Miracle-dz/UniFAR-FTUs huggingface dataset:
Curves are algebraic $K(\pi,1)$: theoretical and practical aspects

We prove that any geometrically connected curve $X$ over a field $k$ is an algebraic $K(\pi,1)$, as soon as its geometric irreducible components have nonzero genus. This means that the cohomology of any locally constant constructible \'etale sheaf of $\mathbb{Z}/n\mathbb{Z}$-modules, with $n$ invertible in $k$, is canonically isomorphic to the cohomology of its corresponding $\pi_1(X)$-module. To this end, we explicitly construct some Galois coverings of $X$ corresponding to Galois coverings of the normalisation of its irreducible components. When $k$ is finite or separably closed, we explicitly describe finite quotients of $\pi_1(X)$ that allow to compute the cohomology groups of the sheaf, and give explicit descriptions of the cup products $H^1\times H^1\to H^2$ and $H^1\times H^2\to H^3$ in terms of finite group cohomology.
How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study

This paper aims to answer the question: Can deep learning models be cost-efficiently trained on a global market of spot VMs spanning different data centers and cloud providers? To provide guidance, we extensively evaluate the cost and throughput implications of training in different zones, continents, and clouds for representative CV, NLP, and ASR models. To expand the current training options further, we compare the scalability potential for hybrid-cloud scenarios by adding cloud resources to on-premise hardware to improve training throughput. Finally, we show how leveraging spot instance pricing enables a new cost-efficient way to train models with multiple cheap VMs, trumping both more centralized and powerful hardware and even on-demand cloud offerings at competitive prices.
Ultra-miniature dual-wavelength spatial frequency domain imaging for micro-endoscopy

Abstract. Significance There is a need for a cost-effective, quantitative imaging tool that can be deployed endoscopically to better detect early stage gastrointestinal cancers. Spatial frequency domain imaging (SFDI) is a low-cost imaging technique that produces near-real time, quantitative maps of absorption and reduced scattering coefficients, but most implementations are bulky and suitable only for use outside the body. Aim We aim to develop an ultra-miniature SFDI system comprising an optical fiber array (diameter 0.125 mm) and a micro camera (1×1 mm package) to displace conventionally bulky components, in particular, the projector. Approach First, we fabricated a prototype with an outer diameter of 3 mm, although the individual component dimensions could permit future packaging to a <1.5 mm diameter. We developed a phase-tracking algorithm to rapidly extract images with fringe projections at three equispaced phase shifts to perform SFDI demodulation. Results To validate the performance, we first demonstrate comparable recovery of quantitative optical properties between our ultra-miniature system and a conventional bench-top SFDI system with an agreement of 15% and 6% for absorption and reduced scattering, respectively. Next, we demonstrate imaging of absorption and reduced scattering of tissue-mimicking phantoms providing enhanced contrast between simulated tissue types (healthy and tumour), done simultaneously at wavelengths of 515 and 660 nm. Using a support vector machine classifier, we estimate that sensitivity and specificity values of >90% are feasible for detecting simulated squamous cell carcinoma. Conclusions This device shows promise as a cost-effective, quantitative imaging tool to detect variations in optical absorption and scattering as indicators of cancer.
Agents Explore the Environment Beyond Good Actions to Improve Their Model for Better Decisions

Improving the decision-making capabilities of agents is a key challenge on the road to artificial intelligence. To improve the planning skills needed to make good decisions, MuZero's agent combines prediction by a network model and planning by a tree search using the predictions. MuZero's learning process can fail when predictions are poor but planning requires them. We use this as an impetus to get the agent to explore parts of the decision tree in the environment that it otherwise would not explore. The agent achieves this, first by normal planning to come up with an improved policy. Second, it randomly deviates from this policy at the beginning of each training episode. And third, it switches back to the improved policy at a random time step to experience the rewards from the environment associated with the improved policy, which is the basis for learning the correct value expectation. The simple board game Tic-Tac-Toe is used to illustrate how this approach can improve the agent's decision-making ability. The source code, written entirely in Java, is available at https://github.com/enpasos/muzero.
A Survey on Learning Objects' Relationship for Image Captioning

Image captioning is a challenging modality transformation task in computer vision and natural language processing, aiming to understand the image content and describe it with a natural language. Recently, the relationship information between objects in the image has been investigated to be of importance in generating a more vivid and readable sentence. Many types of research have been done in relationship mining and learning for leveraging into the caption models. This paper mainly summarizes the methods of relational representation and relational encoding in image captioning. Besides, we discuss the advantages and disadvantages of these methods and provide commonly used datasets for the relational captioning task. Finally, the current problems and challenges in this task are highlighted.
Non-empty pairwise cross-intersecting families

Two families $\mathcal{A}$ and $\mathcal{B}$ are cross-intersecting if $A\cap B\ne \emptyset$ for any $A\in \mathcal{A}$ and $B\in \mathcal{B}$. We call $t$ families $\mathcal{A}_1, \mathcal{A}_2,\dots, \mathcal{A}_t$ pairwise cross-intersecting families if $\mathcal{A}_i$ and $\mathcal{A}_j$ are cross-intersecting when $1\le i<j \le t$. Additionally, if $\mathcal{A}_j\ne \emptyset$ for each $j\in [t]$, then we say that $\mathcal{A}_1, \mathcal{A}_2,\dots, \mathcal{A}_t$ are non-empty pairwise cross-intersecting. Let $\mathcal{A}_1\subset{[n]\choose k_1}, \mathcal{A}_2\subset{[n]\choose k_2}, \dots, \mathcal{A}_t\subset{[n]\choose k_t}$ be non-empty pairwise cross-intersecting families with $t\geq 2$, $k_1\geq k_2\geq \cdots \geq k_t$, $n\ge k_1+k_2$ and $d_1, d_2, \dots, d_t$ be positive numbers. In this paper, we give a sharp upper bound of $\sum_{j=1}^td_j|\mathcal{A}_j|$ and characterize the families $\mathcal{A}_1, \mathcal{A}_2,\dots, \mathcal{A}_t$ attaining the upper bound. Our results unifies results of Frankl and Tokushige [J. Combin. Theory Ser. A 61 (1992)], Shi, Frankl and Qian [Combinatorica 42 (2022)], Huang and Peng \cite{huangpeng}, and Zhang-Feng \cite{ZF2023}. Furthermore, our result can be applied in the treatment for some $n<k_1+k_2$ while all previous known results do not have such an application. In the proof, a result of Kruskal-Katona is applied to allow us to consider only families $\mathcal{A}_i$ whose elements are the first $|\mathcal{A}_i|$ elements in lexicographic order. We bound $\sum_{i=1}^t{|\mathcal{A}_i|}$ by a single variable function $g(R)$, where $R$ is the last element of $\mathcal{A}_1$ in lexicographic order. One crucial and challenge part is to verify that $-g(R)$ has unimodality. We think that the unimodality of functions in this paper are interesting in their own, in addition to the extremal result.
Learning Human Mesh Recovery in 3D Scenes

We present a novel method for recovering the absolute pose and shape of a human in a pre-scanned scene given a single image. Unlike previous methods that perform scene-aware mesh optimization, we propose to first estimate absolute position and dense scene contacts with a sparse 3D CNN, and later enhance a pretrained human mesh recovery network by cross-attention with the derived 3D scene cues. Joint learning on images and scene geometry enables our method to reduce the ambiguity caused by depth and occlusion, resulting in more reasonable global postures and contacts. Encoding scene-aware cues in the network also allows the proposed method to be optimization-free, and opens up the opportunity for real-time applications. The experiments show that the proposed network is capable of recovering accurate and physically-plausible meshes by a single forward pass and outperforms state-of-the-art methods in terms of both accuracy and speed. Code is available on our project page: https://zju3dv.github. io/sahmr/.
Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning

Online tuning of real-world plants is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods, such as Reinforcement Learning-trained Optimisation (RLO) and Bayesian optimisation (BO), hold great promise for achieving outstanding plant performance and reducing tuning times. Which algorithm to choose in different scenarios, however, remains an open question. Here we present a comparative study using a routine task in a real particle accelerator as an example, showing that RLO generally outperforms BO, but is not always the best choice. Based on the study's results, we provide a clear set of criteria to guide the choice of algorithm for a given tuning task. These can ease the adoption of learning-based autonomous tuning solutions to the operation of complex real-world plants, ultimately improving the availability and pushing the limits of operability of these facilities, thereby enabling scientific and engineering advancements.
A Unified Framework to Super-Resolve Face Images of Varied Low Resolutions

The existing face image super-resolution (FSR) algorithms usually train a specific model for a specific low input resolution for optimal results. By contrast, we explore in this work a unified framework that is trained once and then used to super-resolve input face images of varied low resolutions. For that purpose, we propose a novel neural network architecture that is composed of three anchor auto-encoders, one feature weight regressor and a final image decoder. The three anchor auto-encoders are meant for optimal FSR for three pre-defined low input resolutions, or named anchor resolutions, respectively. An input face image of an arbitrary low resolution is firstly up-scaled to the target resolution by bi-cubic interpolation and then fed to the three auto-encoders in parallel. The three encoded anchor features are then fused with weights determined by the feature weight regressor. At last, the fused feature is sent to the final image decoder to derive the super-resolution result. As shown by experiments, the proposed algorithm achieves robust and state-of-the-art performance over a wide range of low input resolutions by a single framework. Code and models will be made available after the publication of this work.
On variations of global mean surface temperature: When Laplace meets Milankovic

In his mathematical theory, Milankovic finds a link between the heat received by the Earth surface per unit time as a function of the solar ephemerids and derives a model of climate changes at periods longer than a few thousand years and more. In this paper, we investigate the potential connections of global temperature and Earth rotation at much shorter periods, in the complementary range of one to a few hundred years. For temperature, we select the HadCrut05. For Earth rotation, defined by pole coordinates and length of day, we use the IERS data sets. Using iterative Singular Spectrum Analysis (iSSA), we extract the trend and quasi-periodic components of these time series. The first quasi-periodic components (period ~80-90 years) are expressions of the Gleissberg cycle and are identical (at the level of uncertainty of the data). Taken together, the trend and Gleissberg components allow one to reconstruct 87% of the variance of the data for lod and 48% for temperature. The next four iSSA components, with periods ~40, 22, 15 and 9 years. The Lagrange and Laplace theories imply that the derivative of pole motion should be identical to lod variations: this strong check is passed by the trend + Gleissberg reconstructions. The annual oscillations of pole motion and lod are linked to annual variations in Sun-Earth distance, in agreement with an astronomical, but not a climatic origin. The results obtained in this paper for the observed temperature/rotation couple add to the growing list of evidence of solar and planetary forcings of gravitational nature on a number of geophysical processes (including sea-level, sea-level pressure, sea-ice extent, oceanic climate indices).
Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex

One of the most impactful findings in computational neuroscience over the past decade is that the object recognition accuracy of deep neural networks (DNNs) correlates with their ability to predict neural responses to natural images in the inferotemporal (IT) cortex. This discovery supported the long-held theory that object recognition is a core objective of the visual cortex, and suggested that more accurate DNNs would serve as better models of IT neuron responses to images. Since then, deep learning has undergone a revolution of scale: billion parameter-scale DNNs trained on billions of images are rivaling or outperforming humans at visual tasks including object recognition. Have today's DNNs become more accurate at predicting IT neuron responses to images as they have grown more accurate at object recognition? Surprisingly, across three independent experiments, we find this is not the case. DNNs have become progressively worse models of IT as their accuracy has increased on ImageNet. To understand why DNNs experience this trade-off and evaluate if they are still an appropriate paradigm for modeling the visual system, we turn to recordings of IT that capture spatially resolved maps of neuronal activity elicited by natural images. These neuronal activity maps reveal that DNNs trained on ImageNet learn to rely on different visual features than those encoded by IT and that this problem worsens as their accuracy increases. We successfully resolved this issue with the neural harmonizer, a plug-and-play training routine for DNNs that aligns their learned representations with humans. Our results suggest that harmonized DNNs break the trade-off between ImageNet accuracy and neural prediction accuracy that assails current DNNs and offer a path to more accurate models of biological vision.
The Error Analysis of Double Cubic Spline Function for Rectangular Ranges

There are wide applications of the spline function in the engineering. The estimation of its remainder is a fundamental problem of approximating theory of spline function. If function f(x,y) has the nth derivatives(n is enough), Practical method of double cubic spline funciton is given in this research. If a function can be analyzed by means of taylor expansion, remainder term R(x,y) can be estimated.
Large Language Models of Code Fail at Completing Code with Potential Bugs

Large language models of code (Code-LLMs) have recently brought tremendous advances to code completion, a fundamental feature of programming assistance and code intelligence. However, most existing works ignore the possible presence of bugs in the code context for generation, which are inevitable in software development. Therefore, we introduce and study the buggy-code completion problem, inspired by the realistic scenario of real-time code suggestion where the code context contains potential bugs -- anti-patterns that can become bugs in the completed program. To systematically study the task, we introduce two datasets: one with synthetic bugs derived from semantics-altering operator changes (buggy-HumanEval) and one with realistic bugs derived from user submissions to coding problems (buggy-FixEval). We find that the presence of potential bugs significantly degrades the generation performance of the high-performing Code-LLMs. For instance, the passing rates of CODEGEN-2B-MONO on test cases of buggy-HumanEval drop more than 50% given a single potential bug in the context. Finally, we investigate several post-hoc methods for mitigating the adverse effect of potential bugs and find that there remains a significant gap in post-mitigation performance.
Potential impacts of environmental bacteria on the microbiota of loggerhead (Caretta caretta) and green (Chelonia mydas) sea turtle eggs and their hatching success

Sea turtle hatching success can be affected by many variables, including pathogenic microbes, but it is unclear which microbes are most impactful and how they are transmitted into the eggs. This study characterized and compared the bacterial communities from the (i) cloaca of nesting sea turtles (ii) sand within and surrounding the nests; and (iii) hatched and unhatched eggshells from loggerhead (Caretta caretta) and green (Chelonia mydas) turtles. High throughput sequencing of bacterial 16S ribosomal RNA gene V4 region amplicons was performed on samples collected from 27 total nests in Fort Lauderdale and Hillsboro beaches in southeast Florida, United States. Significant differences were identified between hatched and unhatched egg microbiota with the differences caused predominately by Pseudomonas spp. , found in higher abundances in unhatched eggs (19.29% relative abundance) than hatched eggs (1.10% relative abundance). Microbiota similarities indicate that the nest sand environment, particularly nest distance from dunes, played a larger role than the nesting mother's cloaca in influencing hatched and unhatched egg microbiota. Pathogenic bacteria potentially derive from mixed‐mode transmission or additional sources not included in this study as suggested by the high proportion (24%–48%) of unhatched egg microbiota derived from unknown sources. Nonetheless, the results suggest Pseudomonas as a candidate pathogen or opportunistic colonizer associated with sea turtle egg‐hatching failure.
Complexity of Anchored Crossing Number and Crossing Number of Almost Planar Graphs

In this paper we deal with the problem of computing the exact crossing number of almost planar graphs and the closely related problem of computing the exact anchored crossing number of a pair of planar graphs. It was shown by [Cabello and Mohar, 2013] that both problems are NP-hard; although they required an unbounded number of high-degree vertices (in the first problem) or an unbounded number of anchors (in the second problem) to prove their result. Somehow surprisingly, only three vertices of degree greater than 3, or only three anchors, are sufficient to maintain hardness of these problems, as we prove here. The new result also improves the previous result on hardness of joint crossing number on surfaces by [Hlin\v{e}n\'y and Salazar, 2015]. Our result is best possible in the anchored case since the anchored crossing number of a pair of planar graphs with two anchors each is trivial, and close to being best possible in the almost planar case since the crossing number is efficiently computable for almost planar graphs of maximum degree 3 [Riskin 1996, Cabello and Mohar 2011].
AI-Supported Assessment of Load Safety

Load safety assessment and compliance is an essential step in the corporate process of every logistics service provider. In 2020, a total of 11,371 police checks of trucks were carried out, during which 9.6% (1091) violations against the load safety regulations were detected. For a logistic service provider, every load safety violation results in height fines and damage to reputation. An assessment of load safety supported by artificial intelligence (AI) will reduce the risk of accidents by unsecured loads and fines during safety assessments. This work shows how photos of the load, taken by the truck driver or the loadmaster after the loading process, can be used to assess load safety. By a trained two-stage artificial neural network (ANN), these photos are classified into three different classes I) cargo loaded safely, II) cargo loaded unsafely, and III) unusable image. By applying several architectures of convolutional neural networks (CNN), it can be shown that it is possible to distinguish between unusable and usable images for cargo safety assessment. This distinction is quite crucial since the truck driver and the loadmaster sometimes provide photos without the essential image features like the case structure of the truck and the whole cargo. A human operator or another ANN will then assess the load safety within the second stage.
Individual fairness under Varied Notions of Group Fairness in Bipartite Matching -- One Framework to Approximate Them Al

We consider the problem of assigning items to platforms while satisfying group and individual fairness constraints. Each item is associated with certain groups and has a preference ordering over platforms. Each platform enforces group fairness by specifying an upper and a lower bound on the number of items that can be matched to it from each group. Although there may be multiple optimal solutions that satisfy the group fairness constraints, we aim to achieve `probabilistic individual fairness' by computing a distribution over `group fair' matchings such that each item has a reasonable probability of being matched to one of its top choices. When each item can belong to multiple groups, the problem of finding a maximum size group-fair matching is NP-hard even when all the group lower bounds are 0, and there are no individual fairness constraints. Given a total of $n$ items, we achieve a $O(\Delta \log n)$ approximation algorithm when an item can belong to at most $\Delta$ groups, and all the group lower bounds are 0. We also provide two approximation algorithms in terms of the total number of groups that have items in the neighborhood of a platform. When each item belongs to a single group, we provide a polynomial-time algorithm that computes a probabilistic individually fair distribution over group fair matching. We further extend our model and algorithms to address the following notions of fairness: `maxmin group fairness', which maximizes the representation of the worst-off groups, and `mindom group fairness', which minimizes the representation of the most dominant groups.
Enhancing Exploration in Latent Space Bayesian Optimization

Latent Space Bayesian Optimization (LSBO) combines generative models, typically Variational Autoencoders (VAE), with Bayesian Optimization (BO) to generate de novo objects of interest. However, LSBO faces challenges due to the mismatch between the objectives of BO and VAE, resulting in poor extrapolation capabilities. In this paper, we propose novel contributions to enhance LSBO efficiency and overcome this challenge. We first introduce the concept of latent consistency/inconsistency as a crucial problem in LSBO, arising from the BO-VAE mismatch. To address this, we propose the Latent Consistent Aware-Acquisition Function (LCA-AF) that leverages consistent regions in LSBO. Additionally, we present LCA-VAE, a novel VAE method that generates a latent space with increased consistent points, improving BO's extrapolation capabilities. Combining LCA-VAE and LCA-AF, we develop LCA-LSBO. Experimental evaluations validate the improved performance of LCA-LSBO in image generation and de-novo chemical design tasks, showcasing its enhanced extrapolation capabilities in LSBO. Our approach achieves high sample-efficiency and effective exploration, emphasizing the significance of addressing latent consistency and leveraging LCA-VAE in LSBO.