Miracle-dz/UniFAR-FTUs huggingface dataset:
Decomposing Global Feature Effects Based on Feature Interactions

Global feature effect methods, such as partial dependence plots, provide an intelligible visualization of the expected marginal feature effect. However, such global feature effect methods can be misleading, as they do not represent local feature effects of single observations well when feature interactions are present. We formally introduce generalized additive decomposition of global effects (GADGET), which is a new framework based on recursive partitioning to find interpretable regions in the feature space such that the interaction-related heterogeneity of local feature effects is minimized. We provide a mathematical foundation of the framework and show that it is applicable to the most popular methods to visualize marginal feature effects, namely partial dependence, accumulated local effects, and Shapley additive explanations (SHAP) dependence. Furthermore, we introduce a new permutation-based interaction test to detect significant feature interactions that is applicable to any feature effect method that fits into our proposed framework. We empirically evaluate the theoretical characteristics of the proposed methods based on various feature effect methods in different experimental settings. Moreover, we apply our introduced methodology to two real-world examples to showcase their usefulness.
Zero-shot Pose Transfer for Unrigged Stylized 3D Characters

Transferring the pose of a reference avatar to stylized 3D characters of various shapes is a fundamental task in computer graphics. Existing methods either require the stylized characters to be rigged, or they use the stylized character in the desired pose as ground truth at training. We present a zero-shot approach that requires only the widely available deformed non-stylized avatars in training, and deforms stylized characters of significantly different shapes at inference. Classical methods achieve strong generalization by deforming the mesh at the triangle level, but this requires labelled correspondences. We leverage the power of local deformation, but without requiring explicit correspondence labels. We introduce a semi-supervised shape-understanding module to bypass the need for explicit correspondences at test time, and an implicit pose deformation module that deforms individual surface points to match the target pose. Further-more, to encourage realistic and accurate deformation of stylized characters, we introduce an efficient volume-based test-time training procedure. Because it does not need rigging, nor the deformed stylized character at training time, our model generalizes to categories with scarce annotation, such as stylized quadrupeds. Extensive experiments demonstrate the effectiveness of the proposed method compared to the state-of-the-art approaches trained with comparable or more supervision. Our project page is available at https://jiashunwang.github. io/ZPT/
One hundred optical emission lines of molecular hydrogen from a low-metallicity photodissociation region

We report the detection of a rich spectrum of more than one hundred optical emission lines of vibrationally hot molecular hydrogen (H2) from the photodissociation region (PDR) around the mini-starburst cluster NGC 346 in the Small Magellanic Cloud. The lines are concentrated in the spectral range 6000 to 9300 Angstrom and have observed brightnesses ranging from 0.01% to 0.4% times that of the H beta lambda 4861 hydrogen recombination line. Analysis of the spatial distribution of the H2 lines shows that they originate from a range of depths in the PDR, intermediate between the shallow layers probed by known fluorescent lines of neutral nitrogen and oxygen, and the more shielded layers probed by neutral carbon recombination lines. Comparison with other PDRs shows that the relative strength of the H2 lines with respect to the [C I] lambda 8727 line increases rapidly with decreasing metallicity, being at least 40 times larger in NGC 346 than in the prototypical PDR of the Orion Bar. The internal PDR dust extinction is also found to be anomalously low in NGC 346. A separate result is the discovery of a high-ionization bow shock around the O2 star Walborn 3.
Pareto Front Identification with Regret Minimization

We consider Pareto front identification for linear bandits (PFILin) where the goal is to identify a set of arms whose reward vectors are not dominated by any of the others when the mean reward vector is a linear function of the context. PFILin includes the best arm identification problem and multi-objective active learning as special cases. The sample complexity of our proposed algorithm is $\tilde{O}(d/\Delta^2)$, where $d$ is the dimension of contexts and $\Delta$ is a measure of problem complexity. Our sample complexity is optimal up to a logarithmic factor. A novel feature of our algorithm is that it uses the contexts of all actions. In addition to efficiently identifying the Pareto front, our algorithm also guarantees $\tilde{O}(\sqrt{d/t})$ bound for instantaneous Pareto regret when the number of samples is larger than $\Omega(d\log dL)$ for $L$ dimensional vector rewards. By using the contexts of all arms, our proposed algorithm simultaneously provides efficient Pareto front identification and regret minimization. Numerical experiments demonstrate that the proposed algorithm successfully identifies the Pareto front while minimizing the regret.
ShaRP: Shape-Regularized Multidimensional Projections

Projections, or dimensionality reduction methods, are techniques of choice for the visual exploration of high-dimensional data. Many such techniques exist, each one of them having a distinct visual signature - i.e., a recognizable way to arrange points in the resulting scatterplot. Such signatures are implicit consequences of algorithm design, such as whether the method focuses on local vs global data pattern preservation; optimization techniques; and hyperparameter settings. We present a novel projection technique - ShaRP - that provides users explicit control over the visual signature of the created scatterplot, which can cater better to interactive visualization scenarios. ShaRP scales well with dimensionality and dataset size, generically handles any quantitative dataset, and provides this extended functionality of controlling projection shapes at a small, user-controllable cost in terms of quality metrics.
The feasibility of artificial consciousness through the lens of neuroscience

Interactions with large language models (LLMs) have led to the suggestion that these models may soon be conscious. From the perspective of neuroscience, this position is difficult to defend. For one, the inputs to LLMs lack the embodied, embedded information content characteristic of our sensory contact with the world around us. Secondly, the architectures of present-day artificial intelligence algorithms are missing key features of the thalamocortical system that have been linked to conscious awareness in mammals. Finally, the evolutionary and developmental trajectories that led to the emergence of living conscious organisms arguably have no parallels in artificial systems as envisioned today. The existence of living organisms depends on their actions and their survival is intricately linked to multi-level cellular, inter-cellular, and organismal processes culminating in agency and consciousness.
The inner radius of nodal domains in high dimensions

We prove that every nodal domain of an eigenfunction of the Laplacian of eigenvalue $\lambda$ on a $d$-dimensional closed Riemannian manifold contains a ball of radius $c\lambda^{-1/2}(\log\lambda)^{-(d-2)/2}$. This ball is centered at a point at which the eigenfunction attains its maximum in absolute value within the nodal domain.
Hard X-ray emission from blazars associated with high-energy neutrinos

Bright blazars were found to be prominent neutrino sources, and a number of IceCube events were associated with them. Evaluating high-energy photon emission of such blazars is crucial for better understanding of the processes and regions where neutrinos are produced. Here, we focus on hard X-ray emission observed by the SRG/ART-XC telescope, by the Swift/BAT imager, and by the INTEGRAL/IBIS telescope. Their energy range ~10 keV is well-suited for probing photons that potentially participate in neutrino production by interacting with ultrarelativistic protons. We find that neutrino-associated blazars tend to demonstrate remarkably strong X-ray emission compared to other VLBI blazars in the sky, chance coincidence probability is p=0.5%. Both neutrinos and hard X-rays are found to come from blazars at cosmological distances z ~ 1, and are boosted by relativistic beaming that makes it possible to detect them on Earth. Our results suggest that neutrinos are produced within compact blazar jets, with target X-ray photons emitted from accelerated jet regions.
The volume of convex bodies and Banach space geometry

Introduction 1. Notation and preliminary background 2. Gaussian variables. K-convexity 3. Ellipsoids 4. Dvoretzky's theorem 5. Entropy, approximation numbers, and Gaussian processes 6. Volume ratio 7. Milman's ellipsoids 8. Another proof of the QS theorem 9. Volume numbers 10. Weak cotype 2 11. Weak type 2 12. Weak Hilbert spaces 13. Some examples: the Tsirelson spaces 14. Reflexivity of weak Hilbert spaces 15. Fredholm determinants Final remarks Bibliography Index.
Disentangled Generative Models for Robust Prediction of System Dynamics

Deep neural networks have become increasingly of interest in dynamical system prediction, but out-of-distribution generalization and long-term stability still remains challenging. In this work, we treat the domain parameters of dynamical systems as factors of variation of the data generating process. By leveraging ideas from supervised disentanglement and causal factorization, we aim to separate the domain parameters from the dynamics in the latent space of generative models. In our experiments we model dynamics both in phase space and in video sequences and conduct rigorous OOD evaluations. Results indicate that disentangled VAEs adapt better to domain parameters spaces that were not present in the training data. At the same time, disentanglement can improve the long-term and out-of-distribution predictions of state-of-the-art models in video sequences.
SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds

Text-to-image diffusion models can create stunning images from natural language descriptions that rival the work of professional artists and photographers. However, these models are large, with complex network architectures and tens of denoising iterations, making them computationally expensive and slow to run. As a result, high-end GPUs and cloud-based inference are required to run diffusion models at scale. This is costly and has privacy implications, especially when user data is sent to a third party. To overcome these challenges, we present a generic approach that, for the first time, unlocks running text-to-image diffusion models on mobile devices in less than $2$ seconds. We achieve so by introducing efficient network architecture and improving step distillation. Specifically, we propose an efficient UNet by identifying the redundancy of the original model and reducing the computation of the image decoder via data distillation. Further, we enhance the step distillation by exploring training strategies and introducing regularization from classifier-free guidance. Our extensive experiments on MS-COCO show that our model with $8$ denoising steps achieves better FID and CLIP scores than Stable Diffusion v$1.5$ with $50$ steps. Our work democratizes content creation by bringing powerful text-to-image diffusion models to the hands of users.
Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection

Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real-world problems. QML has the potential to address cybersecurity-related challenges. Considering the novelty and complex architecture of QML, resources are not yet explicitly available that can pave cybersecurity learners to instill efficient knowledge of this emerging technology. In this research, we design and develop QML-based ten learning modules covering various cybersecurity topics by adopting student centering case-study based learning approach. We apply one subtopic of QML on a cybersecurity topic comprised of pre-lab, lab, and post-lab activities towards providing learners with hands-on QML experiences in solving real-world security problems. In order to engage and motivate students in a learning environment that encourages all students to learn, pre-lab offers a brief introduction to both the QML subtopic and cybersecurity problem. In this paper, we utilize quantum support vector machine (QSVM) for malware classification and protection where we use open source Pennylane QML framework on the drebin215 dataset. We demonstrate our QSVM model and achieve an accuracy of 95% in malware classification and protection. We will develop all the modules and introduce them to the cybersecurity community in the coming days.
CMOS-compatible photonic devices for single-photon generation

Abstract Sources of single photons are one of the key building blocks for quantum photonic technologies such as quantum secure communication and powerful quantum computing. To bring the proof-of-principle demonstration of these technologies from the laboratory to the real world, complementary metal–oxide–semiconductor (CMOS)-compatible photonic chips are highly desirable for photon generation, manipulation, processing and even detection because of their compactness, scalability, robustness, and the potential for integration with electronics. In this paper, we review the development of photonic devices made from materials (e.g., silicon) and processes that are compatible with CMOS fabrication facilities for the generation of single photons.
FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow

Reconstruction of 3D neural fields from posed images has emerged as a promising method for self-supervised representation learning. The key challenge preventing the deployment of these 3D scene learners on large-scale video data is their dependence on precise camera poses from structure-from-motion, which is prohibitively expensive to run at scale. We propose a method that jointly reconstructs camera poses and 3D neural scene representations online and in a single forward pass. We estimate poses by first lifting frame-to-frame optical flow to 3D scene flow via differentiable rendering, preserving locality and shift-equivariance of the image processing backbone. SE(3) camera pose estimation is then performed via a weighted least-squares fit to the scene flow field. This formulation enables us to jointly supervise pose estimation and a generalizable neural scene representation via re-rendering the input video, and thus, train end-to-end and fully self-supervised on real-world video datasets. We demonstrate that our method performs robustly on diverse, real-world video, notably on sequences traditionally challenging to optimization-based pose estimation techniques.
Boosting the Performance of Transformer Architectures for Semantic Textual Similarity

Semantic textual similarity is the task of estimating the similarity between the meaning of two texts. In this paper, we fine-tune transformer architectures for semantic textual similarity on the Semantic Textual Similarity Benchmark by tuning the model partially and then end-to-end. We experiment with BERT, RoBERTa, and DeBERTaV3 cross-encoders by approaching the problem as a binary classification task or a regression task. We combine the outputs of the transformer models and use handmade features as inputs for boosting algorithms. Due to worse test set results coupled with improvements on the validation set, we experiment with different dataset splits to further investigate this occurrence. We also provide an error analysis, focused on the edges of the prediction range.
FreeCHR: An Algebraic Framework for CHR-Embeddings

We introduce the framework FreeCHR, which formalizes the embedding of Constraint Handling Rules (CHR) into a host-language, using the concept of initial algebra semantics from category theory, to establish a high-level implementation scheme for CHR, as well as a common formalization for both theory and practice. We propose a lifting of the syntax of CHR via an endofunctor in the category Set and a lifting of the operational semantics, using the free algebra, generated by the endofunctor. We then lift the very abstract operational semantics of CHR into FreeCHR, and give proofs for soundness and completeness w.r. t. their original definition.
Identifiability and Generalizability in Constrained Inverse Reinforcement Learning

Two main challenges in Reinforcement Learning (RL) are designing appropriate reward functions and ensuring the safety of the learned policy. To address these challenges, we present a theoretical framework for Inverse Reinforcement Learning (IRL) in constrained Markov decision processes. From a convex-analytic perspective, we extend prior results on reward identifiability and generalizability to both the constrained setting and a more general class of regularizations. In particular, we show that identifiability up to potential shaping (Cao et al., 2021) is a consequence of entropy regularization and may generally no longer hold for other regularizations or in the presence of safety constraints. We also show that to ensure generalizability to new transition laws and constraints, the true reward must be identified up to a constant. Additionally, we derive a finite sample guarantee for the suboptimality of the learned rewards, and validate our results in a gridworld environment.
TopEx: Topic-based Explanations for Model Comparison

Meaningfully comparing language models is challenging with current explanation methods. Current explanations are overwhelming for humans due to large vocabularies or incomparable across models. We present TopEx, an explanation method that enables a level playing field for comparing language models via model-agnostic topics. We demonstrate how TopEx can identify similarities and differences between DistilRoBERTa and GPT-2 on a variety of NLP tasks.
Characterizing the Impacts of Multi‐Scale Heterogeneity on Solute Transport in Fracture Networks

We model flow and transport in fracture networks with varying degrees of fracture‐to‐fracture aperture heterogeneity and network intensity to show how changes in these properties can cause the emergence of anomalous flow and transport behavior. If fracture‐to‐fracture aperture heterogeneity is increased in sparse networks, velocity fluctuations inhibit high flow rates and solute transport can be delayed. Surprisingly, transport can be slowed even in cases where hydraulic aperture is monotonically increased. As the intensity of the networks is increased, more connected pathways allow for particles to bypass these effects. There exists transition behavior where with relatively few connected pathways in a network, first arrival times of particles are not heavily affected by fracture‐to‐fracture aperture heterogeneity, but the scaling behavior of the tails is strongly influenced. These results reinforce the importance of considering multi‐scale effects in fractured systems and can inform flow and transport processes in both natural and engineered fractured systems.
Sign-Balanced Pattern-Avoiding Permutation Classes

A set of permutations is called sign-balanced if the set contains the same number of even permutations as odd permutations. Let $S_n(\sigma_1, \sigma_2, \ldots, \sigma_r)$ be the set of permutations in the symmetric group $S_n$ which avoids patterns $\sigma_1, \sigma_2, \ldots, \sigma_r$. The aim of this paper is to investigate when, for certain patterns $\sigma_1, \sigma_2, \ldots, \sigma_r$, $S_n(\sigma_1, \sigma_2, \ldots, \sigma_r)$ is sign-balanced for every integer $n>1$. We prove that for any $\{\sigma_1, \sigma_2, \ldots, \sigma_r\}\subseteq S_3$, if $\{\sigma_1, \sigma_2, \ldots, \sigma_r\}$ is sign-balanced except $\{132, 213, 231, 312\}$, then $S_n(\sigma_1, \sigma_2, \ldots, \sigma_r)$ is sign-balanced for every integer $n>1$. In addition, we give some results in the case of avoiding some patterns of length $4$.
Grüneisen parameter as an entanglement compass and the breakdown of the Hellmann-Feynman theorem

The Gr\"uneisen ratio $\Gamma$, i.e., the singular part of the ratio of thermal expansion to the specific heat, has been broadly employed to explore both finite-$T$ and quantum critical points (QCPs). For a genuine quantum phase transition (QPT), thermal fluctuations are absent and thus the thermodynamic $\Gamma$ cannot be employed. We propose a quantum analogue to $\Gamma$ that computes entanglement as a function of a tuning parameter and show that QPTs take place only for quadratic non-diagonal Hamiltonians. We showcase our approach using the quantum 1D Ising model with transverse field and Kane's quantum computer. The slowing down of the dynamics and thus the ``creation of mass'' close to any QCP/QPT is also discussed.
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER

Complex Named Entity Recognition (NER) is the task of detecting linguistically complex named entities in low-context text. In this paper, we present ACLM Attention-map aware keyword selection for Conditional Language Model fine-tuning), a novel data augmentation approach based on conditional generation, to address the data scarcity problem in low-resource complex NER. ACLM alleviates the context-entity mismatch issue, a problem existing NER data augmentation techniques suffer from and often generates incoherent augmentations by placing complex named entities in the wrong context. ACLM builds on BART and is optimized on a novel text reconstruction or denoising task - we use selective masking (aided by attention maps) to retain the named entities and certain keywords in the input sentence that provide contextually relevant additional knowledge or hints about the named entities. Compared with other data augmentation strategies, ACLM can generate more diverse and coherent augmentations preserving the true word sense of complex entities in the sentence. We demonstrate the effectiveness of ACLM both qualitatively and quantitatively on monolingual, cross-lingual, and multilingual complex NER across various low-resource settings. ACLM outperforms all our neural baselines by a significant margin (1%-36%). In addition, we demonstrate the application of ACLM to other domains that suffer from data scarcity (e.g., biomedical). In practice, ACLM generates more effective and factual augmentations for these domains than prior methods.