Miracle-dz/UniFAR-FTUs huggingface dataset:
Idempotent Generative Network

We propose a new approach for generative modeling based on training a neural network to be idempotent. An idempotent operator is one that can be applied sequentially without changing the result beyond the initial application, namely $f(f(z))=f(z)$. The proposed model $f$ is trained to map a source distribution (e.g, Gaussian noise) to a target distribution (e.g. realistic images) using the following objectives: (1) Instances from the target distribution should map to themselves, namely $f(x)=x$. We define the target manifold as the set of all instances that $f$ maps to themselves. (2) Instances that form the source distribution should map onto the defined target manifold. This is achieved by optimizing the idempotence term, $f(f(z))=f(z)$ which encourages the range of $f(z)$ to be on the target manifold. Under ideal assumptions such a process provably converges to the target distribution. This strategy results in a model capable of generating an output in one step, maintaining a consistent latent space, while also allowing sequential applications for refinement. Additionally, we find that by processing inputs from both target and source distributions, the model adeptly projects corrupted or modified data back to the target manifold. This work is a first step towards a ``global projector'' that enables projecting any input into a target data distribution.
Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition

Active learning, a widely adopted technique for enhancing machine learning models in text and image classification tasks with limited annotation resources, has received relatively little attention in the domain of Named Entity Recognition (NER). The challenge of data imbalance in NER has hindered the effectiveness of active learning, as sequence labellers lack sufficient learning signals. To address these challenges, this paper presents a novel reweighting-based active learning strategy that assigns dynamic smoothed weights to individual tokens. This adaptable strategy is compatible with various token-level acquisition functions and contributes to the development of robust active learners. Experimental results on multiple corpora demonstrate the substantial performance improvement achieved by incorporating our re-weighting strategy into existing acquisition functions, validating its practical efficacy.
Generative Input: Towards Next-Generation Input Methods Paradigm

Since the release of ChatGPT, generative models have achieved tremendous success and become the de facto approach for various NLP tasks. However, its application in the field of input methods remains under-explored. Many neural network approaches have been applied to the construction of Chinese input method engines(IMEs). Previous research often assumed that the input pinyin was correct and focused on Pinyin-to-character(P2C) task, which significantly falls short of meeting users' demands. Moreover, previous research could not leverage user feedback to optimize the model and provide personalized results. In this study, we propose a novel Generative Input paradigm named GeneInput. It uses prompts to handle all input scenarios and other intelligent auxiliary input functions, optimizing the model with user feedback to deliver personalized results. The results demonstrate that we have achieved state-of-the-art performance for the first time in the Full-mode Key-sequence to Characters(FK2C) task. We propose a novel reward model training method that eliminates the need for additional manual annotations and the performance surpasses GPT-4 in tasks involving intelligent association and conversational assistance. Compared to traditional paradigms, GeneInput not only demonstrates superior performance but also exhibits enhanced robustness, scalability, and online learning capabilities.
Adaptive Dynamics of Diverging Fitness Optima

We analyse a non-local parabolic integro-differential equation modelling the evolutionary dynamics of a phenotypically-structured population in a changing environment. Such models arise in the study of species adapting to climate change, or cancer adapting to therapy. Our results concern the long-time behaviour, in the small mutation limit, of the model. The main novelty of our work is that the time- and trait-dependent per capita growth rate is characterised by having multiple (locally) optimal traits which shift at possibly different velocities. When the velocities are the same we find that the solution concentrates on a point-set which depends on the shifting speed. These points can be thought of as"lagged optima"and the fitness value at each lagged optima is the"lagged fitness". When the velocities of the optimal traits are different and each has a potentially different optimal fitness, we find that the solution in fact concentrates as a Dirac delta function on the positive lagged optimum with maximum lagged fitness. Our results imply that in populations undergoing competition in temporally changing environments, both the true optimal fitness and the required rate of adaptation for each of the diverging optimal traits contribute to the eventual dominance of one trait.
Bioaccumulation of chromium, copper, maganese, nickel and lead in a freshwater cichlid, hemichromis fasciatus from Ogba River in Benin City, Nigeria

This study assessed and monitored the extent of Cr, Cu, Mn, Ni and Pb bioaccumulation by the freshwater fish (Hemichromis fasciatus) from Ogba River in Benin City, Nigeria between November, 2004 and October, 2005. The effects of Dry and Rainy seasons on the extent of bioaccumulation were specifically addressed. The results showed metals bioaccumulation levels differences (except Mn) at the sample stations, which were attributed to the influence of drainage effluents influx into the river. Seasonal levels of the metals were also different at the stations. Higher dry season levels were ascribed to higher water temperatures, while rainy season high levels, could result from pronounced leaching of metals. The finding that some metal accumulation levels exceeded WHO and PEPA recommended limits in food fish, indicated that the fish of Ogba River, might not be fit for human consumption. Consequently, close monitoring of metal pollution of Ogba River is strongly recommended.
Long-Range Neural Atom Learning for Molecular Graphs

Graph Neural Networks (GNNs) have been widely adopted for drug discovery with molecular graphs. Nevertheless, current GNNs are mainly good at leveraging short-range interactions (SRI) but struggle to capture long-range interactions (LRI), both of which are crucial for determining molecular properties. To tackle this issue, we propose a method that implicitly projects all original atoms into a few Neural Atoms, which abstracts the collective information of atomic groups within a molecule. Specifically, we explicitly exchange the information among neural atoms and project them back to the atoms' representations as an enhancement. With this mechanism, neural atoms establish the communication channels among distant nodes, effectively reducing the interaction scope of arbitrary node pairs into a single hop. To provide an inspection of our method from a physical perspective, we reveal its connection with the traditional LRI calculation method, Ewald Summation. We conduct extensive experiments on three long-range graph benchmarks, covering both graph-level and link-level tasks on molecular graphs. We empirically justify that our method can be equipped with an arbitrary GNN and help to capture LRI.
Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly

The adversarial vulnerability of deep neural networks (DNNs) has drawn great attention due to the security risk of applying these models in real-world applications. Based on transferability of adversarial examples, an increasing number of transfer-based methods have been developed to fool black-box DNN models whose architecture and parameters are inaccessible. Although tremendous effort has been exerted, there still lacks a standardized benchmark that could be taken advantage of to compare these methods systematically, fairly, and practically. Our investigation shows that the evaluation of some methods needs to be more reasonable and more thorough to verify their effectiveness, to avoid, for example, unfair comparison and insufficient consideration of possible substitute/victim models. Therefore, we establish a transfer-based attack benchmark (TA-Bench) which implements 30+ methods. In this paper, we evaluate and compare them comprehensively on 25 popular substitute/victim models on ImageNet. New insights about the effectiveness of these methods are gained and guidelines for future evaluations are provided. Code at: https://github.com/qizhangli/TA-Bench.
EISim: A Platform for Simulating Intelligent Edge Orchestration Solutions

To support the stringent requirements of the future intelligent and interactive applications, intelligence needs to become an essential part of the resource management in the edge environment. Developing intelligent orchestration solutions is a challenging and arduous task, where the evaluation and comparison of the proposed solution is a focal point. Simulation is commonly used to evaluate and compare proposed solutions. However, the currently existing, openly available simulators are lacking in terms of supporting the research on intelligent edge orchestration methods. To address this need, this article presents a simulation platform called Edge Intelligence Simulator (EISim), the purpose of which is to facilitate the research on intelligent edge orchestration solutions. EISim is extended from an existing fog simulator called PureEdgeSim. In its current form, EISim supports simulating deep reinforcement learning based solutions and different orchestration control topologies in scenarios related to task offloading and resource pricing on edge. The platform also includes additional tools for creating simulation environments, running simulations for agent training and evaluation, and plotting results.
Transitivity And Related Notions For Graph Induced Symbolic Systems

In this paper, we investigate the dynamical behavior of a two dimensional shift $X_G$ (generated by a two dimensional graph $G=(\mathcal{H},\mathcal{V})$) using the adjacency matrices of the generating graph $G$. In particular, we investigate properties such as transitivity, directional transitivity, weak mixing, directional weak mixing and mixing for the shift space $X_G$. We prove that if $(HV)_{ij}\neq 0 \Leftrightarrow (VH)_{ij}\neq 0$ (for all $i,j$), while doubly transitivity (weak mixing) of $X_H$ (or $X_V$) ensures the same for two dimensional shift generated by the graph $G$, directional transitivity (in the direction $(r,s)$) can be characterized through the block representation of $H^rV^s$. We provide necessary and sufficient criteria to establish horizontal (vertical) transitivity for the shift space $X_G$. We also provide examples to establish the necessity of the conditions imposed. Finally, we investigate the decomposability of a given graph into product of graphs with reduced complexity.
InfoGuard: A Design and Usability Study of User-Controlled Application-Independent Encryption for Privacy-Conscious Users

Billions of secure messaging users have adopted end-to-end encryption (E2EE). Nevertheless, challenges remain. Most communication applications do not provide E2EE, and application silos prevent interoperability. Our qualitative analysis of privacy-conscious users' discussions of E2EE on Reddit reveals concerns about trusting client applications with plaintext, lack of clear indicators about how encryption works, high cost to switch apps, and concerns that most apps are not open source. We propose InfoGuard, a system enabling E2EE for user-to-user communication in any application. InfoGuard allows users to trigger encryption on any textbox, even if the application does not support E2EE. InfoGuard encrypts text before it reaches the application, eliminating the client app's access to plaintext. InfoGuard also incorporates visible encryption to make it easier for users to understand that their data is being encrypted and give them greater confidence in the system's security. The design enables fine-grained encryption, allowing specific sensitive data items to be encrypted while the rest remains visible to the server. Participants in our user study found InfoGuard usable and trustworthy, expressing a willingness to adopt it.
Crater Depth Prediction in Granular Collisions: A Uniaxial Compression Model

Impact crater experiments in granular media traditionally involve loosely packed sand targets. However, this study investigates granular impact craters on both loosely and more tightly packed sand targets. We report granular vs. granular experiments that consistently adhere to power-law scaling laws for diameter as a function of impacting energy, similar to those reported by other groups for their experiments utilizing both solid and granular projectiles. In contrast, we observe significant deviations in the depth vs. energy power-law predicted by previous models. To address this discrepancy, we introduce a physical model of uniaxial compression that explains how depth saturates in granular collisions. Furthermore, we present an energy balance alongside this model that aligns well with our crater volume measurements and describes the energy transfer mechanisms acting during crater formation. Central peak formation also plays an essential role in better transferring vertical momentum to horizontal degrees of freedom, resulting in shallow craters on compacted sandbox targets. Our results reveal a depth-to-diameter aspect ratio of approximately $\sim 1/5$, allowing us to interpret the shallowness of planetary craters in light of the uniaxial compression mechanism proposed in this work.
Quatro++: Robust Global Registration Exploiting Ground Segmentation for Loop Closing in LiDAR SLAM

Global registration is a fundamental task that estimates the relative pose between two viewpoints of 3D point clouds. However, there are two issues that degrade the performance of global registration in LiDAR SLAM: one is the sparsity issue and the other is degeneracy. The sparsity issue is caused by the sparse characteristics of the 3D point cloud measurements in a mechanically spinning LiDAR sensor. The degeneracy issue sometimes occurs because the outlier-rejection methods reject too many correspondences, leaving less than three inliers. These two issues have become more severe as the pose discrepancy between the two viewpoints of 3D point clouds becomes greater. To tackle these problems, we propose a robust global registration framework, called Quatro++. Extending our previous work that solely focused on the global registration itself, we address the robust global registration in terms of the loop closing in LiDAR SLAM. To this end, ground segmentation is exploited to achieve robust global registration. Through the experiments, we demonstrate that our proposed method shows a higher succeiasdsss rate than the state-of-the-art global registration methods, overcoming the sparsity and degeneracy issues. In addition, we show that ground segmentation asdasd asignificantly helps to idfdfsncrease the success rate for the ground vehicles. Finally, we apply our proposed method to the loop clossdasdlksajing modulasdse in LiDAR SLAM and confirm that the quality of the loop constraints is improved, showing more precise mapping results. Therefore, the experimental evidence corroborated the suitabilitiasdasdy of our method as an initial alignment in the loop closing. Our code is available at https://quatro-plusplus.github. io .
Dynamic Multimodal Information Bottleneck for Multimodality Classification

Effectively leveraging multimodal data such as various images, laboratory tests and clinical information is gaining traction in a variety of AI-based medical diagnosis and prognosis tasks. Most existing multi-modal techniques only focus on enhancing their performance by leveraging the differences or shared features from various modalities and fusing feature across different modalities. These approaches are generally not optimal for clinical settings, which pose the additional challenges of limited training data, as well as being rife with redundant data or noisy modality channels, leading to subpar performance. To address this gap, we study the robustness of existing methods to data redundancy and noise and propose a generalized dynamic multimodal information bottleneck framework for attaining a robust fused feature representation. Specifically, our information bottleneck module serves to filter out the task-irrelevant information and noises in the fused feature, and we further introduce a sufficiency loss to prevent dropping of task-relevant information, thus explicitly preserving the sufficiency of prediction information in the distilled feature. We validate our model on an in-house and a public COVID19 dataset for mortality prediction as well as two public biomedical datasets for diagnostic tasks. Extensive experiments show that our method surpasses the state-of-the-art and is significantly more robust, being the only method to remain performance when large-scale noisy channels exist. Our code is publicly available at https://github.com/BII-wushuang/DMIB.
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis

We introduce Resilient Multiple Choice Learning (rMCL), an extension of the MCL approach for conditional distribution estimation in regression settings where multiple targets may be sampled for each training input. Multiple Choice Learning is a simple framework to tackle multimodal density estimation, using the Winner-Takes-All (WTA) loss for a set of hypotheses. In regression settings, the existing MCL variants focus on merging the hypotheses, thereby eventually sacrificing the diversity of the predictions. In contrast, our method relies on a novel learned scoring scheme underpinned by a mathematical framework based on Voronoi tessellations of the output space, from which we can derive a probabilistic interpretation. After empirically validating rMCL with experiments on synthetic data, we further assess its merits on the sound source localization problem, demonstrating its practical usefulness and the relevance of its interpretation.
Economical routes to size-specific assembly of self-closing structures

Self-assembly is one of the prevalent strategies used by living systems to fabricate ensembles of precision nanometer-scale structures and devices. The push for analogous approaches to create synthetic nanomaterials has led to the development of a large class of programmable crystalline structures. However, many applications require `self-limiting' assemblies, which autonomously terminate growth at a well-defined size and geometry. For example, curved architectures such as tubules, vesicles, or capsids can be designed to self-close at a particular size, symmetry, and topology. But developing synthetic strategies for self-closing assembly has been challenging, in part because such structures are prone to polymorphism that arises from thermal fluctuations of their local curvature, a problem that worsens with increased target size. Here we demonstrate a strategy to eliminate this source of polymorphism in self-closing assembly of tubules by increasing the assembly complexity. In the limit of single-component assembly, we find that thermal fluctuations allow the system to assemble nearby, off-target structures with varying widths, helicities, and chirality. By increasing the number of distinct components, we reduce the density of off-target states, thereby increasing the selectivity of a user-specified target structure to nearly 100%. We further show that by reducing the design constraints by targeting either the pitch or the width of tubules, fewer components are needed to reach complete selectivity. Combining experiments with theory, our results reveal an economical limit, which determines the minimum number of components that are required to create arbitrary assembly sizes with full selectivity. In the future, this approach could be extended to more complex self-limited structures, such as shells or triply periodic surfaces.
Potential Melting of Extrasolar Planets by Tidal Dissipation

Tidal heating on Io due to its finite eccentricity was predicted to drive surface volcanic activity, which was subsequently confirmed by the Voyager spacecraft. Although the volcanic activity in Io is more complex, in theory volcanism can be driven by runaway melting in which the tidal heating increases as the mantle thickness decreases. We show that this runaway melting mechanism is generic for a composite planetary body with liquid core and solid mantle, provided that (i) the mantle rigidity, μ, is comparable to the central pressure, i.e., μ/(ρ gR P) ≳ 0.1 for a body with density ρ, surface gravitational acceleration g, and radius R P; (ii) the surface is not molten; (iii) tides deposit sufficient energy; and (iv) the planet has nonzero eccentricity. We calculate the approximate liquid core radius as a function of μ/(ρ gR P), and find that more than 90% of the core will melt due to this runaway for μ/(ρ gR P) ≳ 1. From all currently confirmed exoplanets, we find that the terrestrial planets in the L 98-59 system are the most promising candidates for sustaining active volcanism. However, uncertainties regarding the quality factors and the details of tidal heating and cooling mechanisms prohibit definitive claims of volcanism on any of these planets. We generate synthetic transmission spectra of these planets assuming Venus-like atmospheric compositions with an additional 5%, 50%, and 98% SO2 component, which is a tracer of volcanic activity. We find a ≳3σ preference for a model with SO2 with 5–10 transits with JWST for L 98-59bcd.
RPCANet: Deep Unfolding RPCA Based Infrared Small Target Detection

Deep learning (DL) networks have achieved remarkable performance in infrared small target detection (ISTD). However, these structures exhibit a deficiency in interpretability and are widely regarded as black boxes, as they disregard domain knowledge in ISTD. To alleviate this issue, this work proposes an interpretable deep network for detecting infrared dim targets, dubbed RPCANet. Specifically, our approach formulates the ISTD task as sparse target extraction, low-rank background estimation, and image reconstruction in a relaxed Robust Principle Component Analysis (RPCA) model. By unfolding the iterative optimization updating steps into a deep-learning framework, time-consuming and complex matrix calculations are replaced by theory-guided neural networks. RPCANet detects targets with clear interpretability and preserves the intrinsic image feature, instead of directly transforming the detection task into a matrix decomposition problem. Extensive experiments substantiate the effectiveness of our deep unfolding framework and demonstrate its trustworthy results, surpassing baseline methods in both qualitative and quantitative evaluations.
The Plain of Phaistos: Cycles of Social Complexity in the Mesara Region of Crete

The volume presents the results on an interdisciplinary regional field project (1984 - 1987) carried out on the island Of Crete. This volume traces the changing patterns of settlement and cycles of social complexity from the Late Neolithic period to the present day within the heartland of the state of Phaistos. The authors and contributors publish geological, archaeological, environmental, botanical, historical and ethnographic studies that establish the regional identity of the Western Mesara. Using a combination of empirical, processual and post-processual theoretical approaches, the volume investigates a central problem - how and why did the Bronze Age and Classical states arise at Phaistos?