human environments. Our proposed planner automatically extracts geometric features from 2D laser data that are helpful for localization. The planner learns to assign different importance to the geometric features and encourages the robot to navigate through areas that are helpful for laser localization. To facilitate the learning of the planner, we suggest two techniques: (1) an augmented state representation that considers the dynamic changes and the confidence of the localization results, which provides more information and allows the robot to make better decisions, (2) a reward metric that is capable to offer both sparse and dense feedback on behaviors that affect localization accuracy. Our method exhibits significant improvements in lost rate and arrival rate when tested in previously unseen environments.