Miracle-dz/UniFAR-FTUs huggingface dataset:
Druggable causal genes of bipolar disorder identified through Mendelian Randomization analysis offer a route to intervention in integrated stress response

Protein misfolding and aggregation, mitochondrial dysfunction, and oxidative stress are all common features of age-associated proteinopathies. As a consequence, protein synthesis, folding, and degradation are altered in these disorders. The ISR acts as a central regulator of protein homeostasis. Genome wide association studies (GWASes) on bipolar disorder (BD) have been conducted; however, as a causative mechanism underlying the cognitive deficits and neurodegeneration in a wide broad range of brain disorders, the impact of ISR on BD is understudied, particularly for current GWASes of molecular phenotypes have limited sample sizes and limited protein coverages. And many genes and gene products are not easily druggable, so some potentially important causal genes may not offer an obvious route to intervention. We aimed to prioritize BD associated genes for functional follow-up studies using the summary data-based Mendelian Randomization (SMR) and Bayesian colocalization (COLOC) methods to integrate the summary-level data of the GWAS on BD and the expression quantitative trait locus (eQTL) study in blood. We utilized the GWAS data including 41917 BD cases and 371549 controls from the Psychiatric Genomics Consortium (PGC) and the eQTL data from 31,684 participants of predominantly European ancestry from the eQTLGen consortium. Plus, we use Graph DB to alt drug target. The SMR analysis identified EIF2B5 gene that were associated with DB due to not linkage but pleiotropy or causality. The COLOC analysis strongly suggested that EIF2B5 and the trait of BD were affected by shared causal variants, and thus were colocalized. By means of data in EpiGraphDB we find other putative causal BD genes (EIF2AK4 and GSK3B) to prioritise potential alternative drug targets.
PERFORMANCE OF SPECIES Mabea fistulifera AND Eucalyptus urograndis WITH USE OF CELLULOSE RESIDUE IN DEGRADED AREAS

The planting of forest species contributes to the recovery of degraded areas, and the use of industrial waste can favor this process. However, there is still little information on this aspect in Cerrado. The objective of this work was to evaluate the performance of seedlings of the native species Mabea fistulifera Mart. and of the exotic hybrid Eucalyptus urograndis, under fertilization with residue of the industrial production of cellulose, in monospecific plantations in degraded area. Fertilization treatments, applied in the planting line, were three doses of the residue (10, 15 and 20 Mg ha -1); conventional mineral fertilizer (NPK); absence of fertilization (control). The experimental design was a randomized block design, with a portion of subdivided parts: species in the portions (15 m x 60 m, n = 4 / species) and treatments in the subportions (15 m x 12 m, n = 1 / treatment / portion) total of 18 plants in each subplot. Survival rate and growth attributes (height, crown diameter, stem diameter at ground level) were evaluated for two consecutive years. The largest increases in seedling survival and growth of both species were provided by mineral fertilization, followed by the 10 Mg ha -1 dose of the residue, which should be the recommended.
Quantitative reconstruction of Holocene sea ice and sea surface temperature off West Greenland from the first regional diatom data set

Holocene oceanographic conditions in Disko Bay, West Greenland, were reconstructed from high-resolution diatom records derived from two marine sediment cores. A modern data set composed of 35 dated surface sediment samples collected along the West Greenland coast accompanied by remote sensing data was used to develop a diatom transfer function to reconstruct April sea ice concentration (SIC) supported by July sea surface temperature (SST) in the area. Our quantitative reconstruction shows that oceanographic changes recorded throughout the last ~11,000 years reflect seasonal interplay between spring (April SIC) and summer (July SST) conditions. Our records show clear correlation with climate patterns identified from ice core data from GISP2 and Agassiz-Renland for the early to middle Holocene. The early Holocene deglaciation of western Greenland Ice Sheet was characterized in Disko Bay by initial strong centennial-scale fluctuations in April SIC with amplitude of over 40%, followed by high April SIC and July SST. These conditions correspond to a general warming of the climate in the Northern Hemisphere. A decrease in April SIC and July SST was recorded during the Holocene Thermal Optimum reflecting more stable spring-summer conditions in Disko Bay. During the late Holocene, high April SIC characterized the Medieval Climate Anomaly, while high July SST prevailed during the Little Ice Age, supporting previously identified antiphase relationship between surface waters in West Greenland and climate in NW Europe. This antiphase pattern might reflect seasonal variations in regional oceanographic conditions and large-scale fluctuations within the North Atlantic Oscillation and Atlantic Meridional Overturning Circulation.
Strategies for the visualization of geographic time-series data

Strategies for the visual display and analysis of geographic time-series data may be spatial or nonspatial, single-view or multiple-view, static or dynamic. Labels for place names or other geographic metaphors can describe symbols on aspatial time-series charts. Single-static-map strategies incorporate the temporal dimension through techniques ranging from complex point symbols, or temporal glyphs, to generalized trend-surface or flow-linkage maps focusing on movement. The multiple-static-maps strategy juxtaposes two or more maps for a simultaneous visual comparison of time units, whereas the single-dynamic-map strategy either presents maps in a temporal sequence or shows the evolution of a geographic pattern through a temporally sequenced accretion of symbols. In contrast, the multiple-dynamic-maps strategy provides programmed sequences of multiple views or allows the viewer to interact with maps and statistical diagrams representing different instants or periods of time. Electronic graphics systems have.
Neural Point Cloud Diffusion for Disentangled 3D Shape and Appearance Generation

Controllable generation of 3D assets is important for many practical applications like content creation in movies, games and engineering, as well as in AR/VR. Recently, diffusion models have shown remarkable results in generation quality of 3D objects. However, none of the existing models enable disentangled generation to control the shape and appearance separately. For the first time, we present a suitable representation for 3D diffusion models to enable such disentanglement by introducing a hybrid point cloud and neural radiance field approach. We model a diffusion process over point positions jointly with a high-dimensional feature space for a local density and radiance decoder. While the point positions represent the coarse shape of the object, the point features allow modeling the geometry and appearance details. This disentanglement enables us to sample both independently and therefore to control both separately. Our approach sets a new state of the art in generation compared to previous disentanglement-capable methods by reduced FID scores of 30-90% and is on-par with other non disentanglement-capable state-of-the art methods.
Visual Tomography: Physically Faithful Volumetric Models of Partially Translucent Objects

When created faithfully from real-world data, Digital 3D representations of objects can be useful for human or computer-assisted analysis. Such models can also serve for generating training data for machine learning approaches in settings where data is difficult to obtain or where too few training data exists, e.g. by providing novel views or images in varying conditions. While the vast amount of visual 3D reconstruction approaches focus on non-physical models, textured object surfaces or shapes, in this contribution we propose a volumetric reconstruction approach that obtains a physical model including the interior of partially translucent objects such as plankton or insects. Our technique photographs the object under different poses in front of a bright white light source and computes absorption and scattering per voxel. It can be interpreted as visual tomography that we solve by inverse raytracing. We additionally suggest a method to convert non-physical NeRF media into a physically-based volumetric grid for initialization and illustrate the usefulness of the approach using two real-world plankton validation sets, the lab-scanned models being finally also relighted and virtually submerged in a scenario with augmented medium and illumination conditions. Please visit the project homepage at www.marine. informatik.uni-kiel.de/go/vito
Laminar flow synthesis of submicron CaCO$_3$ particles in 3D printed microfluidic chips

Microfluidic technology provides a solution to the challenge of continuous CaCO$_3$ particle synthesis. In this study, we utilized a 3D-printed microfluidic chip to synthesize CaCO$_3$ micro and nanoparticles in vaterite form. Our primary focus was on investigating a continuous one-phase synthesis method tailored for the crystallization of these particles. By employing a combination of confocal and scanning electron microscopy, along with Raman spectroscopy, we were able to thoroughly evaluate the synthesis efficiency. This evaluation included aspects such as particle size distribution, morphology, and polymorph composition. The results unveiled the existence of two distinct synthesis regimes within the 3D-printed microfluidic chips, featuring a channel cross-section of 2 mm$^2$. In the first regime, characterized by turbulence, particles with an average diameter of around 2 $\mu$m were produced, displaying a broad size distribution. Conversely, the second regime, marked by laminar flow, led to the synthesis of submicron particles (approximately 800-900 nm in diameter) and even nanosized particles (70-80 nm). This research significantly contributes valuable insights to both the understanding and optimization of microfluidic synthesis processes, particularly in achieving controlled production of submicron and nanoscale particles.
SE(3)-Equivariant and Noise-Invariant 3D Motion Tracking in Medical Images

Rigid motion tracking is paramount in many medical imaging applications where movements need to be detected, corrected, or accounted for. Modern strategies rely on convolutional neural networks (CNN) and pose this problem as rigid registration. Yet, CNNs do not exploit natural symmetries in this task, as they are equivariant to translations (their outputs shift with their inputs) but not to rotations. Here we propose EquiTrack, the first method that uses recent steerable SE(3)-equivariant CNNs (E-CNN) for motion tracking. While steerable E-CNNs can extract corresponding features across different poses, testing them on noisy medical images reveals that they do not have enough learning capacity to learn noise invariance. Thus, we introduce a hybrid architecture that pairs a denoiser with an E-CNN to decouple the processing of anatomically irrelevant intensity features from the extraction of equivariant spatial features. Rigid transforms are then estimated in closed-form. EquiTrack outperforms state-of-the-art learning and optimisation methods for motion tracking in adult brain MRI and fetal MRI time series. Our code is available at github.com/BBillot/equitrack.
An integrated framework for accelerating reactive flow simulation using GPU and machine learning models

Recent progress in artificial intelligence (AI) and high-performance computing (HPC) have brought potentially game-changing opportunities in accelerating reactive flow simulations. In this study, we introduce an open-source computational fluid dynamics (CFD) framework that integrates the strengths of machine learning (ML) and graphics processing unit (GPU) to demonstrate their combined capability. Within this framework, all computational operations are solely executed on GPU, including ML-accelerated chemistry integration, fully-implicit solving of PDEs, and computation of thermal and transport properties, thereby eliminating the CPU-GPU memory copy overhead. Optimisations both within the kernel functions and during the kernel launch process are conducted to enhance computational performance. Strategies such as static data reorganisation and dynamic data allocation are adopted to reduce the GPU memory footprint. The computational performance is evaluated in two turbulent flame benchmarks using quasi-DNS and LES modelling, respectively. Remarkably, while maintaining a similar level of accuracy to the conventional CPU/CVODE-based solver, the GPU/ML-accelerated approach shows an overall speedup of over two orders of magnitude for both cases. This result highlights that high-fidelity turbulent combustion simulation with finite-rate chemistry that requires normally hundreds of CPUs can now be performed on portable devices such as laptops with a medium-end GPU.
Higher-Order Gravity, Finite Action, and a Safe Beginning for the Universe

General relativity allows for inhomogeneous and anisotropic universes with finite action. By contrast, in quadratic gravity such solutions obtain infinite action and are thus eliminated. What remains are homogeneous and isotropic solutions undergoing accelerated expansion, thereby automatically inducing an early inflationary phase. In this manner, semi-classical consistency may explain some of the basic, coarse-grained features of the early universe. This includes suitable initial conditions for the second law of thermodynamics, in the spirit of the Weyl curvature hypothesis. We note that quadratic gravity is a renormalisable theory and may admit an asymptotically safe regime at high energies, rendering the theory trustworthy to high energies. We also comment on theories containing curvature terms up to infinite derivative order, and on the contrast with no-boundary initial conditions.
Born-Infeld Supermaze Waves

The entropy of the supersymmetric D2-D4-P black hole comes at weak coupling from D2-brane strips stretched between parallel D4 branes and carrying momentum waves. We use the DBI action of D4 branes to construct two pieces of plumbing that enter in the construction of these microstates. The first is a semi-infinite D2 brane ending on a D4 brane and carrying a momentum wave along the common D2-D4 direction. The second is a non-Abelian solution to the 5D maximally-supersymmetric SU(2) Super-Yang-Mills theory describing a momentum-carrying D2 strip stretched between two D4 branes. The solution without momentum is the same as the 't Hooft-Polyakov monopole, and the fields that carry the momentum can be added without changing any of the fields of the monopole.
Shared Affordance-awareness via Augmented Reality for Proactive Assistance in Human-robot Collaboration

Enabling humans and robots to collaborate effectively requires purposeful communication and an understanding of each other's affordances. Prior work in human-robot collaboration has incorporated knowledge of human affordances, i.e., their action possibilities in the current context, into autonomous robot decision-making. This"affordance awareness"is especially promising for service robots that need to know when and how to assist a person that cannot independently complete a task. However, robots still fall short in performing many common tasks autonomously. In this work-in-progress paper, we propose an augmented reality (AR) framework that bridges the gap in an assistive robot's capabilities by actively engaging with a human through a shared affordance-awareness representation. Leveraging the different perspectives from a human wearing an AR headset and a robot's equipped sensors, we can build a perceptual representation of the shared environment and model regions of respective agent affordances. The AR interface can also allow both agents to communicate affordances with one another, as well as prompt for assistance when attempting to perform an action outside their affordance region. This paper presents the main components of the proposed framework and discusses its potential through a domestic cleaning task experiment.
Optimal Beamforming for Secure Integrated Sensing and Communication Exploiting Target Location Distribution

In this paper, we study a secure integrated sensing and communication (ISAC) system where one multi-antenna base station (BS) simultaneously communicates with one single-antenna user and senses the location parameter of a target which serves as a potential eavesdropper via its reflected echo signals. In particular, we consider a challenging scenario where the target's location is unknown and random, while its distribution information is known a priori. First, we derive the posterior Cram\'er-Rao bound (PCRB) of the mean-squared error (MSE) in target location sensing, which has a complicated expression. To draw more insights, we derive a tight approximation of it in closed form, which indicates that the transmit beamforming should achieve a"probability-dependent power focusing"effect over possible target locations, with more power focused on highly-probable locations. Next, considering an artificial noise based beamforming structure, we formulate the transmit beamforming optimization problem to maximize the worst-case secrecy rate among all possible target (eavesdropper) locations, subject to a threshold on the sensing PCRB. The formulated problem is non-convex and difficult to solve. We show that the problem can be solved via a two-stage method, by first obtaining the optimal beamforming corresponding to any given threshold on the signal-to-interference-plus-noise ratio (SINR) at the eavesdropper, and then obtaining the optimal threshold via one-dimensional search. By applying the semi-definite relaxation (SDR) technique, we relax the first problem into a convex form and further prove that the relaxation is tight, based on which the optimal solution of the original beamforming optimization problem can be obtained with polynomial-time complexity. Then, we further propose two suboptimal solutions with lower complexity. Numerical results validate the effectiveness of our designs.
BAE-Net: A Low complexity and high fidelity Bandwidth-Adaptive neural network for speech super-resolution

Speech bandwidth extension (BWE) has demonstrated promising performance in enhancing the perceptual speech quality in real communication systems. Most existing BWE researches primarily focus on fixed upsampling ratios, disregarding the fact that the effective bandwidth of captured audio may fluctuate frequently due to various capturing devices and transmission conditions. In this paper, we propose a novel streaming adaptive bandwidth extension solution dubbed BAE-Net, which is suitable to handle the low-resolution speech with unknown and varying effective bandwidth. To address the challenges of recovering both the high-frequency magnitude and phase speech content blindly, we devise a dual-stream architecture that incorporates the magnitude inpainting and phase refinement. For potential applications on edge devices, this paper also introduces BAE-NET-lite, which is a lightweight, streaming and efficient framework. Quantitative results demonstrate the superiority of BAE-Net in terms of both performance and computational efficiency when compared with existing state-of-the-art BWE methods.
Multi-Modal Domain Adaptation Across Video Scenes for Temporal Video Grounding

Temporal Video Grounding (TVG) aims to localize the temporal boundary of a specific segment in an untrimmed video based on a given language query. Since datasets in this domain are often gathered from limited video scenes, models tend to overfit to scene-specific factors, which leads to suboptimal performance when encountering new scenes in real-world applications. In a new scene, the fine-grained annotations are often insufficient due to the expensive labor cost, while the coarse-grained video-query pairs are easier to obtain. Thus, to address this issue and enhance model performance on new scenes, we explore the TVG task in an unsupervised domain adaptation (UDA) setting across scenes for the first time, where the video-query pairs in the source scene (domain) are labeled with temporal boundaries, while those in the target scene are not. Under the UDA setting, we introduce a novel Adversarial Multi-modal Domain Adaptation (AMDA) method to adaptively adjust the model's scene-related knowledge by incorporating insights from the target data. Specifically, we tackle the domain gap by utilizing domain discriminators, which help identify valuable scene-related features effective across both domains. Concurrently, we mitigate the semantic gap between different modalities by aligning video-query pairs with related semantics. Furthermore, we employ a mask-reconstruction approach to enhance the understanding of temporal semantics within a scene. Extensive experiments on Charades-STA, ActivityNet Captions, and YouCook2 demonstrate the effectiveness of our proposed method.
A Dense Subframe-based SLAM Framework with Side-scan Sonar

Side-scan sonar (SSS) is a lightweight acoustic sensor that is commonly deployed on autonomous underwater vehicles (AUVs) to provide high-resolution seafloor images. However, leveraging side-scan images for simultaneous localization and mapping (SLAM) presents a notable challenge, primarily due to the difficulty of establishing sufficient amount of accurate correspondences between these images. To address this, we introduce a novel subframe-based dense SLAM framework utilizing side-scan sonar data, enabling effective dense matching in overlapping regions of paired side-scan images. With each image being evenly divided into subframes, we propose a robust estimation pipeline to estimate the relative pose between each paired subframes, by using a good inlier set identified from dense correspondences. These relative poses are then integrated as edge constraints in a factor graph to optimize the AUV pose trajectory. The proposed framework is evaluated on three real datasets collected by a Hugin AUV. Among one of them includes manually-annotated keypoint correspondences as ground truth and is used for evaluation of pose trajectory. We also present a feasible way of evaluating mapping quality against multi-beam echosounder (MBES) data without the influence of pose. Experimental results demonstrate that our approach effectively mitigates drift from the dead-reckoning (DR) system and enables quasi-dense bathymetry reconstruction. An open-source implementation of this work is available.
Control of threshold voltages in Si/SiGe quantum devices via optical illumination

Optical illumination of quantum-dot qubit devices at cryogenic temperatures, while not well studied, is often used to recover operating conditions after undesired shocking events or charge injection. Here, we demonstrate systematic threshold voltage shifts in a dopant-free, Si/SiGe field effect transistor using a near infrared (780 nm) laser diode. We find that illumination under an applied gate voltage can be used to set a specific, stable, and reproducible threshold voltage that, over a wide range in gate bias, is equal to that gate bias. Outside this range, the threshold voltage can still be tuned, although the resulting threshold voltage is no longer equal to the applied gate bias during illumination. We present a simple and intuitive model that provides a mechanism for the tunability in gate bias. The model presented also explains why cryogenic illumination is successful at resetting quantum dot qubit devices after undesired charging events.