Miracle-dz/UniFAR-FTUs huggingface dataset:
Tissue Segmentation of Thick-Slice Fetal Brain MR Scans With Guidance From High-Quality Isotropic Volumes

Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development. However, this task is challenging due to the use of thick-slice scans in clinically-acquired fetal brain data. To address this issue, we propose to leverage high-quality isotropic fetal brain MR volumes (and also their corresponding annotations) as guidance for segmentation of thick-slice scans. Due to existence of significant domain gap between high-quality isotropic volume (i.e., source data) and thick-slice scans (i.e., target data), we employ a domain adaptation technique to achieve the associated knowledge transfer (from high-quality “source” volumes to thick-slice “target” scans). Specifically, we first register the available high-quality isotropic fetal brain MR volumes across different gestational weeks to construct longitudinally-complete source data. To capture domain-invariant information, we then perform Fourier decomposition to extract image content and style codes. Finally, we propose a novel Cycle-Consistent Domain Adaptation Network (C<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>DA-Net) to efficiently transfer the knowledge learned from high-quality isotropic volumes for accurate tissue segmentation of thick-slice scans. Our C<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>DA-Net can fully utilize a small set of annotated isotropic volumes to guide tissue segmentation on unannotated thick-slice scans. Extensive experiments on a large-scale dataset of 372 clinically acquired thick-slice MR scans demonstrate that our C<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>DA-Net achieves much better performance than cutting-edge methods quantitatively and qualitatively.
The effect of host population heterogeneity on epidemic outbreaks

In the first part of this paper, we review old and new results about the influence of host population heterogeneity on (various characteristics of) epidemic outbreaks. In the second part we highlight a modelling issue that so far has received little attention: how do contact patterns, and hence transmission opportunities, depend on the size and the composition of the host population? Without any claim on completeness, we offer a range of potential (quasi-mechanistic) submodels. The overall aim of the paper is to describe the state-of-the-art and to catalyse new work.
Characteristic $p$ analogues of the Mumford--Tate and Andr\'e--Oort conjectures for products of ordinary GSpin Shimura varieties

Let $p$ be an odd prime. We state characteristic $p$ analogues of the Mumford--Tate conjecture and the Andr\'e--Oort conjecture for ordinary strata of mod $p$ Shimura varieties. We prove the conjectures for arbitrary products of GSpin Shimura varieties (and their subvarieties). Important subvarieties of GSpin Shimura varieties include modular and Shimura curves, Hilbert modular surfaces, $\mathrm{U}(1,n)$ unitary Shimura varieties, and moduli spaces of principally polarized Abelian and K3 surfaces. The two conjectures are both related to a notion of linearity for mod $p$ Shimura varieties, about which Chai has formulated the Tate-linear conjecture. Though seemingly different, the three conjectures are intricately entangled. We will first solve the Tate-linear conjecture for single GSpin Shimura varieties, above which we build the proof of the Tate-linear conjecture and the characteristic $p$ analogue of the Mumford--Tate conjecture for products of GSpin Shimura varieties. We then use the Tate-linear and the characteristic $p$ analogue of the Mumford--Tate conjectures to prove the characteristic $p$ analogue of the Andr\'e--Oort conjecture. Our proof uses Chai's results on monodromy of $p$-divisible groups and rigidity theorems for formal tori, as well as Crew's parabolicity conjecture which is recently proven by D'Addezio.
Algebraic solution of tropical best approximation problems

We introduce new discrete best approximation problems, formulated and solved in the framework of tropical algebra, which deals with semirings and semifields with idempotent addition. Given a set of samples, each consisting of the input and output of an unknown function defined on an idempotent semifield, the problem is to find a best approximation of the function, by tropical Puiseux polynomial and rational functions. A new solution approach is proposed, which involves the reduction of the problem of polynomial approximation to the best approximate solution of a tropical linear vector equation with an unknown vector on one side (a one-sided equation). We derive a best approximate solution to the one-sided equation, and we evaluate the inherent approximation error in a direct analytical form. Furthermore, we reduce the rational approximation problem to the best approximate solution of an equation with unknown vectors on both sides (a two-sided equation). A best approximate solution to the two-sided equation is obtained in numerical form, by using an iterative alternating algorithm. To illustrate the new technique developed, we solve example approximation problems in terms of a real semifield, where addition is defined as maximum and multiplication as arithmetic addition (max-plus algebra), which corresponds to the best Chebyshev approximation by piecewise linear functions.
Exploring the optimal cycle for a quantum heat engine using reinforcement learning

Quantum thermodynamic relationships in emerging nanodevices are significant but often complex to deal with. The application of machine learning in quantum thermodynamics has provided a new perspective. This study employs reinforcement learning to output the optimal cycle of quantum heat engine. Specifically, the soft actor-critic algorithm is adopted to optimize the cycle of three-level coherent quantum heat engine with the aim of maximal average power. The results show that the optimal average output power of the coherent three-level heat engine is 1.28 times greater than the original cycle (steady limit). Meanwhile, the efficiency of the optimal cycle is greater than the Curzon-Ahlborn efficiency as well as reporting by other researchers. Notably, this optimal cycle can be fitted as an Otto-like cycle by applying the Boltzmann function during the compression and expansion processes, which illustrates the effectiveness of the method.
Economic complexity and the sustainability transition: A review of data, methods, and literature

Economic Complexity (EC) methods have gained increasing popularity across fields and disciplines. In particular, the EC toolbox has proved particularly promising in the study of complex and interrelated phenomena, such as the transition towards a greener economy. Using the EC approach, scholars have been investigating the relationship between EC and sustainability, proposing to identify the distinguishing characteristics of green products and to assess the readiness of productive and technological structures for the sustainability transition. This article proposes to review and summarize the data, methods, and empirical literature that are relevant to the study of the sustainability transition from an EC perspective. We review three distinct but connected blocks of literature on EC and environmental sustainability. First, we survey the evidence linking measures of EC to indicators related to environmental sustainability. Second, we review articles that strive to assess the green competitiveness of productive systems. Third, we examine evidence on green technological development and its connection to non-green knowledge bases. Finally, we summarize the findings for each block and identify avenues for further research in this recent and growing body of empirical literature.
Note on Harada’s conformal Killing gravity

We show that"Gravity at cosmological distances: Explaining the accelerating expansion without dark energy"recently proposed by J. Harada [6] is equivalent to the Einstein equation extended by the presence of an arbitrary conformal Killing tensor. This turns Harada's equations of third order in the derivatives of the metric tensor to second order, and offers a simple strategy of solution that shortcuts Harada's derivation and obtains both modified Friedmann equations. An application is presented for the case of flat space and constant curvature.
Neural Super-Resolution for Real-time Rendering with Radiance Demodulation

It is time-consuming to render high-resolution images in applications such as video games and virtual reality, and thus super-resolution technologies become increasingly popular for real-time rendering. However, it is challenging to preserve sharp texture details, keep the temporal stability and avoid the ghosting artifacts in real-time super-resolution rendering. To address this issue, we introduce radiance demodulation to separate the rendered image or radiance into a lighting component and a material component, considering the fact that the light component is smoother than the rendered image so that the high-resolution material component with detailed textures can be easily obtained. We perform the super-resolution on the lighting component only and re-modulate it with the high-resolution material component to obtain the final super-resolution image with more texture details. A reliable warping module is proposed by explicitly marking the occluded regions to avoid the ghosting artifacts. To further enhance the temporal stability, we design a frame-recurrent neural network and a temporal loss to aggregate the previous and current frames, which can better capture the spatial-temporal consistency among reconstructed frames. As a result, our method is able to produce temporally stable results in real-time rendering with high-quality details, even in the challenging 4 $\times$ 4 super-resolution scenarios.
Fast and effortless computation of profile likelihoods using CONNECT

The frequentist method of profile likelihoods has recently received renewed attention in the field of cosmology. This is because the results of inferences based on the latter may differ from those of Bayesian inferences, either because of prior choices or because of non-Gaussianity in the likelihood function. Consequently, both methods are required for a fully nuanced analysis. However, in the last decades, cosmological parameter estimation has largely been dominated by Bayesian statistics due to the numerical complexity of constructing profile likelihoods, arising mainly from the need for a large number of gradient-free optimisations of the likelihood function. In this paper, we show how to accommodate the computational requirements of profile likelihoods using the publicly available neural network framework connect together with a novel modification of the gradient-based basin-hopping optimisation algorithm. Apart from the reduced evaluation time of the likelihood due to the neural network, we also achieve an additional speed-up of 1–2 orders of magnitude compared to profile likelihoods computed with the gradient-free method of simulated annealing, with excellent agreement between the two. This allows for the production of typical triangle plots normally associated with Bayesian marginalisation within cosmology (and previously unachievable using likelihood maximisation because of the prohibitive computational cost). We have tested the setup on three cosmological models: the ΛCDM model, an extension with varying neutrino mass, and finally a decaying cold dark matter model. Given the default precision settings in connect, we achieve a high precision in χ2 with a difference to the results obtained by class of Δχ2 ≈ 0.2 (and, importantly, without any bias in inferred parameter values) — easily good enough for profile likelihood analyses.
The relativistic Euler equations: ESI notes on their geo-analytic structures and implications for shocks in 1D and multi-dimensions

In this article, we provide notes that complement the lectures on the relativistic Euler equations and shocks that were given by the second author at the program Mathematical Perspectives of Gravitation Beyond the Vacuum Regime, which was hosted by the Erwin Schrödinger International Institute for Mathematics and Physics in Vienna in February 2022. We set the stage by introducing a standard first-order formulation of the relativistic Euler equations and providing a brief overview of local well-posedness in Sobolev spaces. Then, using Riemann invariants, we provide the first detailed construction of a localized subset of the maximal globally hyperbolic developments of an open set of initially smooth, shock-forming isentropic solutions in 1D, with a focus on describing the singular boundary and the Cauchy horizon that emerges from the singularity. Next, we provide an overview of the new second-order formulation of the 3D relativistic Euler equations derived in Disconzi and Speck (2019 Ann. Henri Poincare 20 2173–270), its rich geometric and analytic structures, their implications for the mathematical theory of shock waves, and their connection to the setup we use in our 1D analysis of shocks. We then highlight some key prior results on the study of shock formation and related problems. Furthermore, we provide an overview of how the formulation of the flow derived in Disconzi and Speck (2019 Ann. Henri Poincare 20 2173–270) can be used to study shock formation in multiple spatial dimensions. Finally, we discuss various open problems tied to shocks.
PQC-HA: A Framework for Prototyping and In-Hardware Evaluation of Post-Quantum Cryptography Hardware Accelerators

In the third round of the NIST Post-Quantum Cryptography standardization project, the focus is on optimizing software and hardware implementations of candidate schemes. The winning schemes are CRYSTALS Kyber and CRYSTALS Dilithium, which serve as a Key Encapsulation Mechanism (KEM) and Digital Signature Algorithm (DSA), respectively. This study utilizes the TaPaSCo open-source framework to create hardware building blocks for both schemes using High-level Synthesis (HLS) from minimally modified ANSI C software reference implementations across all security levels. Additionally, a generic TaPaSCo host runtime application is developed in Rust to verify their functionality through the standard NIST interface, utilizing the corresponding Known Answer Test mechanism on actual hardware. Building on this foundation, the communication overhead for TaPaSCo hardware accelerators on PCIe-connected FPGA devices is evaluated and compared with previous work and optimized AVX2 software reference implementations. The results demonstrate the feasibility of verifying and evaluating the performance of Post-Quantum Cryptography accelerators on real hardware using TaPaSCo. Furthermore, the off-chip accelerator communication overhead of the NIST standard interface is measured, which, on its own, outweighs the execution wall clock time of the optimized software reference implementation of Kyber at Security Level 1.
Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection

Camouflaged objects that blend into natural scenes pose significant challenges for deep-learning models to detect and synthesize. While camouflaged object detection is a crucial task in computer vision with diverse real-world applications, this research topic has been constrained by limited data availability. We propose a framework for synthesizing camouflage data to enhance the detection of camouflaged objects in natural scenes. Our approach employs a generative model to produce realistic camouflage images, which can be used to train existing object detection models. Specifically, we use a camouflage environment generator supervised by a camouflage distribution classifier to synthesize the camouflage images, which are then fed into our generator to expand the dataset. Our framework outperforms the current state-of-the-art method on three datasets (COD10k, CAMO, and CHAMELEON), demonstrating its effectiveness in improving camouflaged object detection. This approach can serve as a plug-and-play data generation and augmentation module for existing camouflaged object detection tasks and provides a novel way to introduce more diversity and distributions into current camouflage datasets.
ADRMX: Additive Disentanglement of Domain Features with Remix Loss

The common assumption that train and test sets follow similar distributions is often violated in deployment settings. Given multiple source domains, domain generalization aims to create robust models capable of generalizing to new unseen domains. To this end, most of existing studies focus on extracting domain invariant features across the available source domains in order to mitigate the effects of inter-domain distributional changes. However, this approach may limit the model's generalization capacity by relying solely on finding common features among the source domains. It overlooks the potential presence of domain-specific characteristics that could be prevalent in a subset of domains, potentially containing valuable information. In this work, a novel architecture named Additive Disentanglement of Domain Features with Remix Loss (ADRMX) is presented, which addresses this limitation by incorporating domain variant features together with the domain invariant ones using an original additive disentanglement strategy. Moreover, a new data augmentation technique is introduced to further support the generalization capacity of ADRMX, where samples from different domains are mixed within the latent space. Through extensive experiments conducted on DomainBed under fair conditions, ADRMX is shown to achieve state-of-the-art performance. Code will be made available at GitHub after the revision process.
Agrotechnological Options for Upscaling Agricultural Productivity in Eastern Indo Gangetic Plains under Impending Climate Change Situations: A Review

This review article scrutiniz some of the vibrant technologies developed/ refined/adopted to improve agricultural system productivity in the Eastern Indo Gangetic plains. Efficient agronomic research and technological development for improving agricultural productivity in eastern region are the only options to feed our people under this difficult situation. These tested and refined technologies are not only capable of improving productivity in a sustainable manner but are equally efficient in minimizing the outbreak of insects and disease pests. Important agro-technology developed and refined for Eastern Region conditions are briefly discussed in this article.
Improved YOLOv8 Detection Algorithm in Security Inspection Image

Security inspection is the first line of defense to ensure the safety of people's lives and property, and intelligent security inspection is an inevitable trend in the future development of the security inspection industry. Aiming at the problems of overlapping detection objects, false detection of contraband, and missed detection in the process of X-ray image detection, an improved X-ray contraband detection algorithm CSS-YOLO based on YOLOv8s is proposed.
GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher

Safety lies at the core of the development of Large Language Models (LLMs). There is ample work on aligning LLMs with human ethics and preferences, including data filtering in pretraining, supervised fine-tuning, reinforcement learning from human feedback, and red teaming, etc. In this study, we discover that chat in cipher can bypass the safety alignment techniques of LLMs, which are mainly conducted in natural languages. We propose a novel framework CipherChat to systematically examine the generalizability of safety alignment to non-natural languages -- ciphers. CipherChat enables humans to chat with LLMs through cipher prompts topped with system role descriptions and few-shot enciphered demonstrations. We use CipherChat to assess state-of-the-art LLMs, including ChatGPT and GPT-4 for different representative human ciphers across 11 safety domains in both English and Chinese. Experimental results show that certain ciphers succeed almost 100% of the time to bypass the safety alignment of GPT-4 in several safety domains, demonstrating the necessity of developing safety alignment for non-natural languages. Notably, we identify that LLMs seem to have a ''secret cipher'', and propose a novel SelfCipher that uses only role play and several demonstrations in natural language to evoke this capability. SelfCipher surprisingly outperforms existing human ciphers in almost all cases. Our code and data will be released at https://github.com/RobustNLP/CipherChat.
Impact-Aware Multi-Contact Balance Criteria

Intentionally applying impacts while maintaining balance is challenging for legged robots. This study originated from observing experimental data of the humanoid robot HRP-4 intentionally hitting a wall with its right arm while standing on two feet. Strangely, violating the usual zero moment point balance criteria did not systematically result in a fall. To investigate this phenomenon, we propose the zero-step capture region for non-coplanar contacts, defined as the center of mass (CoM) velocity area, and validated it with push-recovery experiments employing the HRP-4 balancing on two non-coplanar contacts. To further enable on-purpose impacts, we compute the set of candidate post-impact CoM velocities accounting for frictional-impact dynamics in three dimensions, and restrict the entire set within the CoM velocity area to maintain balance with the sustained contacts during and after impacts. We illustrate the maximum contact velocity for various HRP-4 stances in simulation, indicating potential for integration into other task-space whole-body controllers or planners. This study is the first to address the challenging problem of applying an intentional impact with a kinematic-controlled humanoid robot on non-coplanar contacts.
Taboo and Collaborative Knowledge Production: Evidence from Wikipedia

By definition, people are reticent or even unwilling to talk about taboo subjects. Because subjects like sexuality, health, and violence are taboo in most cultures, important information on each of these subjects can be difficult to obtain. Are peer produced knowledge bases like Wikipedia a promising approach for providing people with information on taboo subjects? With its reliance on volunteers who might also be averse to taboo, can the peer production model produce high-quality information on taboo subjects? In this paper, we seek to understand the role of taboo in knowledge bases produced by volunteers. We do so by developing a novel computational approach to identify taboo subjects and by using this method to identify a set of articles on taboo subjects in English Wikipedia. We find that articles on taboo subjects are more popular than non-taboo articles and that they are frequently vandalized. Despite frequent vandalism attacks, we also find that taboo articles are higher quality than non-taboo articles. We hypothesize that stigmatizing societal attitudes will lead contributors to taboo subjects to seek to be less identifiable. Although our results are consistent with this proposal in several ways, we surprisingly find that contributors make themselves more identifiable in others.