Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Compressed Segmentation Volumes for Scientific Visualization

Voxel-based segmentation volumes often store a large number of labels and voxels, and the resulting amount of data can make storage, transfer, and interactive visualization difficult. We present a lossless compression technique which addresses these challenges. It processes individual small bricks of a segmentation volume and compactly encodes the labelled regions and their boundaries by an iterative refinement scheme. The result for each brick is a list of labels, and a sequence of operations to reconstruct the brick which is further compressed using rANS-entropy coding. As the relative frequencies of operations are very similar across bricks, the entropy coding can use global frequency tables for an entire data set which enables efficient and effective parallel (de)compression. Our technique achieves high throughput (up to gigabytes per second both for compression and decompression) and strong compression ratios of about 1% to 3% of the original data set size while being applicable to GPU-based rendering. We evaluate our method for various data sets from different fields and demonstrate GPU-based volume visualization with on-the-fly decompression, level-of-detail rendering (with optional on-demand streaming of detail coefficients to the GPU), and a caching strategy for decompressed bricks for further performance improvement.
Variation of Clasper Scent Gland Composition of Heliconius Butterflies from a Biodiversity Hotspot

Male Heliconius butterflies possess two pheromone emitting structures, wing androconia and abdominal clasper scent glands. The composition of the clasper scent gland of males of 17 Heliconius and Eueides species from an overlapping area in Ecuador, comprising three mimicry groups, was investigated by GC/MS. The chemical signal serves as an anti‐aphrodisiac signal that is transferred from males to females during mating, indicating the mating status of the female to prevent them from harassment by other males. In addition, the odour may also serve in predator defence. There is potential for convergence driven by mimicry, although, such convergence might be detrimental for species recognition of the butterflies within the mimicry ring, making mating more difficult. More than 500 compounds were detected, consisting of volatile, semi‐volatile or non‐volatile compounds, including terpenes, fatty acid esters or aromatic compounds. Several novel esters were identified by GC/MS and GC/IR data, microderivatisation and synthesis, including butyl (Z)‐3‐dodecenoate and other (Z)‐3‐alkenoates, 3‐oxohexyl citronellate and 5‐methylhexa‐3,5‐dienyl (E)‐2,3‐dihydrofarnesoate. The secretions were found to be species specific, potentially allowing for species differentiation. Statistical analysis of the compounds showed differentiation by phylogenetic clade and species, but not by mimicry group.
Schizophrenia mediating the effect of smoking phenotypes on antisocial behavior: A Mendelian randomization analysis

Abstract Aims Previous studies have indicated that smoking is linked to an increased risk of developing schizophrenia, and that individuals with schizophrenia are more prone to engaging in antisocial behavior. However, the causal effects of smoking behaviors on antisocial behavior and the potential mediating role of schizophrenia remains largely unclear. Methods In the present study, using the summary data from genome wide association studies of smoking phenotypes (N = 323,386–805,431), schizophrenia (Ncases = 53,386, Ncontrols = 77,258), and antisocial behavior (N = 85,359), we assessed bidirectional causality between smoking phenotypes and schizophrenia by the Mendelian randomization (MR) approach. Using a two‐step MR approach, we further examined whether causal effects of smoking phenotypes/schizophrenia on antisocial behavior were mediated by schizophrenia/smoking phenotypes. Results The results showed that smoking initiation (SmkInit) and age of smoking initiation (AgeSmk) causally increase the risk of schizophrenia (SmkInit: OR = 2.06, 95% CI = 1.77–2.39, p = 4.36 × 10−21; AgeSmk: OR = 0.32, 95% CI = 0.16–0.62, p = 8.11 × 10−4, Bonferroni corrected). However, there was no causal effect that liability to schizophrenia leads to smoking phenotypes. MR evidence also revealed causal influences of SmkInit and the amount smoked (CigDay) on antisocial behavior (SmkInit: OR = 1.28, 95% CI = 1.17–1.41, p = 2.53 × 10−7; CigDay: OR = 1.16, 95% CI = 1.06–1.27, p = 1.60 × 10−3, Bonferroni corrected). Furthermore, the mediation analysis indicated that the relationship between SmkInit and antisocial behavior was partly mediated by schizophrenia (mediated proportion = 6.92%, 95% CI = 0.004–0.03, p = 9.66 × 10−3). Conclusions These results provide compelling evidence for taking smoking interventions as a prevention strategy for schizophrenia and its related antisocial behavior.
Charmless 
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 decays: The fully antisymmetric final state

Under flavor $SU(3)$ symmetry (SU(3)$_F$), the final-state particles in $B\to PPP$ decays ($P$ is a pseudoscalar meson) are treated as identical, and the $PPP$ must be in a fully-symmetric (FS) state, a fully-antisymmetric (FA) state, or in one of four mixed states. In this paper, we present the formalism for the FA states. We write the amplitudes for the 22 $B\to PPP$ decays that can be in an FA state in terms of both SU(3)$_F$ reduced matrix elements and diagrams. This shows the equivalence of diagrams and SU(3)$_F$. We also give 15 relations among the amplitudes in the SU(3)$_F$ limit, as well as the additional four that appear when the diagrams $E$/$A$/$PA$ are neglected. We present sets of $B \to PPP$ decays that can be used to extract $\gamma$ using the FA amplitudes. The value(s) of $\gamma$ found in this way can be compared with the value(s) found using the FS states.
Technology development for ultraintense all-OPCPA systems

Optical parametric chirped-pulse amplification (OPCPA) [Dubietis et al., Opt. Commun. 88, 437 (1992)] implemented by multikilojoule Nd:glass pump lasers is a promising approach to produce ultraintense pulses ( ${>}10^{23}~\text{W}/\text{cm}^{2}$ ). Technologies are being developed to upgrade the OMEGA EP Laser System with the goal to pump an optical parametric amplifier line (EP OPAL) with two of the OMEGA EP beamlines. The resulting ultraintense pulses (1.5 kJ, 20 fs, $10^{24}~\text{W}/\text{cm}^{2}$ ) would be used jointly with picosecond and nanosecond pulses produced by the other two beamlines. A midscale OPAL pumped by the Multi-Terawatt (MTW) laser is being constructed to produce 7.5-J, 15-fs pulses and demonstrate scalable technologies suitable for the upgrade. MTW OPAL will share a target area with the MTW laser (50 J, 1 to 100 ps), enabling several joint-shot configurations. We report on the status of the MTW OPAL system, and the technology development required for this class of all-OPCPA laser system for ultraintense pulses.
Balancing between the Local and Global Structures (LGS) in Graph Embedding

We present a method for balancing between the Local and Global Structures (LGS) in graph embedding, via a tunable parameter. Some embedding methods aim to capture global structures, while others attempt to preserve local neighborhoods. Few methods attempt to do both, and it is not always possible to capture well both local and global information in two dimensions, which is where most graph drawing live. The choice of using a local or a global embedding for visualization depends not only on the task but also on the structure of the underlying data, which may not be known in advance. For a given graph, LGS aims to find a good balance between the local and global structure to preserve. We evaluate the performance of LGS with synthetic and real-world datasets and our results indicate that it is competitive with the state-of-the-art methods, using established quality metrics such as stress and neighborhood preservation. We introduce a novel quality metric, cluster distance preservation, to assess intermediate structure capture. All source-code, datasets, experiments and analysis are available online.
Generate Your Own Scotland: Satellite Image Generation Conditioned on Maps

Despite recent advancements in image generation, diffusion models still remain largely underexplored in Earth Observation. In this paper we show that state-of-the-art pretrained diffusion models can be conditioned on cartographic data to generate realistic satellite images. We provide two large datasets of paired OpenStreetMap images and satellite views over the region of Mainland Scotland and the Central Belt. We train a ControlNet model and qualitatively evaluate the results, demonstrating that both image quality and map fidelity are possible. Finally, we provide some insights on the opportunities and challenges of applying these models for remote sensing. Our model weights and code for creating the dataset are publicly available at https://github.com/miquel-espinosa/map-sat.
Ladder-of-Thought: Using Knowledge as Steps to Elevate Stance Detection

Stance detection aims to identify the attitude expressed in a document towards a given target. Techniques such as Chain-of-Thought (CoT) prompting have advanced this task, enhancing a model's reasoning capabilities through the derivation of intermediate rationales. However, CoT relies primarily on a model's pre-trained internal knowledge during reasoning, thereby neglecting the valuable external information that is previously unknown to the model. This omission, especially within the unsupervised reasoning process, can affect the model's overall performance. Moreover, while CoT enhances Large Language Models (LLMs), smaller LMs, though efficient operationally, face challenges in delivering nuanced reasoning. In response to these identified gaps, we introduce the Ladder-of-Thought (LoT) for the stance detection task. Constructed through a dual-phase Progressive Optimization Framework, LoT directs the small LMs to assimilate high-quality external knowledge, refining the intermediate rationales produced. These bolstered rationales subsequently serve as the foundation for more precise predictions - akin to how a ladder facilitates reaching elevated goals. LoT achieves a balance between efficiency and performance. Our empirical evaluations underscore LoT's efficacy, marking a 16% improvement over GPT-3.5 and a 10% enhancement compared to GPT-3.5 with CoT on stance detection task.
Comparative microbiome analysis reveals the variation in microbial communities between ‘Kyoho’ grape and its bud mutant variety

Microbes are an important part of the vineyard ecosystem, which significantly influence the quality of grapes. Previously, we identified a bud mutant variety (named ‘Fengzao’) from ‘Kyoho’ grapes. The variation of microbial communities in grape and its bud mutant variety has not been studied yet. So, in this study, with the samples of both ‘Fengzao’ and ‘Kyoho’, we conducted high-throughput microbiome sequencing and investigated their microbial communities in different tissues. Obvious differences were observed in the microbial communities between ‘Fengzao’ and ‘Kyoho’. The fruit and the stem are the tissues with relatively higher abundance of microbes, while the leaves contained less microbes. The fruit and the stem of ‘Kyoho’ and the stem of ‘Fengzao’ had relatively higher species diversity based on the alpha diversity analysis. Proteobacteria, Enterobacteriaceae and Rhodobacteraceae had significantly high abundance in ‘Fengzao’. Firmicutes and Pseudomonas were highly abundant in the stems of ‘Kyoho’, and family of Spirochaetaceae, Anaplasmataceae, Chlorobiaceae, and Sphingomonadaceae, and genera of Spirochaeta, Sphingomonas, Chlorobaculum and Wolbachia were abundant in the fruits of ‘Kyoho’. These identified microbes are main components of the microbial communities, and could be important regulators of grapevine growth and development. This study revealed the differences in the microbial compositions between ‘Kyoho’ and its bud mutant, and these identified microbes will be significant resources for the future researches on the quality regulation and disease control of grapevines.
DiffusionPoser: Real-time Human Motion Reconstruction From Arbitrary Sparse Sensors Using Autoregressive Diffusion

Motion capture from a limited number of body-worn sensors, such as inertial measurement units (IMUs) and pressure insoles, has important applications in health, human performance, and entertainment. Recent work has focused on accurately reconstructing whole-body motion from a specific sensor configuration using six IMUs. While a common goal across applications is to use the minimal number of sensors to achieve required accuracy, the optimal arrangement of the sensors might differ from application to application. We propose a single diffusion model, DiffusionPoser, which reconstructs human motion in real-time from an arbitrary combination of sensors, including IMUs placed at specified locations, and, pressure insoles. Unlike existing methods, our model grants users the flexibility to determine the number and arrangement of sensors tailored to the specific activity of interest, without the need for retraining. A novel autoregressive inferencing scheme ensures real-time motion reconstruction that closely aligns with measured sensor signals. The generative nature of DiffusionPoser ensures realistic behavior, even for degrees-of-freedom not directly measured. Qualitative results can be found on our website: https://diffusionposer.github. io/.
Shape of my heart: Cardiac models through learned signed distance functions

The efficient construction of an anatomical model is one of the major challenges of patient-specific in-silico models of the human heart. Current methods frequently rely on linear statistical models, allowing no advanced topological changes, or requiring medical image segmentation followed by a meshing pipeline, which strongly depends on image resolution, quality, and modality. These approaches are therefore limited in their transferability to other imaging domains. In this work, the cardiac shape is reconstructed by means of three-dimensional deep signed distance functions with Lipschitz regularity. For this purpose, the shapes of cardiac MRI reconstructions are learned from public databases to model the spatial relation of multiple chambers in Cartesian space. We demonstrate that this approach is also capable of reconstructing anatomical models from partial data, such as point clouds from a single ventricle, or modalities different from the trained MRI, such as electroanatomical mapping, and in addition, allows us to generate new anatomical shapes by randomly sampling latent vectors.
Edge-Assisted Lightweight Region-of-Interest Extraction and Transmission for Vehicle Perception

To enhance on-road environmental perception for autonomous driving, accurate and real-time analytics on high-resolution video frames generated from on-board cameras becomes crucial. In this paper, we design a lightweight object location method based on class activation mapping (CAM) to rapidly capture the region of interest (RoI) boxes that contain driving safety related objects from on-board cameras, which can not only improve the inference accuracy of vision tasks, but also reduce the amount of transmitted data. Considering the limited on-board computation resources, the RoI boxes extracted from the raw image are offloaded to the edge for further processing. Considering both the dynamics of vehicle-to-edge communications and the limited edge resources, we propose an adaptive RoI box offloading algorithm to ensure prompt and accurate inference by adjusting the down-sampling rate of each box. Extensive experimental results on four high-resolution video streams demonstrate that our approach can effectively improve the overall accuracy by up to 16 % and reduce the transmission demand by up to 49%, compared with other benchmarks.
Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception

Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems. Collaborative perception (CP) as an essential component to achieve V2X can overcome the inherent limitations of individual perception, including occlusion and long-range perception. In this survey, we provide a comprehensive review of CP methods for V2X scenarios, bringing a profound and in-depth understanding to the community. Specifically, we first introduce the architecture and workflow of typical V2X systems, which affords a broader perspective to understand the entire V2X system and the role of CP within it. Then, we thoroughly summarize and analyze existing V2X perception datasets and CP methods. Particularly, we introduce numerous CP methods from various crucial perspectives, including collaboration stages, roadside sensors placement, latency compensation, performance-bandwidth trade-off, attack/defense, pose alignment, etc. Moreover, we conduct extensive experimental analyses to compare and examine current CP methods, revealing some essential and unexplored insights. Specifically, we analyze the performance changes of different methods under different bandwidths, providing a deep insight into the performance-bandwidth trade-off issue. Also, we examine methods under different LiDAR ranges. To study the model robustness, we further investigate the effects of various simulated real-world noises on the performance of different CP methods, covering communication latency, lossy communication, localization errors, and mixed noises. In addition, we look into the sim-to-real generalization ability of existing CP methods. At last, we thoroughly discuss issues and challenges, highlighting promising directions for future efforts. Our codes for experimental analysis will be public at https://github.com/memberRE/Collaborative-Perception.
In-class Data Analysis Replications: Teaching Students while Testing Science

Science is facing a reproducibility crisis. Previous work has proposed incorporating data analysis replications into classrooms as a potential solution. However, despite the potential benefits, it is unclear whether this approach is feasible, and if so, what the involved stakeholders-students, educators, and scientists-should expect from it. Can students perform a data analysis replication over the course of a class? What are the costs and benefits for educators? And how can this solution help benchmark and improve the state of science? In the present study, we incorporated data analysis replications in the project component of the Applied Data Analysis course (CS-401) taught at EPFL (N=354 students). Here we report pre-registered findings based on surveys administered throughout the course. First, we demonstrate that students can replicate previously published scientific papers, most of them qualitatively and some exactly. We find discrepancies between what students expect of data analysis replications and what they experience by doing them along with changes in expectations about reproducibility, which together serve as evidence of attitude shifts to foster students' critical thinking. Second, we provide information for educators about how much overhead is needed to incorporate replications into the classroom and identify concerns that replications bring as compared to more traditional assignments. Third, we identify tangible benefits of the in-class data analysis replications for scientific communities, such as a collection of replication reports and insights about replication barriers in scientific work that should be avoided going forward. Overall, we demonstrate that incorporating replication tasks into a large data science class can increase the reproducibility of scientific work as a by-product of data science instruction, thus benefiting both science and students.
Least Squares Maximum and Weighted Generalization-Memorization Machines

In this paper, we propose a new way of remembering by introducing a memory influence mechanism for the least squares support vector machine (LSSVM). Without changing the equation constraints of the original LSSVM, this mechanism, allows an accurate partitioning of the training set without overfitting. The maximum memory impact model (MIMM) and the weighted impact memory model (WIMM) are then proposed. It is demonstrated that these models can be degraded to the LSSVM. Furthermore, we propose some different memory impact functions for the MIMM and WIMM. The experimental results show that that our MIMM and WIMM have better generalization performance compared to the LSSVM and significant advantage in time cost compared to other memory models.
Twofold Structured Features-Based Siamese Network for Infrared Target Tracking

Nowadays, infrared target tracking has been a critical technology in the field of computer vision and has many applications, such as motion analysis, pedestrian surveillance, intelligent detection, and so forth. Unfortunately, due to the lack of color, texture and other detailed information, tracking drift often occurs when the tracker encounters infrared targets that vary in size or shape. To address this issue, we present a twofold structured features-based Siamese network for infrared target tracking. First of all, in order to improve the discriminative capacity for infrared targets, a novel feature fusion network is proposed to fuse both shallow spatial information and deep semantic information into the extracted features in a comprehensive manner. Then, a multi-template update module based on template update mechanism is designed to effectively deal with interferences from target appearance changes which are prone to cause early tracking failures. Finally, both qualitative and quantitative experiments are carried out on VOT-TIR 2016 dataset, which demonstrates that our method achieves the balance of promising tracking performance and real-time tracking speed against other out-of-the-art trackers.
Semantic Encapsulation using Linking Types

Interoperability pervades nearly all mainstream language implementations, as most systems leverage subcomponents written in different languages. And yet, such linking can expose a language to foreign behaviors that are internally inexpressible, which poses a serious threat to safety invariants and programmer reasoning. To preserve such invariants, a language may try to add features to limit the reliance on external libraries, but endless extensions can obscure the core abstractions the language was designed to provide. In this paper, we outline an approach that encapsulates foreign code in a sound way—i.e., without disturbing the invariants promised by types of the core language. First, one introduces novel linking types that characterize the behaviors of foreign libraries that are inexpressible in the core language. To reason about the soundness of linking, one constructs a realizability model that captures the meaning of both core types and linking types as sets of target-language terms. Using this model, one can formally prove when foreign behavior is encapsulated; that is, unobservable to core code. We show one way to discharge such proofs automatically by augmenting the compiler to insert verified encapsulation wrappers around components that use foreign libraries. Inspired by existing approaches to FFIs, we develop a pair of case studies that extend a pure, functional language: one extension for state, and another for exceptions. The first allows us to implement mutable references via a library, whereas the second allows us to implement try and catch as library functions. Both extensions and the overall system are proven sound using logical relations that use realizability techniques.