Miracle-dz/UniFAR-FTUs huggingface dataset:
Representation-Agnostic Shape Fields

3D shape analysis has been widely explored in the era of deep learning. Numerous models have been developed for various 3D data representation formats, e.g., MeshCNN for meshes, PointNet for point clouds and VoxNet for voxels. In this study, we present Representation-Agnostic Shape Fields (RASF), a generalizable and computation-efficient shape embedding module for 3D deep learning. RASF is implemented with a learnable 3D grid with multiple channels to store local geometry. Based on RASF, shape embeddings for various 3D shape representations (point clouds, meshes and voxels) are retrieved by coordinate indexing. While there are multiple ways to optimize the learnable parameters of RASF, we provide two effective schemes among all in this paper for RASF pre-training: shape reconstruction and normal estimation. Once trained, RASF becomes a plug-and-play performance booster with negligible cost. Extensive experiments on diverse 3D representation formats, networks and applications, validate the universal effectiveness of the proposed RASF. Code and pre-trained models are publicly available https://github.com/seanywang0408/RASF
A note on the Canonical Ramsey Theorem and Ramsey ultrafilters

We give a characterizations of Ramsey ultraﬁlters on P ( ω ) in terms of functions f : ω n → ω and their ultraﬁlter extensions. To do this, we prove that for any partition P of [ ω ] n there is a ﬁnite partition Q of [ ω ] 2 n such that any set X ⊆ ω that is homogeneous for Q is a ﬁnite union of sets that are canonical for P . Ramsey theory studies homogeneous (monochromatic) substructures for various partitions (colorings) of structures. In Canonical Ramsey Theory, a more general notion of a canonical substructure is considered. A nonprincipal ultraﬁlter on P ( ω ) that contains an homogeneous set for any ﬁnite partition P of [ ω ] n , 1 ≤ n < ω , is called a Ramsey ultraﬁlter. It is well known that an ultraﬁlter u on P ( ω ) is a Ramsey ultraﬁlter if and only if it is selective and if and only if it is minimal (with respect to the Rudin-Keisler (pre)order), see, for example, [1]. Both of these characterizations are formulated in terms of functions f : ω → ω or their ultraﬁlter extensions. We show that a nonprincipal ultraﬁlter on P ( ω ) is a Ramsey ultraﬁlter if and only if it contains a canonical set for each partition of [ ω ] n , and we give characterizations of Ramsey ultraﬁlters similar to the above in terms of functions on ω of arbitrary ﬁnite arity and the concept of their ultraﬁlter extensions, which appeared in recent works [2, 3]. To do this, we prove that for any partition P of [ ω ] n , 1 ≤ n < ω , there is a ﬁnite partition Q of [ ω ] 2 n such that any set X ⊆ ω that is homogeneous for Q is a ﬁnite union of sets that are canonical
Evaluation of April Tag and WhyCode Fiducial Systems for Autonomous Precision Drone Landing with a Gimbal-Mounted Camera

— Fiducial markers provide a computationally cheap way for drones to determine their location with respect to a landing pad and execute precision landings. However, most existing work in this ﬁeld uses a ﬁxed, downward facing camera that does not leverage the common gimbal-mounted camera setup found on many drones. Such rigid systems cannot easily track detected markers, and may lose sight of the markers in non-ideal conditions (e.g. wind gusts). This paper evaluates April Tag and WhyCode ﬁducial systems for drone landing with a gimbal-mounted, monocular camera, with the advantage that the drone system can track the marker over time. However, since the orientation of the camera changes, we must know the orientation of the marker, which is unreliable in monocular ﬁducial systems. Additionally, the system must be fast. We propose 2 methods for mitigating the orientation ambiguity of WhyCode, and 1 method for increasing the runtime detection rate of April Tag. We evaluate our 3 systems against 2 default systems in terms of marker orientation ambiguity, and detection rate. We test rates of marker detection in a ROS framework on a Raspberry Pi 4, and we rank the systems in terms of their performance. Our ﬁrst WhyCode variant signiﬁcantly reduces orientation ambiguity with an insigniﬁcant reduction in detection rate. Our second WhyCode variant does not show signiﬁcantly different orientation ambiguity from the default WhyCode system, but does provide additional functionality in terms of multi-marker WhyCode bundle arrangements. Our April Tag variant does not show performance improvements on a Raspberry Pi 4.
New algorithms for computing the least trimmed squares estimator

Instead of minimizing the sum of all $n$ squared residuals as the classical least squares (LS) does, Rousseeuw (1984) proposed to minimize the sum of $h$ ($n/2 \leq h<n$) smallest squared residuals, the resulting estimator is called least trimmed squares (LTS). The idea of the LTS is simple but its computation is challenging since no LS-type analytical computation formula exists anymore. Attempts had been made since its presence, the feasible solution algorithm (Hawkins (1994)), fastlts.f (Rousseeuw and Van Driessen (1999)), and FAST-LTS (Rousseeuw and Van Driessen (2006)), among others, are promising approximate algorithms. The latter two have been incorporated into R function ltsReg by Valentin Todorov. These algorithms utilize combinatorial- or subsampling- approaches. With the great software accessibility and fast speed, the LTS, enjoying many desired properties, has become one of the most popular robust regression estimators across multiple disciplines. This article proposes analytic approaches -- employing first-order derivative (gradient) and second-order derivative (Hessian matrix) of the objective function. Our approximate algorithms for the LTS are vetted in synthetic and real data examples. Compared with ltsReg -- the benchmark in robust regression and well-known for its speed, our algorithms are comparable (and sometimes even favorable) with respect to both speed and accuracy criteria. Other major contributions include (i) originating the uniqueness and the strong and Fisher consistency at empirical and population settings respectively; (ii) deriving the influence function in a general setting; (iii) re-establishing the asymptotic normality (consequently root-n consistency) of the estimator with a neat and general approach.
Implicit Parameter-free Online Learning with Truncated Linear Models

Parameter-free algorithms are online learning algorithms that do not require setting learning rates. They achieve optimal regret with respect to the distance between the initial point and any competitor. Yet, parameter-free algorithms do not take into account the geometry of the losses. Recently, in the stochastic optimization literature, it has been proposed to instead use truncated linear lower bounds, which produce better performance by more closely modeling the losses. In particular, truncated linear models greatly reduce the problem of overshooting the minimum of the loss function. Unfortunately, truncated linear models cannot be used with parameter-free algorithms because the updates become very expensive to compute. In this paper, we propose new parameter-free algorithms that can take advantage of truncated linear models through a new update that has an"implicit"flavor. Based on a novel decomposition of the regret, the new update is efficient, requires only one gradient at each step, never overshoots the minimum of the truncated model, and retains the favorable parameter-free properties. We also conduct an empirical study demonstrating the practical utility of our algorithms.
Continual Sequence Generation with Adaptive Compositional Modules

Continual learning is essential for real-world deployment when there is a need to quickly adapt the model to new tasks without forgetting knowledge of old tasks. Existing work on continual sequence generation either always reuses existing parameters to learn new tasks, which is vulnerable to catastrophic forgetting on dissimilar tasks, or blindly adds new parameters for every new task, which could prevent knowledge sharing between similar tasks. To get the best of both worlds, in this work, we propose continual sequence generation with adaptive compositional modules to adaptively add modules in transformer architectures and compose both old and new modules for new tasks. We also incorporate pseudo experience replay to facilitate knowledge transfer in those shared modules. Experiment results on various sequences of generation tasks show that our framework can adaptively add modules or reuse modules based on task similarity, outperforming state-of-the-art baselines in terms of both performance and parameter efficiency. We make our code public at https://github.com/GT-SALT/Adaptive-Compositional-Modules.
HDLock: exploiting privileged encoding to protect hyperdimensional computing models against IP stealing

Hyperdimensional Computing (HDC) is facing infringement issues due to straightforward computations. This work, for the first time, raises a critical vulnerability of HDC --- an attacker can reverse engineer the entire model, only requiring the unindexed hypervector memory. To mitigate this attack, we propose a defense strategy, namely HDLock, which significantly increases the reasoning cost of encoding. Specifically, HDLock adds extra feature hypervector combination and permutation in the encoding module. Compared to the standard HDC model, a two-layer-key HDLock can increase the adversarial reasoning complexity by 10 order of magnitudes without inference accuracy loss, with only 21% latency overhead.
Computational ergonomics for task delegation in Human-Robot Collaboration: spatiotemporal adaptation of the robot to the human through contactless gesture recognition

The high prevalence of work-related musculoskeletal disorders (WMSDs) could be addressed by optimizing Human-Robot Collaboration (HRC) frameworks for manufacturing applications. In this context, this paper proposes two hypotheses for ergonomically effective task delegation and HRC. The first hypothesis states that it is possible to quantify ergonomically professional tasks using motion data from a reduced set of sensors. Then, the most dangerous tasks can be delegated to a collaborative robot. The second hypothesis is that by including gesture recognition and spatial adaptation, the ergonomics of an HRC scenario can be improved by avoiding needless motions that could expose operators to ergonomic risks and by lowering the physical effort required of operators. An HRC scenario for a television manufacturing process is optimized to test both hypotheses. For the ergonomic evaluation, motion primitives with known ergonomic risks were modeled for their detection in professional tasks and to estimate a risk score based on the European Assembly Worksheet (EAWS). A Deep Learning gesture recognition module trained with egocentric television assembly data was used to complement the collaboration between the human operator and the robot. Additionally, a skeleton-tracking algorithm provided the robot with information about the operator's pose, allowing it to spatially adapt its motion to the operator's anthropometrics. Three experiments were conducted to determine the effect of gesture recognition and spatial adaptation on the operator's range of motion. The rate of spatial adaptation was used as a key performance indicator (KPI), and a new KPI for measuring the reduction in the operator's motion is presented in this paper.
Dynamical quantum phase transitions in a noisy lattice gauge theory

Lattice gauge theories (LGTs) form an intriguing class of theories highly relevant to both high-energy particle physics and low-energy condensed matter physics with the rapid development of engineered quantum devices providing new tools to study e.g. dynamics of such theories. The massive Schwinger model is known to exhibit intricate properties of more complicated theories and has recently been shown to undergo dynamical quantum phase transitions out of equilibrium. With current technology, noise is inevitable and potentially fatal for a successful quantum simulation. This paper studies the dynamics subject to noise of a (1 + 1)D U(1) quantum link model following a quench of the sign of the mass term. We ﬁnd that not only is the system capable of handling noise at rates realistic in NISQ-era devices, promising the possiblity to study the target dynamics with current technology, but the eﬀect of noise can be understood in terms of simple models. Speciﬁcally the gauge-breaking nature of bit-ﬂip channels results in exponential dampening of state amplitudes, and thus observables, which does not aﬀect the structures of interest. This is especially important as it demonstrates that the gauge theory can be successfully studied with devices that only exhibit approximate gauge invariance.
Multi-similarity based Hyperrelation Network for few-shot segmentation

Few-shot semantic segmentation aims at recognizing the object regions of unseen categories with only a few annotated examples as supervision. The key to few-shot segmentation is to establish a robust semantic relationship between the support and query images and to prevent overfitting. In this paper, we propose an effective Multi-similarity Hyperrelation Network (MSHNet) to tackle the few-shot semantic segmentation problem. In MSHNet, we propose a new Generative Prototype Similarity (GPS), which together with cosine similarity can establish a strong semantic relation between the support and query images. The locally generated prototype similarity based on global feature is logically complementary to the global cosine similarity based on local feature, and the relationship between the query image and the supported image can be expressed more comprehensively by using the two similarities simultaneously. In addition, we propose a Symmetric Merging Block (SMB) in MSHNet to efficiently merge multi-layer, multi-shot and multi-similarity hyperrelational features. MSHNet is built on the basis of similarity rather than specific category features, which can achieve more general unity and effectively reduce overfitting. On two benchmark semantic segmentation datasets Pascal-5i and COCO-20i, MSHNet achieves new state-of-the-art performances on 1-shot and 5-shot semantic segmentation tasks.
Discovery of a thin lithium plateau among metal-poor red giant branch stars

The surface lithium abundance A(Li) of warm metal-poor dwarf stars exhibits a narrow plateau down to [Fe/H]~-2.8 dex, while at lower metallicities the average value drops by 0.3 dex with a significant star-by-star scatter (called lithium meltdown). This behaviour is in conflict with predictions of standard stellar evolution models calculated with the initial A(Li) provided by the standard Big Bang nucleosynthesis. The lower red giant branch (LRGB) stars provide a complementary tool to understand the initial A(Li) distribution in metal-poor stars. We have collected a sample of high-resolution spectra of 58 LRGB stars spanning a range of [Fe/H] between ~ -7.0 dex and ~ -1.3 dex. The LRGB stars display an A(Li) distribution clearly different from that of the dwarfs, without signatures of a meltdown and with two distinct components: (a) a thin A(Li) plateau with an average A(Li)=1.09+-0.01 dex (sigma=0.07 dex), and (b) a small fraction of Li-poor stars with A(Li) lower than ~0.7 dex. The A(Li) distribution observed in LRGB stars can be reconciled with an initial abundance close to the cosmological value, by including an additional chemical element transport in stellar evolution models. The required efficiency of this transport allows us to match also the Spite plateau lithium abundance measured in the dwarfs. The emerging scenario is that all metal-poor stars formed with the same initial A(Li) but those that are likely the product of coalescence or that experienced binary mass transfer and show lower A(Li) . We conclude that A(Li) in LRGB stars is qualitatively compatible with the cosmological A(Li) value and that the meltdown observed in dwarf stars does not reflect a real drop of the abundance at birth.
backbone: An R package to extract network backbones

Networks are useful for representing phenomena in a broad range of domains. Although their ability to represent complexity can be a virtue, it is sometimes useful to focus on a simplified network that contains only the most important edges: the backbone. This paper introduces and demonstrates a substantially expanded version of the backbone package for R, which now provides methods for extracting backbones from weighted networks, weighted bipartite projections, and unweighted networks. For each type of network, fully replicable code is presented first for small toy examples, then for complete empirical examples using transportation, political, and social networks. The paper also demonstrates the implications of several issues of statistical inference that arise in backbone extraction. It concludes by briefly reviewing existing applications of backbone extraction using the backbone package, and future directions for research on network backbone extraction.
Lean Evolutionary Reinforcement Learning by Multitasking with Importance Sampling

—Studies have shown evolution strategies (ES) to be a promising approach for reinforcement learning (RL) with deep neural networks. However, the issue of high sample complexity persists in applications of ES to deep RL. In this paper, we address the shortcoming of today’s methods via a novel neuroevolutionary multitasking (NuEMT) algorithm, designed to transfer information from a set of auxiliary tasks (of short episode length) to the target (full length) RL task at hand. The artiﬁcially generated auxiliary tasks allow an agent to update and quickly evaluate policies on shorter time horizons. The evolved skills are then transferred to guide the longer and harder task towards an optimal policy. We demonstrate that the NuEMT algorithm achieves data-lean evolutionary RL, reducing expensive agent-environment interaction data requirements. Our key algorithmic contribution in this setting is to introduce, for the ﬁrst time, a multitask information transfer mechanism based on the statistical importance sampling technique. In addition, an adaptive resource allocation strategy is utilized to assign computational resources to auxiliary tasks based on their gleaned usefulness. Experiments on a range of continuous control tasks from the OpenAI Gym conﬁrm that our proposed algorithm is efﬁcient compared to recent ES baselines.
Automatic Detection of Entity-Manipulated Text using Factual Knowledge

In this work, we focus on the problem of distinguishing a human written news article from a news article that is created by manipulating entities in a human written news article (e.g., replacing entities with factually incorrect entities). Such manipulated articles can mislead the reader by posing as a human written news article. We propose a neural network based detector that detects manipulated news articles by reasoning about the facts mentioned in the article. Our proposed detector exploits factual knowledge via graph convolutional neural network along with the textual information in the news article. We also create challenging datasets for this task by considering various strategies to generate the new replacement entity (e.g., entity generation from GPT-2). In all the settings, our proposed model either matches or outperforms the state-of-the-art detector in terms of accuracy. Our code and data are available at https://github.com/UBC-NLP/manipulated_entity_detection.
One current viral threat to wildlife or in captivity

In general, it is assumed that there are many differences between human beings and other animal species. That may be true, but in terms of diseases caused by viruses and microorganisms that difference is much narrower. As André Lwoff, Nobel Prize winner in 1965, said, “viruses are viruses” and therefore the fact that they affect animal species makes no difference. Over the centuries, the human species has been threatened by various bacterial and viral pathogens. In this respect, animal species are not immune. Currently, a ribovirus (SARS-CoV-2) affects the human species presenting several variants, the last known being omicron. Likewise, in animals and initially in dogs, the Canine Vistemper virus was described, of which several genotypes are also known that make any vaccination plan tremble, including other families besides the Canidae. Due to the above, it is not unreasonable to propose CDV as a latent threat in animal species, wild or in captivity. A dizzying read.
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER

Data augmentation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as NER, data augmentation methods often suffer from token-label misalignment, which leads to unsatsifactory performance. In this work, we propose Masked Entity Language Modeling (MELM) as a novel data augmentation framework for low-resource NER. To alleviate the token-label misalignment issue, we explicitly inject NER labels into sentence context, and thus the fine-tuned MELM is able to predict masked entity tokens by explicitly conditioning on their labels. Thereby, MELM generates high-quality augmented data with novel entities, which provides rich entity regularity knowledge and boosts NER performance. When training data from multiple languages are available, we also integrate MELM with code-mixing for further improvement. We demonstrate the effectiveness of MELM on monolingual, cross-lingual and multilingual NER across various low-resource levels. Experimental results show that our MELM consistently outperforms the baseline methods.
Globetrotter: Connecting Languages by Connecting Images

Machine translation between many languages at once is highly challenging, since training with ground truth re-quires supervision between all language pairs, which is dif-ficult to obtain. Our key insight is that, while languages may vary drastically, the underlying visual appearance of the world remains consistent. We introduce a method that uses visual observations to bridge the gap between languages, rather than relying on parallel corpora or topo-logical properties of the representations. We train a model that aligns segments of text from different languages if and only if the images associated with them are similar and each image in turn is well-aligned with its textual description. We train our model from scratch on a new dataset of text in over fifty languages with accompanying images. Experiments show that our method outperforms previous work on unsupervised word and sentence translation using retrieval. Code, models and data are available on globetrotter.cs. columbia.edu
On the Problem of Undirected st-connectivity

In this paper, we discuss an algorithm for the problem of undirected st-connectivity that is deterministic and log-space, namely that of Reingold within his 2008 paper "Undirected Connectivity in Log-Space" [Rei08]. We further present a separate proof by Rozenman and Vadhan of U STCONN ∈ L [RV05] and discuss its similarity with Reingold’s proof. Undirected st-connectively is known to be complete for the complexity class SL –problems solvable by symmetric, nondeterministic, log-space algorithms. Likewise, by Aleliunas et. al. [AKL + 79], it is known that undirected st-connectivity is within the RL complexity class, problems solvable by randomized (probabilistic) Turing machines with one-sided error in logarithmic space and polynomial time. Finally, our paper also shows that undirected st-connectivity is within the L complexity class, problems solvable by deterministic Turing machines in logarithmic space. Leading from this result, we shall explain why SL = L and discuss why is it believed that RL = L .