Miracle-dz/UniFAR-FTUs huggingface dataset:
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness

Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive"indicator properties"of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.
The Unreasonable Effectiveness of Large Language-Vision Models for Source-free Video Domain Adaptation

Source-Free Video Unsupervised Domain Adaptation (SFVUDA) task consists in adapting an action recognition model, trained on a labelled source dataset, to an unlabelled target dataset, without accessing the actual source data. The previous approaches have attempted to address SFVUDA by leveraging self-supervision (e.g., enforcing temporal consistency) derived from the target data itself. In this work, we take an orthogonal approach by exploiting "web-supervision" from Large Language-Vision Models (LLVMs), driven by the rationale that LLVMs contain a rich world prior surprisingly robust to domain-shift. We showcase the unreasonable effectiveness of integrating LLVMs for SFVUDA by devising an intuitive and parameter-efficient method, which we name Domain Adaptation with Large Language-Vision models (DALL-V), that distills the world prior and complementary source model information into a student network tailored for the target. Despite the simplicity, DALL-V 1 achieves significant improvement over state-of-the-art SFVUDA methods.
Microscopic derivation of a Schr\"odinger equation in dimension one with a nonlinear point interaction

We derive an effective equation for the dynamics of many identical bosons in dimension one, in the presence of a tiny impurity. The interaction between every pair of bosons is mediated by the impurity through a three-body interaction. Assuming a simultaneous mean-field and short-range scaling, with the short-range proceeding slower than the mean-field, and choosing an initial fully condensed state, we prove propagation of chaos and obtain an effective one-particle Schr\"odinger equation with a nonlinearity concentrated at a point. More precisely, we prove convergence of one-particle density operators in the trace-class topology, and estimate the fluctuations as superexponential. This is the first derivation of the so-called nonlinear delta model, widely investigated in the last decades.
Approximating Min-Diameter: Standard and Bichromatic

The min-diameter of a directed graph $G$ is a measure of the largest distance between nodes. It is equal to the maximum min-distance $d_{min}(u,v)$ across all pairs $u,v \in V(G)$, where $d_{min}(u,v) = \min(d(u,v), d(v,u))$. Our work provides a $O(m^{1.426}n^{0.288})$-time $3/2$-approximation algorithm for min-diameter in DAGs, and a faster $O(m^{0.713}n)$-time almost-$3/2$-approximation variant. (An almost-$\alpha$-approximation algorithm determines the min-diameter to within a multiplicative factor of $\alpha$ plus constant additive error.) By a conditional lower bound result of [Abboud et al, SODA 2016], a better than $3/2$-approximation can't be achieved in truly subquadratic time under the Strong Exponential Time Hypothesis (SETH), so our result is conditionally tight. We additionally obtain a new conditional lower bound for min-diameter approximation in general directed graphs, showing that under SETH, one cannot achieve an approximation factor below 2 in truly subquadratic time. We also present the first study of approximating bichromatic min-diameter, which is the maximum min-distance between oppositely colored vertices in a 2-colored graph.
Retro-FPN: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation

Learning per-point semantic features from the hierarchical feature pyramid is essential for point cloud semantic segmentation. However, most previous methods suffered from ambiguous region features or failed to refine per-point features effectively, which leads to information loss and ambiguous semantic identification. To resolve this, we propose Retro-FPN to model the per-point feature prediction as an explicit and retrospective refining process, which goes through all the pyramid layers to extract semantic features explicitly for each point. Its key novelty is a retro-transformer for summarizing semantic contexts from the previous layer and accordingly refining the features in the current stage. In this way, the categorization of each point is conditioned on its local semantic pattern. Specifically, the retro-transformer consists of a local cross-attention block and a semantic gate unit. The cross-attention serves to summarize the semantic pattern retrospectively from the previous layer. And the gate unit carefully incorporates the summarized contexts and refines the current semantic features. Retro-FPN is a pluggable neural network that applies to hierarchical decoders. By integrating Retro-FPN with three representative backbones, including both point-based and voxel-based methods, we show that Retro-FPN can significantly improve performance over state-of-the-art backbones. Comprehensive experiments on widely used benchmarks can justify the effectiveness of our design. The source is available at https://github.com/AllenXiangX/Retro-FPN.
Denoising diffusion-based MR to CT image translation enables whole spine vertebral segmentation in 2D and 3D without manual annotations

Background: Automated segmentation of spinal MR images plays a vital role both scientifically and clinically. However, accurately delineating posterior spine structures presents challenges. Methods: This retrospective study, approved by the ethical committee, involved translating T1w and T2w MR image series into CT images in a total of n=263 pairs of CT/MR series. Landmark-based registration was performed to align image pairs. We compared 2D paired (Pix2Pix, denoising diffusion implicit models (DDIM) image mode, DDIM noise mode) and unpaired (contrastive unpaired translation, SynDiff) image-to-image translation using “peak signal to noise ratio” (PSNR) as quality measure. A publicly available segmentation network segmented the synthesized CT
Mitigating Misinformation Spreading in Social Networks Via Edge Blocking

The wide adoption of social media platforms has brought about numerous benefits for communication and information sharing. However, it has also led to the rapid spread of misinformation, causing significant harm to individuals, communities, and society at large. Consequently, there has been a growing interest in devising efficient and effective strategies to contain the spread of misinformation. One popular countermeasure is blocking edges in the underlying network. We model the spread of misinformation using the classical Independent Cascade model and study the problem of minimizing the spread by blocking a given number of edges. We prove that this problem is computationally hard, but we propose an intuitive community-based algorithm, which aims to detect well-connected communities in the network and disconnect the inter-community edges. Our experiments on various real-world social networks demonstrate that the proposed algorithm significantly outperforms the prior methods, which mostly rely on centrality measures.
Neural-network quantum state study of the long-range antiferromagnetic Ising chain

We investigate quantum phase transitions in the transverse field Ising chain with algebraically decaying long-range antiferromagnetic interactions by using the variational Monte Carlo method with the restricted Boltzmann machine being employed as a trial wave function ansatz. In the finite-size scaling analysis with the order parameter and the second R\'enyi entropy, we find that the central charge deviates from 1/2 at a small decay exponent $\alpha_\mathrm{LR}$ in contrast to the critical exponents staying very close to the short-range (SR) Ising values regardless of $\alpha_\mathrm{LR}$ examined, supporting the previously proposed scenario of conformal invariance breakdown. To identify the threshold of the Ising universality and the conformal symmetry, we perform two additional tests for the universal Binder ratio and the conformal field theory (CFT) description of the correlation function. It turns out that both indicate a noticeable deviation from the SR Ising class at $\alpha_\mathrm{LR}<2$. However, a closer look at the scaled correlation function for $\alpha_\mathrm{LR} \ge 2$ shows a gradual change from the asymptotic line of the CFT verified at $\alpha_\mathrm{LR} = 3$, providing a rough estimate of the threshold being in the range of $2 \lesssim \alpha_\mathrm{LR}<3$.
Hypergraphs with irrational Tur\'{a}n density and many extremal configurations

Unlike graphs, determining Tur\'{a}n densities of hypergraphs is known to be notoriously hard in general. The essential reason is that for many classical families of $r$-uniform hypergraphs $\mathcal{F}$, there are perhaps many near-extremal $\mathcal{M}_t$-free configurations with very different structure. Such a phenomenon is called not stable, and Liu and Mubayi gave a first not stable example. Another perhaps reason is that little is known about the set consisting of all possible Tur\'{a}n densities which has cardinality of the continuum. Let $t\ge 2$ be an integer. In this paper, we construct a finite family $\mathcal{M}_t$ of 3-uniform hypergraphs such that the Tur\'{a}n density of $\mathcal{M}_t$ is irrational, and there are $t$ near-extremal $\mathcal{M}_t$-free configurations that are far from each other in edit-distance. This is the first not stable example that has an irrational Tur\'{a}n density. It also provides a new phenomenon about feasible region functions.
Smooth Distance Approximation

Traditional problems in computational geometry involve aspects that are both discrete and continuous. One such example is nearest-neighbor searching, where the input is discrete, but the result depends on distances, which vary continuously. In many real-world applications of geometric data structures, it is assumed that query results are continuous, free of jump discontinuities. This is at odds with many modern data structures in computational geometry, which employ approximations to achieve efficiency, but these approximations often suffer from discontinuities. In this paper, we present a general method for transforming an approximate but discontinuous data structure into one that produces a smooth approximation, while matching the asymptotic space efficiencies of the original. We achieve this by adapting an approach called the partition-of-unity method, which smoothly blends multiple local approximations into a single smooth global approximation. We illustrate the use of this technique in a specific application of approximating the distance to the boundary of a convex polytope in $\mathbb{R}^d$ from any point in its interior. We begin by developing a novel data structure that efficiently computes an absolute $\varepsilon$-approximation to this query in time $O(\log (1/\varepsilon))$ using $O(1/\varepsilon^{d/2})$ storage space. Then, we proceed to apply the proposed partition-of-unity blending to guarantee the smoothness of the approximate distance field, establishing optimal asymptotic bounds on the norms of its gradient and Hessian.
Ensemble Kalman Filters with Resampling

Filtering is concerned with online estimation of the state of a dynamical system from partial and noisy observations. In applications where the state of the system is high dimensional, ensemble Kalman filters are often the method of choice. These algorithms rely on an ensemble of interacting particles to sequentially estimate the state as new observations become available. Despite the practical success of ensemble Kalman filters, theoretical understanding is hindered by the intricate dependence structure of the interacting particles. This paper investigates ensemble Kalman filters that incorporate an additional resampling step to break the dependency between particles. The new algorithm is amenable to a theoretical analysis that extends and improves upon those available for filters without resampling, while also performing well in numerical examples.
Two Kinetically Different Supercooled Liquids -- Potential Energy Landscape Perspective

During the process of rapid cooling, two distinct types of supercooled liquids are found. One kind of supercooled liquids destines to crystallize (the crystal-forming liquid (GFL)), and the other forms a glass (the glass-forming liquid (GFL)). Despite having no significant differences in conventional physical quantities such as structure and energy, the distribution of the potential energy after short-time averaging reveals the emergence of distinction. By analyzing the skewness of the potential energy distribution after the short-time average, a new characteristic time ($\Delta_{CTS)}$) emerges. For a given temperature, the skewness reaches an extremum when the duration of the short-time average equals this characteristic time. Interestingly, this characteristic time scale follows a Curie-like law ($\Delta_{CTS}\sim 1/(T-T^{*})^{{\gamma}}$) with an exponent $\gamma$, which effectively distinguishes these two types of supercooled liquids. The value of $\gamma$ undergoes a discontinuous transition at the critical cooling rate. The value of $\gamma_{g}$, corresponding to GFL, is always greater than $\gamma_{c}$ associated with CFL. Remarkably, $T^{*}$ precisely represents the glass transition temperature (Tg ) or the crystallization temperature (Tc ) for GFL and CFL, respectively. Theoretical analysis suggests that, the Curie-like law implies a kinetic phase transition. The essential difference between these two types of supercooled liquids lies in the different in local potential energy landscapes. First, the atomic motion in GFL may have more strong correlation than that in CFL. Second, CFL may possess a lower configurational entropy than GFL. The current study not only delineates the differentiation between the two types of supercooled liquids, but also provides a new perspective for exploring the nature of glasses.
Nearly Minimax Optimal Wasserstein Conditional Independence Testing

This paper is concerned with minimax conditional independence testing. In contrast to some previous works on the topic, which use the total variation distance to separate the null from the alternative, here we use the Wasserstein distance. In addition, we impose Wasserstein smoothness conditions which on bounded domains are weaker than the corresponding total variation smoothness imposed, for instance, by Neykov et al. [2021]. This added flexibility expands the distributions which are allowed under the null and the alternative to include distributions which may contain point masses for instance. We characterize the optimal rate of the critical radius of testing up to logarithmic factors. Our test statistic which nearly achieves the optimal critical radius is novel, and can be thought of as a weighted multi-resolution version of the U-statistic studied by Neykov et al. [2021].
ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model Reuse

The rapid expansion of foundation pre-trained models and their fine-tuned counterparts has significantly contributed to the advancement of machine learning. Leveraging pre-trained models to extract knowledge and expedite learning in real-world tasks, known as"Model Reuse", has become crucial in various applications. Previous research focuses on reusing models within a certain aspect, including reusing model weights, structures, and hypothesis spaces. This paper introduces ZhiJian, a comprehensive and user-friendly toolbox for model reuse, utilizing the PyTorch backend. ZhiJian presents a novel paradigm that unifies diverse perspectives on model reuse, encompassing target architecture construction with PTM, tuning target model with PTM, and PTM-based inference. This empowers deep learning practitioners to explore downstream tasks and identify the complementary advantages among different methods. ZhiJian is readily accessible at https://github.com/zhangyikaii/lamda-zhijian facilitating seamless utilization of pre-trained models and streamlining the model reuse process for researchers and developers.
Learning MDL logic programs from noisy data

Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game playing, and program synthesis, show that our approach can outperform existing approaches in terms of predictive accuracies and scale to moderate amounts of noise.
Fine-grained Text and Image Guided Point Cloud Completion with CLIP Model

This paper focuses on the recently popular task of point cloud completion guided by multimodal information. Although existing methods have achieved excellent performance by fusing auxiliary images, there are still some deficiencies, including the poor generalization ability of the model and insufficient fine-grained semantic information for extracted features. In this work, we propose a novel multimodal fusion network for point cloud completion, which can simultaneously fuse visual and textual information to predict the semantic and geometric characteristics of incomplete shapes effectively. Specifically, to overcome the lack of prior information caused by the small-scale dataset, we employ a pre-trained vision-language model that is trained with a large amount of image-text pairs. Therefore, the textual and visual encoders of this large-scale model have stronger generalization ability. Then, we propose a multi-stage feature fusion strategy to fuse the textual and visual features into the backbone network progressively. Meanwhile, to further explore the effectiveness of fine-grained text descriptions for point cloud completion, we also build a text corpus with fine-grained descriptions, which can provide richer geometric details for 3D shapes. The rich text descriptions can be used for training and evaluating our network. Extensive quantitative and qualitative experiments demonstrate the superior performance of our method compared to state-of-the-art point cloud completion networks.
Thermalization universality-class transition induced by Anderson localization

We study the disorder-induced crossover between the two recently discovered thermalization slowing-down universality classes -- characterized by long- and short-range coupling -- in classical unitary circuits maps close to integrability. We compute Lyapunov spectra, which display qualitatively distinct features depending on whether the proximity to the integrable limit is short or long ranged. For sufficiently small nonlinearity, translationally invariant systems fall into the long-range class. Adding disorder to such a system triggers a transition to the short-range class -- implying a breaking of this invariance -- and in the very limit of vanishing non-linearity Anderson localization emerges. The crossover from long- to short-range class is attained by tuning the localization length, $\xi$, from $\xi \approx N$ to $\xi \ll N$, where $N$ is the system size. As a consequence, the Lyapunov spectrum becomes exponentially suppressed, depending on how strongly its translational invariance is destroyed. We expect that this disorder-induced crossover will lead to prethermalized phases and, following quantization, to many-body localization.
Crustal shaping of high frequency ground motion in the Washington-Oregon, northern and central California regions

Large da ta sets of vertical and horizontal seisraograms from the Pacific Northwest Seismic Network, Northern California Seismic Network, and Berkeley Digital Seismic Network were used to study the high frequency (0.25-16 Hz) ground motion char­ acteristics in W ashington-Oregon, northern, and central California. I have split the data set of central California into eastern and western parts to investigate regional variations of ground motion param eters. I performed regressions of Fourier velocity spectra and peak filtered ground velocities to obtain the propagation, excitation, and site terms. The regression results for the propagation term show a simpler from of geomet­ rical spreading in central east California compared to other regions. For northern California this term shows a rapid decrease of am plitude with distance especially for frequencies higher than 4 Hz. The propagation term for the Pacific Northwest and central west California show a typical trend of attenuation with some effects of supercritical reflections. For the Pacific Northwest, the best model th a t fits the observation is expressed by Q( f ) = 280 /°-55. The geometrical spreading is r~ L2 for a distance range of 40 to 70 km. For other distance ranges the decrease of am plitude is less rapid. The central east California and central west California results are parameterized with Q( f ) — 280 / 0-5° and Q ( f ) = 250 / 0,3° respectively. The northern California results are not easy to model and require using a frequency dependent rq and geometrical spreading. The average model for two frequency ranges is Q( f ) = 210 /°-50. The geometrical spreading effect for the frequencies higher than 4 Hz is very strong at distances less than 70 km. The excitation term s were modeled using the Brune’s source model. An average stress drop ( A ct) of 3 0 bars was obtained for the Pacific Northwest. The northern 1 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. California average value for stress drop is 90 bars. The observations of central east California were modeled with a stress drop of 45 bars while the best fit for the western part required A ct = 125 bars. The wide range of the values obtained for Q, g(r), and A a, indicates th a t the ground motion param eters of a specific region may not be used for another region. For example, using the stress drops of central California may overestimate the ground motion level predictions in the Pacific Northwest. Regional attenuation relationships can be developed using small earthquake data which are available in many regions. These relationships reflect the regional wave propagation characteristics and give more accurate results in seismic hazard analysis.