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Investigating children's embodied learning in mixed-reality environments, where they collaboratively simulate scientific processes, requires analyzing complex multimodal data to interpret their learning and coordination behaviors. Learning scientists have developed Interaction Analysis (IA) methodologies for analyzing such data, but this requires researchers to watch hours of videos to extract and interpret students' learning patterns. Our study aims to simplify researchers' tasks, using Machine Learning and Multimodal Learning Analytics to support the IA processes. Our study combines machine learning algorithms and multimodal analyses to support and streamline researcher efforts in developing a comprehensive understanding of students' scientific engagement through their movements, gaze, and affective responses in a simulated scenario. To facilitate an effective researcher-AI partnership, we present an initial case study to determine the feasibility of visually representing students' states, actions, gaze, affect, and movement on a timeline. Our case study focuses on a specific science scenario where students learn about photosynthesis. The timeline allows us to investigate the alignment of critical learning moments identified by multimodal and interaction analysis, and uncover insights into students' temporal learning progressions.

This paper introduces a significantly better class of activation functions than the almost universally used ReLU like and Sigmoidal class of activation functions. Two new activation functions referred to as the Cone and Parabolic-Cone that differ drastically from popular activation functions and significantly outperform these on the CIFAR-10 and Imagenette benchmmarks are proposed. The cone activation functions are positive only on a finite interval and are strictly negative except at the end-points of the interval, where they become zero. Thus the set of inputs that produce a positive output for a neuron with cone activation functions is a hyperstrip and not a half-space as is the usual case. Since a hyper strip is the region between two parallel hyper-planes, it allows neurons to more finely divide the input feature space into positive and negative classes than with infinitely wide half-spaces. In particular the XOR function can be learn by a single neuron with cone-like activation functions. Both the cone and parabolic-cone activation functions are shown to achieve higher accuracies with significantly fewer neurons on benchmarks. The results presented in this paper indicate that many nonlinear real-world datasets may be separated with fewer hyperstrips than half-spaces. The Cone and Parabolic-Cone activation functions have larger derivatives than ReLU and are shown to significantly speedup training., Comment: 14 pages

High-level synthesis, source-to-source compilers, and various Design Space Exploration techniques for pragma insertion have significantly improved the Quality of Results of generated designs. These tools offer benefits such as reduced development time and enhanced performance. However, achieving high-quality results often requires additional manual code transformations and tiling selections, which are typically performed separately or as pre-processing steps. Although DSE techniques enable code transformation upfront, the vastness of the search space often limits the exploration of all possible code transformations, making it challenging to determine which transformations are necessary. Additionally, ensuring correctness remains challenging, especially for complex transformations and optimizations. To tackle this obstacle, we first propose a comprehensive framework leveraging HLS compilers. Our system streamlines code transformation, pragma insertion, and tiles size selection for on-chip data caching through a unified optimization problem, aiming to enhance parallelization, particularly beneficial for computation-bound kernels. Them employing a novel Non-Linear Programming (NLP) approach, we simultaneously ascertain transformations, pragmas, and tile sizes, focusing on regular loop-based kernels. Our evaluation demonstrates that our framework adeptly identifies the appropriate transformations, including scenarios where no transformation is necessary, and inserts pragmas to achieve a favorable Quality of Results.

Folklore in complexity theory suspects that circuit lower bounds against $\mathbf{NC}^1$ or $\mathbf{P}/\operatorname{poly}$, currently out of reach, are a necessary step towards proving strong proof complexity lower bounds for systems like Frege or Extended Frege. Establishing such a connection formally, however, is already daunting, as it would imply the breakthrough separation $\mathbf{NEXP} \not\subseteq \mathbf{P}/\operatorname{poly}$, as recently observed by Pich and Santhanam (2023). We show such a connection conditionally for the Implicit Extended Frege proof system ($\mathsf{iEF}$) introduced by Kraj\'i\v{c}ek (The Journal of Symbolic Logic, 2004), capable of formalizing most of contemporary complexity theory. In particular, we show that if $\mathsf{iEF}$ proves efficiently the standard derandomization assumption that a concrete Boolean function is hard on average for subexponential-size circuits, then any superpolynomial lower bound on the length of $\mathsf{iEF}$ proofs implies $\#\mathbf{P} \not\subseteq \mathbf{FP}/\operatorname{poly}$ (which would in turn imply, for example, $\mathbf{PSPACE} \not\subseteq \mathbf{P}/\operatorname{poly}$). Our proof exploits the formalization inside $\mathsf{iEF}$ of the soundness of the sum-check protocol of Lund, Fortnow, Karloff, and Nisan (Journal of the ACM, 1992). This has consequences for the self-provability of circuit upper bounds in $\mathsf{iEF}$. Interestingly, further improving our result seems to require progress in constructing interactive proof systems with more efficient provers., Comment: A conference version of this work is accepted to the 51st EATCS International Colloquium on Automata, Languages and Programming (ICALP 2024)

We investigate the X-ray properties of a large sample of 71 broad line and narrow line AGN at 2

In-context learning (ICL) approaches typically leverage prompting to condition decoder-only language model generation on reference information. Just-in-time processing of a context is inefficient due to the quadratic cost of self-attention operations, and caching is desirable. However, caching transformer states can easily require almost as much space as the model parameters. When the right context isn't known in advance, caching ICL can be challenging. This work addresses these limitations by introducing models that, inspired by the encoder-decoder architecture, use cross-attention to condition generation on reference text without the prompt. More precisely, we leverage pre-trained decoder-only models and only train a small number of added layers. We use Question-Answering (QA) as a testbed to evaluate the ability of our models to perform conditional generation and observe that they outperform ICL, are comparable to fine-tuned prompted LLMs, and drastically reduce the space footprint relative to standard KV caching by two orders of magnitude.

A quadruply lensed source, J125856.3-031944, has been discovered using the DELVE survey and WISE W1 - W2 colors. Followup direct imaging carried out with the MPIA 2.2 m and the Baade 6.5 m telescopes is analyzed, as is spectroscopy from the 2.5 m Nordic Optical Telescope. The lensed image configuration is kite-like, with the faintest image 2 magnitudes fainter than the other three. Redward of 6000 AA that image is badly blended with the lensing galaxy, which is elongated along the symmetry axis of the kite. Magellan direct imaging carried out in Sloan g permits better deblending. As the lensed image configuration is nearly circular, simple models give individual magnifications of at least 25 for the 3 brighter images. The source's narrow emission lines and low intrinsic luminosity qualify it as a type 2 AGN., Comment: 8 pages, 5 figures, submitted to AJ, comments welcome

In this work, the problem of communicating decisions of a classifier over a noisy channel is considered. With machine learning based models being used in variety of time-sensitive applications, transmission of these decisions in a reliable and timely manner is of significant importance. To this end, we study the scenario where a probability vector (representing the decisions of a classifier) at the transmitter, needs to be transmitted over a noisy channel. Assuming that the distortion between the original probability vector and the reconstructed one at the receiver is measured via f-divergence, we study the trade-off between transmission latency and the distortion. We completely analyze this trade-off using uniform, lattice, and sparse lattice-based quantization techniques to encode the probability vector by first characterizing bit budgets for each technique given a requirement on the allowed source distortion. These bounds are then combined with results from finite-blocklength literature to provide a framework for analyzing the effects of both quantization distortion and distortion due to decoding error probability (i.e., channel effects) on the incurred transmission latency. Our results show that there is an interesting interplay between source distortion (i.e., distortion for the probability vector measured via f-divergence) and the subsequent channel encoding/decoding parameters; and indicate that a joint design of these parameters is crucial to navigate the latency-distortion tradeoff. We study the impact of changing different parameters (e.g. number of classes, SNR, source distortion) on the latency-distortion tradeoff and perform experiments on AWGN and fading channels. Our results indicate that sparse lattice-based quantization is the most effective at minimizing latency across various regimes and for sparse, high-dimensional probability vectors (i.e., high number of classes)., Comment: Submitted to IEEE Transactions on Communications

The automatic detection of tree-ring boundaries and other anatomical features using image analysis has progressed substantially over the past decade with advances in machine learning and imagery technology, as well as increasing demands from the dendrochronology community. This paper presents a publicly available database of 64 scanned images of transverse sections of commercially grown Pinus taeda trees from northern Uruguay, ranging from 17 to 24 years old. The collection contains several challenging features for automatic ring detection, including illumination and surface preparation variation, fungal infection (blue stains), knot formation, missing cortex or interruptions in outer rings, and radial cracking. This dataset can be used to develop and test automatic tree ring detection algorithms. This paper presents to the dendrochronology community one such method, Cross-Section Tree-Ring Detection (CS-TRD), which identifies and marks complete annual rings in cross-sections for tree species presenting a clear definition between early and latewood. We compare the CS-TRD performance against the ground truth manual delineation of all rings over the UruDendro dataset. The CS-TRD software identified rings with an average F-score of 89% and RMSE error of 5.27px for the entire database in less than 20 seconds per image. Finally, we propose a robust measure of the ring growth using the \emph{equivalent radius} of a circle having the same area enclosed by the detected tree ring. Overall, this study contributes to the dendrochronologist's toolbox of fast and low-cost methods to automatically detect rings in conifer species, particularly for measuring diameter growth rates and stem transverse area using entire cross-sections., Comment: Submitted to Dendrochronologia. arXiv admin note: text overlap with arXiv:2305.10809

Supervised machine learning often requires large training sets to train accurate models, yet obtaining large amounts of labeled data is not always feasible. Hence, it becomes crucial to explore active learning methods for reducing the size of training sets while maintaining high accuracy. The aim is to select the optimal subset of data for labeling from an initial unlabeled set, ensuring precise prediction of outcomes. However, conventional active learning approaches are comparable to classical random sampling. This paper proposes a wrapper active learning method for classification, organizing the sampling process into a tree structure, that improves state-of-the-art algorithms. A classification tree constructed on an initial set of labeled samples is considered to decompose the space into low-entropy regions. Input-space based criteria are used thereafter to sub-sample from these regions, the total number of points to be labeled being decomposed into each region. This adaptation proves to be a significant enhancement over existing active learning methods. Through experiments conducted on various benchmark data sets, the paper demonstrates the efficacy of the proposed framework by being effective in constructing accurate classification models, even when provided with a severely restricted labeled data set.

This paper presents a novel approach for learned synergistic reconstruction of medical images using multi-branch generative models. Leveraging variational autoencoders (VAEs), our model learns from pairs of images simultaneously, enabling effective denoising and reconstruction. Synergistic image reconstruction is achieved by incorporating the trained models in a regularizer that evaluates the distance between the images and the model. We demonstrate the efficacy of our approach on both Modified National Institute of Standards and Technology (MNIST) and positron emission tomography (PET)/computed tomography (CT) datasets, showcasing improved image quality for low-dose imaging. Despite challenges such as patch decomposition and model limitations, our results underscore the potential of generative models for enhancing medical imaging reconstruction., Comment: 12 pages, 16 figures, 2 tables, submitted to IEEE TRPMS

Time-resolved electric field strength measurements have been performed, using an electric-field induced second harmonic (E-FISH) diagnostic, in a nanosecond glow discharge of an impulse dielectric barrier discharge (iDBD), in an ammonia gas environment. A temporal resolution of 2 ns and a spatial resolution estimated at 70 $\mu$m (given by laser waist) have been achieved. The comparative study of E-FISH measurements with and without a plasma discharge, operated at 4 kHz, reveal the presence of a persistent counter electric field, which is assumed to be caused by charge accumulation in between the AlN dielectrics used. Furthermore, by studying the influence of the applied voltage, the pressure, and the inter-dielectric distance, measurements seem to indicate the presence of charges remaining also in the post-discharge volume from the previous discharge to the next one.\&\#xD, Comment: Plasma Sources Science and Technology, In press

Cine cardiac magnetic resonance (CMR) imaging is recognised as the benchmark modality for the comprehensive assessment of cardiac function. Nevertheless, the acquisition process of cine CMR is considered as an impediment due to its prolonged scanning time. One commonly used strategy to expedite the acquisition process is through k-space undersampling, though it comes with a drawback of introducing aliasing effects in the reconstructed image. Lately, deep learning-based methods have shown remarkable results over traditional approaches in rapidly achieving precise CMR reconstructed images. This study aims to explore the untapped potential of attention mechanisms incorporated with a deep learning model within the context of the CMR reconstruction problem. We are motivated by the fact that attention has proven beneficial in downstream tasks such as image classification and segmentation, but has not been systematically analysed in the context of CMR reconstruction. Our primary goal is to identify the strengths and potential limitations of attention algorithms when integrated with a convolutional backbone model such as a U-Net. To achieve this, we benchmark different state-of-the-art spatial and channel attention mechanisms on the CMRxRecon dataset and quantitatively evaluate the quality of reconstruction using objective metrics. Furthermore, inspired by the best performing attention mechanism, we propose a new, simple yet effective, attention pipeline specifically optimised for the task of cardiac image reconstruction that outperforms other state-of-the-art attention methods. The layer and model code will be made publicly available., Comment: This paper has been submitted for the 32nd European Signal Processing Conference EUSIPCO 2024 in Lyon

The Atmospheric Dispersion Corrector (ADC) of the Gemini Planet Imager (GPI) corrects the chromatic dispersion caused by differential atmospheric refraction (DAR), making it an important optic for exoplanet observation. Despite requiring less than 5 mas of residual DAR to avoid potentially affecting the coronagraph, the GPI ADC averages $\sim7$ and $\sim11$ mas of residual DAR in $H$ and $J$ band respectively. We analyzed GPI data in those bands to find explanations for the underperformance. We found the model GPI uses to predict DAR underestimates humidity's impact on incident DAR, causing on average a 0.54 mas increase in $H$ band residual DAR. Additionally, the GPI ADC consistently undercorrects in $H$ band by about 7 mas, causing almost all the $H$ band residual DAR. $J$ band does not have such an offset. Perpendicular dispersion induced by the GPI ADC, potentially from a misalignment in the prisms' relative orientation, causes 86% of the residual DAR in $J$ band. Correcting these issues could reduce residual DAR, thereby improving exoplanet detection. We also made a new approximation for the index of refraction of air from 0.7 microns to 1.36 microns that more accurately accounts for the effects of humidity., Comment: 21 pages, 10 figures, published in JATIS

The Segment Anything Model (SAM) and CLIP are remarkable vision foundation models (VFMs). SAM, a prompt driven segmentation model, excels in segmentation tasks across diverse domains, while CLIP is renowned for its zero shot recognition capabilities. However, their unified potential has not yet been explored in medical image segmentation. To adapt SAM to medical imaging, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available. This work presents an in depth exploration of integrating SAM and CLIP into a unified framework for medical image segmentation. Specifically, we propose a simple unified framework, SaLIP, for organ segmentation. Initially, SAM is used for part based segmentation within the image, followed by CLIP to retrieve the mask corresponding to the region of interest (ROI) from the pool of SAM generated masks. Finally, SAM is prompted by the retrieved ROI to segment a specific organ. Thus, SaLIP is training and fine tuning free and does not rely on domain expertise or labeled data for prompt engineering. Our method shows substantial enhancements in zero shot segmentation, showcasing notable improvements in DICE scores across diverse segmentation tasks like brain (63.46%), lung (50.11%), and fetal head (30.82%), when compared to un prompted SAM. Code and text prompts are available at: https://github.com/aleemsidra/SaLIP.

Coral reefs are vital ecosystems that are under increasing threat due to local human impacts and climate change. Efficient and accurate monitoring of coral reefs is crucial for their conservation and management. In this paper, we present an automatic coral detection system utilizing the You Only Look Once (YOLO) deep learning model, which is specifically tailored for underwater imagery analysis. To train and evaluate our system, we employ a dataset consisting of 400 original underwater images. We increased the number of annotated images to 580 through image manipulation using data augmentation techniques, which can improve the model's performance by providing more diverse examples for training. The dataset is carefully collected from underwater videos that capture various coral reef environments, species, and lighting conditions. Our system leverages the YOLOv5 algorithm's real-time object detection capabilities, enabling efficient and accurate coral detection. We used YOLOv5 to extract discriminating features from the annotated dataset, enabling the system to generalize, including previously unseen underwater images. The successful implementation of the automatic coral detection system with YOLOv5 on our original image dataset highlights the potential of advanced computer vision techniques for coral reef research and conservation. Further research will focus on refining the algorithm to handle challenging underwater image conditions, and expanding the dataset to incorporate a wider range of coral species and spatio-temporal variations.

We introduce the Alternating Reading Task (ART) Corpus, a collection of dyadic sentence reading for studying the entrainment and imitation behaviour in speech communication. The ART corpus features three experimental conditions - solo reading, alternating reading, and deliberate imitation - as well as three sub-corpora encompassing French-, Italian-, and Slovak-accented English. This design allows systematic investigation of speech entrainment in a controlled and less-spontaneous setting. Alongside detailed transcriptions, it includes English proficiency scores, demographics, and in-experiment questionnaires for probing linguistic, personal and interpersonal influences on entrainment. Our presentation covers its design, collection, annotation processes, initial analysis, and future research prospects., Comment: 15 pages, 2 figures, 7 tables, accepted at LREC-COLING 2024 conference

In a world where Quantum Networks are rapidly becoming a reality, the development of the Quantum Internet is gaining increasing interest. Nevertheless, modern quantum networks are still in the early stages of development and have limited capacity to distribute resources among different users -- a constraint that needs to be taken into account. In this work we aim to investigate these constraints, using a novel setup for implementing Quantum Random Access Codes (QRACs), communication protocols known for their quantum advantage over their classical counterparts and semi-device-independent self-testing applications. Our QRAC states, made for the first time using weak coherent pulses instead of entangled single photons, allow us to experimentally test our encoding and decoding strategy from the resource allocation perspective. Moreover, by emulating a coexistent classical communication, we test the resilience of our implementation in presence of noise. The achieved results represent a significant milestone both for theoretical studies of quantum resource allocation and for the implementation of quantum infrastructures capable of coexisting with regular telecommunication networks.

This study assessed the safety, feasibility, and acceptability of a novel device to monitor ischaemic stroke patients. The device captured electroencephalography (EEG) and electrocardiography (ECG) data to compute an ECG-based metric termed the Electrocardiography Brain Perfusion index (EBPi), which may function as a proxy for cerebral blood flow (CBF). Seventeen ischaemic stroke patients wore the device for nine hours and reported feedback at 1, 3, 6 and 9 hours regarding user experience, comfort, and satisfaction (acceptability). Safety was assessed as the number of adverse events reported. Feasibility was assessed as the percentage of uninterrupted EEG/ECG data recorded (data capture efficiency). No adverse events were reported, only minor incidences of discomfort. Overall device comfort (92.5% +/- 10.3%) and data capture efficiency (95.8% +/- 6.8%) were very high with relatively low variance. The device did not restrict participants from receiving clinical care and rarely (n=6) restricted participants from undertaking routine tasks. This study provides a promising evidence base for the deployment of the device in a clinical setting. If clinically validated, EBPi may be able to detect CBF changes to monitor early neurological deterioration and treatment outcomes, thus filling an important gap in current monitoring options.

HESS J1813$-$178 is a very-high-energy $\gamma$-ray source spatially coincident with the young and energetic pulsar PSR J1813$-$1749 and thought to be associated with its pulsar wind nebula (PWN). Recently, evidence for extended high-energy emission in the vicinity of the pulsar has been revealed in the Fermi Large Area Telescope (LAT) data. This motivates revisiting the HESS J1813$-$178 region, taking advantage of improved analysis methods and an extended data set. Using data taken by the High Energy Stereoscopic System (H.E.S.S.) experiment and the Fermi-LAT, we aim to describe the $\gamma$-ray emission in the region with a consistent model, to provide insights into its origin. We performed a likelihood-based analysis on 32 hours of H.E.S.S. data and 12 years of Fermi-LAT data and fit a spectro-morphological model to the combined datasets. These results allowed us to develop a physical model for the origin of the observed $\gamma$-ray emission in the region. In addition to the compact very-high-energy $\gamma$-ray emission centered on the pulsar, we find a significant yet previously undetected component along the Galactic plane. With Fermi-LAT data, we confirm extended high-energy emission consistent with the position and elongation of the extended emission observed with H.E.S.S. These results establish a consistent description of the emission in the region from GeV energies to several tens of TeV. This study suggests that HESS J1813$-$178 is associated with a $\gamma$-ray PWN powered by PSR J1813$-$1749. A possible origin of the extended emission component is inverse Compton emission from electrons and positrons that have escaped the confines of the pulsar and form a halo around the PWN., Comment: 13+5 pages, 13+11 figures. Accepted for publication in A&A. Corresponding authors: T.Wach, A.Mitchell, V.Joshi, P.Chamb\'ery

Even for short protocols (<1 min), head movement can compromise accurate haemodynamic modelling of cerebral CT perfusion (CTP) imaging in acute stroke. Frame-to-frame registration is the most common form of retrospective correction but neglects the fact that motion is continuous, not discrete. By contrast, external tracking devices provide continuous motion monitoring and thereby the opportunity to fully correct the acquired data for motion. The aim of this study was to characterise the Intel D415 stereo depth camera, a compact low-cost markerless tracking device, in terms of its suitability for retrospective CTP motion correction. The results showed that jitter was stable, and thermally-induced pose drift was {\le} 1.5 mm and {\le} 0.5{\deg} during the first 10-20 min, after which it also became stable. For static poses, the mean difference between the Intel D415 motion estimates and ground-truth poses for a head phantom was {\le} 1.24 \pm 0.01 mm and {\le} 0.68 \pm 0.01{\deg} for position and orientation, respectively. For dynamic poses measured while a head phantom travelled smooth continuous trajectories with median speed 0.031 ms^(-1) (speed range 0-0.500 ms^(-1)), the root-mean-square-error (RMSE) was {\le} 1.40 \pm 0.12 mm and {\le} 0.24 \pm 0.02{\deg}. When tracking a simulated patient head trajectory derived from a clinical CTP scan, the average RMSE was {\le} 0.86 \pm 0.03 mm and {\le} 0.16 \pm 0.03{\deg}. Tracking the head motion of a human volunteer inside a clinical CT scanner, the average RMSE was {\le} 2.72 \pm 0.24 mm and {\le} 0.55 \pm 0.07{\deg}. Overall, our results suggest that a single D415 tracking system can achieve promising pose estimation accuracy, though still worse than typical brain CT resolution, including CTP. The error is likely to be reduced to a practical level by combining multiple devices and this should be investigated in future work.

Eta Carinae underwent the Great Eruption in the 1840s and a Lesser Eruption in the 1890s. Its apparent spectrum, modified by intervening ejecta, the Homunculus and Little Homunculus, continues to evolve but contains information pertaining to events in the 19th century. The LOS spectrum contains narrow absorption velocities, from -122 to -1665 km/s: rungs of a broken ladder caused by shells formed by the interacting winds. Estimated shell origin dates correlate with origin dates of expanding emission structures preceding the Great Eruption. The LOS absorption velocities extend the record post Great Eruption to the Lesser Eruption. We suggest that these shells originated from a binary merger within a triple system. Shells formed not only from periastron passages of the current secondary, but also from ear-like extensions preceding and following the periastron event. Additional models need to be considered., Comment: 18 pages, 5 figures, 1 table

Conformal field theory and its axiomatisation in terms of vertex operator algebras or chiral algebras are most commonly considered on the Riemann sphere. However, an important constraint in physics and an interesting source of mathematics is the fact that conformal field theories are expected to be well defined on any Riemann surface. To this end, a thorough understanding of chiral torus 1-point functions, ideally including explicit formulae, is a prerequisite for a detailed understanding of higher genera. These are distinguished from characters or vacuum torus 1-point functions because the insertion point is explicitly allowed to be labelled by any module over the vertex operator algebra rather than just the vertex operator algebra itself. Compellingly, chiral torus 1-point functions exhibit interesting modular properties, which we explore here in the example of the simple affine $\mathfrak{sl}(2)$ vertex operator algebras at non-negative integral levels. We determine the dimension of the space spanned by such functions, choose a natural basis to construct vector-valued modular forms and describe the congruence properties of these forms. In particular, we find explicit generators for the spaces of all vector-valued modular forms of dimension at most three, when the insertion comes from a simple module other than the vertex operator algebra. Finally, we use the fact that categories of modules over rational vertex operator algebras are modular tensor categories to give explicit formulae for the action of the modular group on chiral torus 1-point functions entirely in terms of categorical data. The usual modular $\mathsf{S}$ and $\mathsf{T}$ matrices of characters are known not to be complete invariants of modular tensor categories, so these generalised modular data are good candidates for more fine-grained invariants., Comment: 34 pages, 2 tables

The limited availability of 3D medical image datasets, due to privacy concerns and high collection or annotation costs, poses significant challenges in the field of medical imaging. While a promising alternative is the use of synthesized medical data, there are few solutions for realistic 3D medical image synthesis due to difficulties in backbone design and fewer 3D training samples compared to 2D counterparts. In this paper, we propose GEM-3D, a novel generative approach to the synthesis of 3D medical images and the enhancement of existing datasets using conditional diffusion models. Our method begins with a 2D slice, noted as the informed slice to serve the patient prior, and propagates the generation process using a 3D segmentation mask. By decomposing the 3D medical images into masks and patient prior information, GEM-3D offers a flexible yet effective solution for generating versatile 3D images from existing datasets. GEM-3D can enable dataset enhancement by combining informed slice selection and generation at random positions, along with editable mask volumes to introduce large variations in diffusion sampling. Moreover, as the informed slice contains patient-wise information, GEM-3D can also facilitate counterfactual image synthesis and dataset-level de-enhancement with desired control. Experiments on brain MRI and abdomen CT images demonstrate that GEM-3D is capable of synthesizing high-quality 3D medical images with volumetric consistency, offering a straightforward solution for dataset enhancement during inference. The code is available at https://github.com/HKU-MedAI/GEM-3D., Comment: 20 pages, 8 figures

Perception plays a crucial role in various robot applications. However, existing well-annotated datasets are biased towards autonomous driving scenarios, while unlabelled SLAM datasets are quickly over-fitted, and often lack environment and domain variations. To expand the frontier of these fields, we introduce a comprehensive dataset named MCD (Multi-Campus Dataset), featuring a wide range of sensing modalities, high-accuracy ground truth, and diverse challenging environments across three Eurasian university campuses. MCD comprises both CCS (Classical Cylindrical Spinning) and NRE (Non-Repetitive Epicyclic) lidars, high-quality IMUs (Inertial Measurement Units), cameras, and UWB (Ultra-WideBand) sensors. Furthermore, in a pioneering effort, we introduce semantic annotations of 29 classes over 59k sparse NRE lidar scans across three domains, thus providing a novel challenge to existing semantic segmentation research upon this largely unexplored lidar modality. Finally, we propose, for the first time to the best of our knowledge, continuous-time ground truth based on optimization-based registration of lidar-inertial data on large survey-grade prior maps, which are also publicly released, each several times the size of existing ones. We conduct a rigorous evaluation of numerous state-of-the-art algorithms on MCD, report their performance, and highlight the challenges awaiting solutions from the research community., Comment: Accepted by The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024

We report the observation of a Twist Grain Boundary (TGB) phase of DNA, a striking motif of three dimensional (3D) equilibrium self-assembly of the DNA tetramer 5'-GTAC-3', a base sequence that is self-complimentary, pairing to form 4-base long, blunt-end Watson/Crick (WC) duplexes. Hydrophobic blunt ends and liquid crystal ordering enable these short duplexes to aggregate into long-DNA-like columns, even though the double helix has a break every 4 bases. A further step assembles these columns into monolayer sheets in which the columns are mutually parallel, and, finally, these sheets stack into lamellar arrays in which the column axis of each layer rotates in helical fashion, through a 60 degree angle with each passage to the next layer. This reorientation in a left-handed TGB helix enables each 2nm diameter WC column to be parallel to, and to partially enter, the major grooves of its neighbors., Comment: 21 pages, 3 figures

Black-hole X-ray binaries exhibit different spectral and timing properties in different accretion states. The X-ray outburst of a recently discovered and extraordinarily bright source, Swift$~$J1727.8$-$1613, has enabled the first investigation of how the X-ray polarization properties of a source evolve with spectral state. The 2$-$8 keV polarization degree was previously measured by the Imaging X-ray Polarimetry Explorer (IXPE) to be $\approx$ 4% in the hard and hard intermediate states. Here we present new IXPE results taken in the soft state, with the X-ray flux dominated by the thermal accretion-disk emission. We find that the polarization degree has dropped dramatically to $\lesssim$ 1%. This result indicates that the measured X-ray polarization is largely sensitive to the accretion state and the polarization fraction is significantly higher in the hard state when the X-ray emission is dominated by up-scattered radiation in the X-ray corona. The combined polarization measurements in the soft and hard states disfavor a very high or low inclination of the system., Comment: 9 pages, 7 figures

The idea that rod-like molecules possessing an electric dipole moment could exhibit a ferroelectric nematic phase was suggested more than a century ago. However, only recently such a phase has been reported for two quite different liquid crystals: RM734 (4-[(4-nitrophenoxy)carbonyl)]phenyl 2,4-dimethoxybenzoate) and DIO (2.3',4',5'-tetrafluoro[1,1'-biphenyl]-4-yl 2.6-difluoro-4-(5-propyl-1,3-dioxan-2-yl) benzoate). For RM734 a direct ferroelectric nematic (NF) to classical nematic N transition was reported, whereas for DIO an intermediate phase Nx was discovered between the NF and the N phases. Here we present high-resolution calorimetric evidence that an intermediate Nx phase also exists in RM734 along a narrow temperature range between the NF and the N phases., Comment: 18 pages, 7 Figures