Search

Inertial microfluidics has been limited to dilute particle concentrations due to defocusing (spreading out) at high particle concentrations. We observe a counterintuitive shift of focusing to the outer curved wall under high concentration flow, which contradicts the existing particle focusing theory. We developed a multiphase model incorporating lift forces and particle-particle interactions to explain this behaviour. Numerical simulations validated by experimental data reveal the shift is governed by the ratio of the lift force strength to that of particle interaction frequencies.

The purpose of this research was to investigate the progression outcomes of the June 2020 Key Stage 5 cohort following the completion of their post-16 studies amid the COVID-19 pandemic. The primary interest was to understand whether students from this cohort had progressed differently compared to the previous pre-pandemic cohort of students and, if they had, how they might have been impacted. The progression outcomes analysed were: destinations (e.g., higher education, further education), types of higher education institutions (e.g., Russell Group) and subject areas studied in higher education (e.g., Computing). This research focused on the immediate progression to higher or further education of students who achieved at least an A Level, AS Level, Extended Project Qualification, Applied General, or Tech Level in June 2020. A comparison was made with the June 2019 Key Stage 5 cohort to understand if the outcomes of the June 2020 cohort changed noticeably compared to the pre-pandemic levels. The research also investigated the progression outcomes of specific groups of students to understand whether students from different demographic and socio-economic backgrounds (e.g., disadvantaged students) had been impacted differently.

In language processing, transformers benefit greatly from text being condensed. This is achieved through a larger vocabulary that captures word fragments instead of plain characters. This is often done with Byte Pair Encoding. In the context of images, tokenisation of visual data is usually limited to regular grids obtained from quantisation methods, without global content awareness. Our work improves tokenisation of visual data by bringing Byte Pair Encoding from 1D to multiple dimensions, as a complementary add-on to existing compression. We achieve this through counting constellations of token pairs and replacing the most frequent token pair with a newly introduced token. The multidimensionality only increases the computation time by a factor of 2 for images, making it applicable even to large datasets like ImageNet within minutes on consumer hardware. This is a lossless preprocessing step. Our evaluation shows improved training and inference performance of transformers on visual data achieved by compressing frequent constellations of tokens: The resulting sequences are shorter, with more uniformly distributed information content, e.g. condensing empty regions in an image into single tokens. As our experiments show, these condensed sequences are easier to process. We additionally introduce a strategy to amplify this compression further by clustering the vocabulary.

Future quantum networks will be composed of both terrestrial links for metropolitan and continent-scale connections and space-based links for global coverage and infrastructure resilience. However, the propagation of quantum signals through the atmosphere is severely impacted by the effects of turbulence. This is even more the case for entanglement-based quantum communication protocols requiring two free-space channels to be considered simultaneously. In this work, we assess the advantage of turbulence mitigation by adaptive optics, in particular during daytime link operation, so as to increase the coupling of the received signal into an optical fiber. We show in particular that this improves the performance of entanglement-based quantum key distribution by up to a few hundred bits per second when compared with the uncorrected scenario

We introduce DAHL, a benchmark dataset and automated evaluation system designed to assess hallucination in long-form text generation, specifically within the biomedical domain. Our benchmark dataset, meticulously curated from biomedical research papers, consists of 8,573 questions across 29 categories. DAHL evaluates fact-conflicting hallucinations in Large Language Models (LLMs) by deconstructing responses into atomic units, each representing a single piece of information. The accuracy of these responses is averaged to produce the DAHL Score, offering a more in-depth evaluation of hallucinations compared to previous methods that rely on multiple-choice tasks. We conduct experiments with 8 different models, finding that larger models tend to hallucinate less; however, beyond a model size of 7 to 8 billion parameters, further scaling does not significantly improve factual accuracy. The DAHL Score holds potential as an efficient alternative to human-annotated preference labels, being able to be expanded to other specialized domains. We release the dataset and code in public., Comment: EMNLP2024/FEVER

How objective and unbiased are we while making decisions? This work investigates cognitive bias identification in high-stake decision making process by human experts, questioning its effectiveness in real-world settings, such as candidates assessments for university admission. We begin with a statistical analysis assessing correlations among different decision points among in the current process, which discovers discrepancies that imply cognitive bias and inconsistency in decisions. This motivates our exploration of bias-aware AI-augmented workflow that surpass human judgment. We propose BGM-HAN, an enhanced Hierarchical Attention Network with Byte-Pair Encoding, Gated Residual Connections and Multi-Head Attention. Using it as a backbone model, we further propose a Shortlist-Analyse-Recommend (SAR) agentic workflow, which simulate real-world decision-making. In our experiments, both the proposed model and the agentic workflow significantly improves on both human judgment and alternative models, validated with real-world data., Comment: Source code is available at: https://github.com/junhua/bgm-han

Novel view synthesis of dynamic scenes is becoming important in various applications, including augmented and virtual reality. We propose a novel 4D Gaussian Splatting (4DGS) algorithm for dynamic scenes from casually recorded monocular videos. To overcome the overfitting problem of existing work for these real-world videos, we introduce an uncertainty-aware regularization that identifies uncertain regions with few observations and selectively imposes additional priors based on diffusion models and depth smoothness on such regions. This approach improves both the performance of novel view synthesis and the quality of training image reconstruction. We also identify the initialization problem of 4DGS in fast-moving dynamic regions, where the Structure from Motion (SfM) algorithm fails to provide reliable 3D landmarks. To initialize Gaussian primitives in such regions, we present a dynamic region densification method using the estimated depth maps and scene flow. Our experiments show that the proposed method improves the performance of 4DGS reconstruction from a video captured by a handheld monocular camera and also exhibits promising results in few-shot static scene reconstruction., Comment: NeurIPS 2024

In this study, we explore the use of Co-55 as a radioisotope for multiplexed PET (mPET) by utilizing its emission of a prompt gamma-ray in cascade with a positron during decay. We leverage the prompt-gamma signal to generate triple coincidences for a Co-55-labeled tracer, allowing us to distinguish it from a tracer labeled with a pure positron emitter, such as F-18. By employing triple versus double coincidence detection and signal processing methodology, we successfully separate the Co-55 signal from that of F-18. Phantom studies were conducted to establish the correlation between Co-55 double and triple coincidence counts and Co-55 activity. Additionally, we demonstrate the potential for quantifying hot spots within a warm background produced by both Co-55 and F-18 signals in a single PET scan. Finally, we showcase the ability to simultaneously image two tracers in vivo in a single PET session with mouse models of cancer., Comment: 5 pages, 4 figures, submitted IEEE ISBI 2025; v2: clarification on unstable Co-55-labeled antibody

Persistent homology (PH) studies the topology of data across multiple scales by building nested collections of topological spaces called filtrations, computing homology and returning an algebraic object that can be vizualised as a barcode--a multiset of intervals. The barcode is stable and interpretable, leading to applications within mathematics and data science. We study the spaces of point clouds with the same barcode by connecting persistence with real algebraic geometry and rigidity theory. Utilizing a semi-algebraic setup of point cloud persistence, we give lower and upper bounds on its dimension and provide combinatorial conditions in terms of the local and global rigidity properties of graphs associated with point clouds and filtrations. We prove that for generic point clouds in $\mathbb{R}^d$ ($d \geq 2$), a point cloud is identifiable up to isometry from its VR persistence if the associated graph is globally rigid, and locally identifiable up to isometry from its \v{C}ech persistence if the associated hypergraph is rigid., Comment: 33 pages, 8 figures

Classical simulations of quantum circuits are essential for verifying and benchmarking quantum algorithms, particularly for large circuits, where computational demands increase exponentially with the number of qubits. Among available methods, the classical simulation of quantum circuits inspired by density functional theory -- the so-called QC-DFT method, shows promise for large circuit simulations as it approximates the quantum circuits using single-qubit reduced density matrices to model multi-qubit systems. However, the QC-DFT method performs very poorly when dealing with multi-qubit gates. In this work, we introduce a novel CNOT "functional" that leverages neural networks to generate unitary transformations, effectively mitigating the simulation errors observed in the original QC-DFT method. For random circuit simulations, our modified QC-DFT enables efficient computation of single-qubit marginal measurement probabilities, or single-qubit probability (SQPs), and achieves lower SQP errors and higher fidelities than the original QC-DFT method. Despite limitations in capturing full entanglement and joint probability distributions, we find potential applications of SQPs in simulating Shor's and Grover's algorithms for specific solution classes. These findings advance the capabilities of classical simulations for some quantum problems and provide insights into managing entanglement and gate errors in practical quantum computing., Comment: 6 pages, 2 figures, and supplemental materials

A recent study of the distribution of dwarf galaxies in the MATLAS sample in galaxy groups revealed an excess of flattened satellite structures, reminiscent of the co-rotating planes of dwarf galaxies discovered in the local Universe. If confirmed, this lends credence to the plane-of-satellite problem and further challenges the standard model of hierarchical structure formation. However, with only photometric data and no confirmation of the satellite membership, the study could not address the plane-of-satellite problem in full detail. Here we present spectroscopic follow-up observations of one of the most promising planes-of-satellites candidates in the MATLAS survey, the satellite system of NGC 474. Employing MUSE at the VLT and full spectrum fitting, we studied 13 dwarf galaxy candidates and confirmed nine to be members of the field around NGC 474. Measuring the stellar populations of all observed galaxies, we find that the MATLAS dwarfs have lower metallicities than the Local Group dwarfs at given luminosity. Two dwarf galaxies may form a pair of satellites based on their close projection and common velocity. Within the virial radius, we do not find a significant plane-of-satellites, however, there is a sub-population of six dwarf galaxies which seem to be anti-correlated in phase-space. Due to the low number of dwarf galaxies, this signal may arise by chance. With over 2000 dwarf galaxy candidates found in the MATLAS survey, this remains an intriguing data set to study the plane-of-satellites problem in a statistical fashion once more follow-up observations have been conducted., Comment: 9 pages, 8 figures, 2 tables. Accepted for publication in Astronomy & Astrophysics (A&A)

Light detection and ranging (LiDAR)-based odometry has been widely utilized for pose estimation due to its use of high-accuracy range measurements and immunity to ambient light conditions. However, the performance of LiDAR odometry varies depending on the environment and deteriorates in degenerative environments such as long corridors. This issue stems from the dependence on a single error metric, which has different strengths and weaknesses depending on the geometrical characteristics of the surroundings. To address these problems, this study proposes a novel iterative closest point (ICP) method called GenZ-ICP. We revisited both point-to-plane and point-to-point error metrics and propose a method that leverages their strengths in a complementary manner. Moreover, adaptability to diverse environments was enhanced by utilizing an adaptive weight that is adjusted based on the geometrical characteristics of the surroundings. As demonstrated in our experimental evaluation, the proposed GenZ-ICP exhibits high adaptability to various environments and resilience to optimization degradation in corridor-like degenerative scenarios by preventing ill-posed problems during the optimization process., Comment: 8 pages, 5 figures, Accepted to IEEE Robotics and Automation Letters (RA-L)

Spiking neural networks (SNNs) are powerful models of spatiotemporal computation and are well suited for deployment on resource-constrained edge devices and neuromorphic hardware due to their low power consumption. Leveraging attention mechanisms similar to those found in their artificial neural network counterparts, recently emerged spiking transformers have showcased promising performance and efficiency by capitalizing on the binary nature of spiking operations. Recognizing the current lack of dedicated hardware support for spiking transformers, this paper presents the first work on 3D spiking transformer hardware architecture and design methodology. We present an architecture and physical design co-optimization approach tailored specifically for spiking transformers. Through memory-on-logic and logic-on-logic stacking enabled by 3D integration, we demonstrate significant energy and delay improvements compared to conventional 2D CMOS integration.

In the Detection and Multi-Object Tracking of Sweet Peppers Challenge, we present Track Any Peppers (TAP) - a weakly supervised ensemble technique for sweet peppers tracking. TAP leverages the zero-shot detection capabilities of vision-language foundation models like Grounding DINO to automatically generate pseudo-labels for sweet peppers in video sequences with minimal human intervention. These pseudo-labels, refined when necessary, are used to train a YOLOv8 segmentation network. To enhance detection accuracy under challenging conditions, we incorporate pre-processing techniques such as relighting adjustments and apply depth-based filtering during post-inference. For object tracking, we integrate the Matching by Segment Anything (MASA) adapter with the BoT-SORT algorithm. Our approach achieves a HOTA score of 80.4%, MOTA of 66.1%, Recall of 74.0%, and Precision of 90.7%, demonstrating effective tracking of sweet peppers without extensive manual effort. This work highlights the potential of foundation models for efficient and accurate object detection and tracking in agricultural settings.

This paper details a significant milestone towards maturing a buoyant aerial robotic platform, or aerobot, for flight in the Venus clouds. We describe two flights of our subscale altitude-controlled aerobot, fabricated from the materials necessary to survive Venus conditions. During these flights over the Nevada Black Rock desert, the prototype flew at the identical atmospheric densities as 54 to 55 km cloud layer altitudes on Venus. We further describe a first-principle aerobot dynamics model which we validate against the Nevada flight data and subsequently employ to predict the performance of future aerobots on Venus. The aerobot discussed in this paper is under JPL development for an in-situ mission flying multiple circumnavigations of Venus, sampling the chemical and physical properties of the planet's atmosphere and also remotely sensing surface properties., Comment: Preprint submitted to AIAA Journal of Aircraft

Off-road environments pose significant perception challenges for high-speed autonomous navigation due to unstructured terrain, degraded sensing conditions, and domain-shifts among biomes. Learning semantic information across these conditions and biomes can be challenging when a large amount of ground truth data is required. In this work, we propose an approach that leverages a pre-trained Vision Transformer (ViT) with fine-tuning on a small (<500 images), sparse and coarsely labeled (<30% pixels) multi-biome dataset to predict 2D semantic segmentation classes. These classes are fused over time via a novel range-based metric and aggregated into a 3D semantic voxel map. We demonstrate zero-shot out-of-biome 2D semantic segmentation on the Yamaha (52.9 mIoU) and Rellis (55.5 mIoU) datasets along with few-shot coarse sparse labeling with existing data for improved segmentation performance on Yamaha (66.6 mIoU) and Rellis (67.2 mIoU). We further illustrate the feasibility of using a voxel map with a range-based semantic fusion approach to handle common off-road hazards like pop-up hazards, overhangs, and water features., Comment: Accepted to Australasian Conference on Robotics and Automation (ACRA 2024)

With safety measures towards the current Covid-19 pandemic, many retails clothing stores have restricted on-site fittings and shifted their business online. Inability to touch on product evaluations shows an apparent limitation as compared to retail shopping especially when the object's material information is crucial like clothing. Haptic technologies show potential of bridging the gap between online shops and the shoppers by providing a sense of touch, yet little research has been done especially on the effect of the simulation of tactile texture on the shopping experience. In this study, we modified a mock-up e-commerce website by adding clothing products and enabling a mid-air haptic interface with Ultrahaptics Evaluation Kit (UHEV1). We developed texture sensations using Time Point Streaming (TSP) modulation for clothing products with different texture materials and a user study was carried out to investigate the tactile texture sensation on shoppers' experience in evaluating online products. Our results show that tactile texture sensation using multipoint mid-air haptic feedback improves online shopper's satisfaction on the product browsing experience. This study contributes to the improvement of general lifestyle of the society in terms of e-commerce experience and could expand its application to impact different sectors like education and different communities including the visually impaired., Comment: Part of proceedings of 6th International Conference AsiaHaptics 2024

Traditional medical training faces challenges like ethical concerns, safety risks, and high costs. VR technology offers a promising solution but is limited by low complexity and lack of tactile feedback. This paper presents a cost-effective haptic VR surgery simulation which simulates realistic Kidney Transplant using commercial devices to enhance training authenticity and immersion. Trainees can conduct incision and anastomosis procedures using a haptic stylus device that provides tactile sensations. Results from the test with medical participants showed that haptic feedback positively enhances the VR medical training experience., Comment: Part of proceedings of 6th International Conference AsiaHaptics 2024

This paper presents an innovative pseudo-haptic model for weight simulation in virtual reality (VR) environments. By integrating visual feedback with voluntary exerted force through a passive haptic glove, the model creates haptic illusions of weight perception. Two VR cube games were developed to evaluate the model's effectiveness. The first game assesses participants' ability to discriminate relative weights, while the second evaluates their capability to estimate absolute weights. Twelve participants, aged 18 to 59, tested the games. Results suggest that the pseudo-haptic model is effective for relative weight discrimination tasks and holds potential for various VR applications. Further research with a larger participant group and more complex scenarios is recommended to refine and validate the model., Comment: Part of proceedings of 6th International Conference AsiaHaptics 2024

Decoherence is a major challenge for quantum technologies. A way to mitigate its negative impact is by employing quantum optimal control. The decoherence dynamics varies significantly based on the characteristics of the surrounding environment of qubits, consequently affecting the outcome of the control optimization. In this work, we investigate the dependence of the shape of a spin inversion control pulse on the correlation time of the environment noise. Furthermore, we analyze the effects of constraints and optimization options on the optimization outcome and identify a set of strategies that improve the optimization performance. Finally, we present an experimental realization of the numerically-optimized pulses validating the optimization feasibility. Our work serves as a generic yet essential guide to implementing optimal control in the presence of realistic noise, e.g., in nitrogen-vacancy centers in diamond., Comment: 20 pages, 9 figures

Logistic regression, the Support Vector Machine (SVM), and least squares are well-studied methods in the statistical and computer science community, with various practical applications. High-dimensional data arriving on a real-time basis makes the design of online learning algorithms that produce sparse solutions essential. The seminal work of \hyperlink{cite.langford2009sparse}{Langford, Li, and Zhang (2009)} developed a method to obtain sparsity via truncated gradient descent, showing a near-optimal online regret bound. Based on this method, we develop a quantum sparse online learning algorithm for logistic regression, the SVM, and least squares. Given efficient quantum access to the inputs, we show that a quadratic speedup in the time complexity with respect to the dimension of the problem is achievable, while maintaining a regret of $O(1/\sqrt{T})$, where $T$ is the number of iterations., Comment: 31 pages, 1 table, 4 algorithms

The realization of a quantum computer represents a tremendous scientific and technological challenge due to the extreme fragility of quantum information. The physical support of information, namely the quantum bit or qubit, must at the same time be strongly coupled to other qubits by gates to compute information, and well decoupled from its environment to keep its quantum behavior. An interesting physical system for realizing such qubits are magnetic impurities in semiconductors, such as bismuth donors in silicon. Indeed, spins associated to bismuth donors can reach an extremely long coherence time -- of the order of seconds. Yet it is extremely difficult to establish and control efficient gates between these spins. Here we demonstrate a protocol where single bismuth donors can coherently transfer their quantum information to a superconducting flux qubit, which acts as a mediator or quantum bus. This superconducting device allows to connect distant spins on-demand with little impact on their coherent behavior., Comment: 22 pages, 4 figures and 5 extended data figures

The stellar initial mass function (IMF) is a key parameter to understand the star formation process and the integrated properties of stellar populations in remote galaxies. We present a spectroscopic study of young massive clusters (YMCs) in the starburst galaxies NGC 4038/39. The integrated spectra of seven YMCs obtained with GMOS-S attached to the 8.2-m Gemini South telescope reveal the spectral features associated with stellar ages and the underlying IMFs. We constrain the ages of the YMCs using the absorption lines and strong emission bands from Wolf-Rayet stars. The internal reddening is also estimated from the strength of the Na I D absorption lines. Based on these constraints, the observed spectra are matched with the synthetic spectra generated from a simple stellar population model. Several parameters of the clusters including age, reddening, cluster mass, and the underlying IMF are derived from the spectral matching. The ages of the YMCs range from 2.5 to 6.5 Myr, and these clusters contain stellar masses ranging from 1.6 X 10^5 M_sun to 7.9 X 10^7 M_sun. The underlying IMFs appear to differ from the universal form of the Salpeter/Kroupa IMF. Interestingly, massive clusters tend to have the bottom-heavy IMFs, although the masses of some clusters are overestimated due to the crowding effect. Based on this, our results suggest that the universal form of the IMF is not always valid when analyzing integrated light from unresolved stellar systems. However, further study with a larger sample size is required to reach a definite conclusion., Comment: 18 pages, 9 figures, accepted for publication in AJ

We introduce and study the Chern filtration on the cohomology of the moduli of bundles on curves. This can be viewed as a natural cohomological invariant defined via tautological classes that interpolates between additive Betti numbers and the multiplicative ring structure. In the rank two case, we fully compute the Chern filtration for moduli of stable bundles and all intermediate stacks in the Harder--Narasimhan stratification. We observe a curious symmetry of the Chern filtration on the moduli of rank two stable bundles, and construct $\mathfrak{sl}_2$-actions that categorify this symmetry. Our study of the Chern filtration is motivated by the $P=C$ phenomena in several related geometries., Comment: 50 pages, comments are welcome!

We explore the gravitational-wave emission from head-on collisions of equal-mass solitonic boson-star binaries from simulations spanning a two-dimensional parameter space, consisting of the central scalar-field amplitude of the stars and the solitonic potential parameter. We report the gravitational-wave energies emitted by boson-star binaries which, due to their combination of moderately high compactness with significant deformability, we often find to be louder by up to an order of magnitude than analogous black-hole collisions. The dependence of the radiated energy on the boson-star parameters exhibits striking needle-sharp features and discontinuous jumps to the value emitted by black-hole binaries. We explain these features in terms of the solitonic potential and the stability properties of the respective individual stars., Comment: 17 pages, 10 figures

Verifying the violation of Bell's inequality is one of the most representative methods to demonstrate that entangled photon pairs prepared in a quantum optics-based system exhibit quantum properties. While experiments on Bell inequality violations have been theoretically well-established and extensively conducted to implement various quantum information technologies in laboratory settings, mathematical modeling for accurately predicting the distribution of high-intensity entangled photon pairs in high-loss environments remains an issue that requires further research. As the brightness of the entangled photon pairs increases, the influence of multi-photon effects becomes more significant, leading to a decrease in the CHSH value $S$ and also a reduction in the standard deviation of the CHSH value $\Delta S$. Therefore, a new analysis of the $(S-2)/\Delta S$ value is required to more precisely confirm the degree of CHSH inequality violation including the reliability of $S$. In this paper, we propose a mathematical model to predict the $(S-2)/\Delta S$ value as a function of the brightness of the entangled photon pair source, and we also suggest the need to optimize the brightness of this source. Additionally, we provide experimental evidence supporting this model. The experiment confirms that when the mean photon number is $\mu=0.026$ in an entanglement distribution setup with a total loss of $-19.03$ dB, the CHSH value drops to 2.69, while the $(S-2)/\Delta S$ value increases to 60.95., Comment: 11 pages, 1 table, and 4 figures

The effectiveness of feedback in enhancing learning outcomes is well documented within Educational Data Mining (EDM). Various prior research has explored methodologies to enhance the effectiveness of feedback. Recent developments in Large Language Models (LLMs) have extended their utility in enhancing automated feedback systems. This study aims to explore the potential of LLMs in facilitating automated feedback in math education. We examine the effectiveness of LLMs in evaluating student responses by comparing 3 different models: Llama, SBERT-Canberra, and GPT4 model. The evaluation requires the model to provide both a quantitative score and qualitative feedback on the student's responses to open-ended math problems. We employ Mistral, a version of Llama catered to math, and fine-tune this model for evaluating student responses by leveraging a dataset of student responses and teacher-written feedback for middle-school math problems. A similar approach was taken for training the SBERT model as well, while the GPT4 model used a zero-shot learning approach. We evaluate the model's performance in scoring accuracy and the quality of feedback by utilizing judgments from 2 teachers. The teachers utilized a shared rubric in assessing the accuracy and relevance of the generated feedback. We conduct both quantitative and qualitative analyses of the model performance. By offering a detailed comparison of these methods, this study aims to further the ongoing development of automated feedback systems and outlines potential future directions for leveraging generative LLMs to create more personalized learning experiences., Comment: 12 pages including references, 4 figures, 9 tables

Blind spectroscopy of massive lensing galaxy clusters with MUSE has revealed large numbers of gravitationally-lensed Lyman-$ \alpha $ emitters exhibiting asymmetric profiles at $ 2.9 \leq z \leq 6.7 $, suggesting abundant outflows from low-mass star-forming galaxies in the early universe. Are these primaeval galaxies experiencing their first bursts of star formation, or established galaxies experiencing rejuvenation? With JWST rest-frame optical/NIR continuum imaging now available for many of these objects, we can search for older stellar populations. Here, we search for spectroscopic confirmation of outflows from these galaxies, finding a few high-signal-to-noise cases in which blueshifted interstellar absorption lines are detected. Next, we analyse the star formation histories with combined HST + JWST photometry. We find most them to be well characterised by very young, low metallicity stellar populations. However, despite the rest-frame optical/NIR coverage of JWST, we cannot place strict upper bounds on the mass in old stars (age $ > 100\,\text{Myr} $)., Comment: 10 pages, 3 figures. Submitted to Proceedings of the International Astronomical Union 2024 (Symposium 391 - The first chapters of our cosmic history with JWST)

We compute analytically the three-loop correlation function of the local operator $\text{tr} \, \phi^3$ inserted into three on-shell states, in maximally supersymmetric Yang-Mills theory. The result is expressed in terms of Chen iterated integrals. We also present our result using generalised polylogarithms, and evaluate them numerically, finding agreement with a previous numerical result in the literature. We observe that the result depends on fewer kinematic singularities compared to individual Feynman integrals. Furthermore, upon choosing a suitable definition of the finite part, we find that the latter satisfies powerful symbol adjacency relations similar to those previously observed for the $\text{tr} \, \phi^2$ case., Comment: 17 pages, 4 figures, 4 tables, 680 integrals

We examined the double red clump (RC) observed in the Galactic bulge, interpreted as a difference in distance ("X-shaped bulge scenario") or in chemical composition ("multiple population scenario"). To verify chemical differences between the RC groups, we performed low-resolution spectroscopy for RC and red giant branch (RGB) stars using Gemini-South/GMOS in three fields of the bulge, and collected diverse data from literature. We divided our sample stars not only into bright and faint RC groups, but also into bluer ([Fe/H] < -0.1) and redder ([Fe/H] > -0.1) groups following the recent u-band photometric studies. For the metal-poor stars, no statistically significant difference in CN index was detected between the bright and faint RC groups for all observed fields. However, we found, from cross-matching with high-resolution spectroscopic data, a sign of Na enhancement in the "metal-poor and bright" RC group compared to the "metal-poor and faint" group at (l,b)=(-1 deg,-8.5 deg). When the contributions of the RGB stars on the RC regimes are taken into account, the Na abundance difference between genuine RCs would correspond to approximately 0.23 dex, similar to globular cluster (GCs) with multiple populations. In contrast, the metal-rich stars do not show chemical differences between the bright and faint RCs. It implies that the double RC observed in the metal-poor component of the bulge might be linked to the multiple populations originated from GC-like subsystem, whereas that of the metal-rich component would have produced by the X-shaped structure. Our results support the previous studies suggesting composite nature of the Milky Way bulge., Comment: Accepted for publication in ApJ

Context. With the increasing number of discoveries of globular clusters in the inner Milky Way, the need for spectroscopic confirmation and further investigation of their stellar populations and chemodynamical properties has become crucial. Aims. Gran 5 is a newly reported low-mass globular cluster located close to the Galactic center, and it is thought to be an accreted object associated with the Gaia-Enceladus structure. This study aims to investigate the stellar populations of Gran 5 and their detailed chemical properties. Methods. We performed high-resolution near-infrared spectroscopy on seven stars in the field of Gran 5 using IGRINS on the Gemini-South telescope. Results. We identified six stars as cluster members and reveal that they are divided into two stellar populations with different metallicities, with mean [Fe/H] values of -0.76 dex and -0.55 dex, respectively. In addition, the chemodynamical properties of Gran 5 agree with those of in situ globular clusters. Conclusions. Our findings represent the first detection of two stellar populations with different metallicities in a low-mass globular cluster. This suggests that the metallicity variation in Gran 5 may have arisen from processes different from those in other globular clusters with metallicity variation, or that it may have lost a substantial amount of its initial mass during its evolution., Comment: 12 pages, 9 figures, accepted for publication in A&A

Tails serve various functions in both robotics and biology, including expression, grasping, and defense. The vertebrate tails associated with these functions exhibit diverse patterns of vertebral lengths, but the precise mechanisms linking form to function have not yet been established. Vertebrate tails are complex musculoskeletal structures, making both direct experimentation and computational modeling challenging. This paper presents Tendon-Actuated Linked-Element (TALE), a modular robotic test bed to explore how tail morphology influences function. By varying 3D printed bones, silicone joints, and tendon configurations, TALE can match the morphology of extant, extinct, and even theoretical tails. We first characterized the stiffness of our joint design empirically and in simulation before testing the hypothesis that tails with different vertebral proportions curve differently. We then compared the maximum bending state of two common vertebrate proportions and one theoretical morphology. Uniform bending of joints with different vertebral proportions led to substantial differences in the location of the tail tip, suggesting a significant influence on overall tail function. Future studies can introduce more complex morphologies to establish the mechanisms of diverse tail functions. With this foundational knowledge, we will isolate the key features underlying tail function to inform the design for robotic tails. Images and videos can be found on TALE's project page: https://www.embirlab.com/tale., Comment: 8 pages, 5 figures

Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. Recurrence risk assessment plays a crucial role in personalizing treatment. Current methods, including genomic assays, have limited accuracy and clinical utility, leading to suboptimal decisions for many patients. We developed a test for breast cancer patient stratification based on digital pathology and clinical characteristics using novel AI methods. Specifically, we utilized a vision transformer-based pan-cancer foundation model trained with self-supervised learning to extract features from digitized H&E-stained slides. These features were integrated with clinical data to form a multi-modal AI test predicting cancer recurrence and death. The test was developed and evaluated using data from a total of 8,161 breast cancer patients across 15 cohorts originating from seven countries. Of these, 3,502 patients from five cohorts were used exclusively for evaluation, while the remaining patients were used for training. Our test accurately predicted our primary endpoint, disease-free interval, in the five external cohorts (C-index: 0.71 [0.68-0.75], HR: 3.63 [3.02-4.37, p<0.01]). In a direct comparison (N=858), the AI test was more accurate than Oncotype DX, the standard-of-care 21-gene assay, with a C-index of 0.67 [0.61-0.74] versus 0.61 [0.49-0.73], respectively. Additionally, the AI test added independent information to Oncotype DX in a multivariate analysis (HR: 3.11 [1.91-5.09, p<0.01)]). The test demonstrated robust accuracy across all major breast cancer subtypes, including TNBC (C-index: 0.71 [0.62-0.81], HR: 3.81 [2.35-6.17, p=0.02]), where no diagnostic tools are currently recommended by clinical guidelines. These results suggest that our AI test can improve accuracy, extend applicability to a wider range of patients, and enhance access to treatment selection tools.

Hypergraphs serve as a powerful tool for modeling complex relationships across domains like social networks, transactions, and recommendation systems. The (k,g)-core model effectively identifies cohesive subgraphs by assessing internal connections and co-occurrence patterns, but it is susceptible to inflated cohesiveness due to trivial hyperedges. To address this, we propose the $(k,g,p)$-core model, which incorporates the relative importance of hyperedges for more accurate subgraph detection. We develop both Na\"ive and Advanced pruning algorithms, demonstrating through extensive experiments that our approach reduces the execution frequency of costly operations by 51.9% on real-world datasets.

Graph contrastive learning (GCL) generally requires a large number of samples. The one of the effective ways to reduce the number of samples is using hard negatives (e.g., Mixup). Designing mixing-based approach for GAD can be difficult due to imbalanced data or limited number of anomalies. We propose ANOMIX, a framework that consists of a novel graph mixing approach, ANOMIX-M, and multi-level contrasts for GAD. ANOMIX-M can effectively mix abnormality and normality from input graph to generate hard negatives, which are important for efficient GCL. ANOMIX is (a) A first mixing approach: firstly attempting graph mixing to generate hard negatives for GAD task and node- and subgraph-level contrasts to distinguish underlying anomalies. (b) Accurate: winning the highest AUC, up to 5.49% higher and 1.76% faster. (c) Effective: reducing the number of samples nearly 80% in GCL. Code is available at https://github.com/missinghwan/ANOMIX.

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

Low-light optical imaging refers to the use of cameras to capture images with minimal photon flux. This area has broad application to diverse fields, including optical microscopy for biological studies. In such studies, it is important to reduce the intensity of illumination to reduce adverse effects such as photobleaching and phototoxicity that may perturb the biological system under study. The challenge when minimising illumination is to maintain image quality that reflects the underlying biology and can be used for quantitative measurements. An example is the optical redox ratio which is computed from autofluorescence intensity to measure metabolism. In all such cases, it is critical for researchers to optimise selection and application of scientific cameras to their microscopes, but few resources discuss performance in the low-light regime. In this tutorial, we address the challenges in optical fluorescence imaging at low-light levels for quantitative microscopy, with an emphasis on live biological samples. We analyse the performance of specialised low-light scientific cameras such as the EMCCD, qCMOS, and sCMOS, while considering the differences in platform architecture and the contribution of various sources of noise. The tutorial covers a detailed discussion of user-controllable parameters, as well as the application of post-processing algorithms for denoising. We illustrate these concepts using autofluorescence images of live mammalian embryos captured with a two-photon light sheet fluorescence microscope.

We describe a general method for constructing a minimal basis of transcendental functions tailored to a scattering amplitude. Starting with formal solutions for all master integral topologies, we grade the appearing functions by properties such as their symbol alphabet or letter adjacency. We rotate the basis such that functions with spurious features appear in the least possible number of basis elements. Since their coefficients must vanish for physical quantities, this approach avoids complex cancellations. As a first application, we evaluate all integral topologies relevant to the three-loop $Hggg$ and $Hgq\bar{q}$ amplitudes in the leading-colour approximation and heavy-top limit. We describe the derivation of canonical differential equation systems and present a method for fixing boundary conditions without the need for a full functional representation. Using multiple numerical reductions, we test the maximal transcendentality conjecture for $Hggg$ and identify a new letter which appears in functions of weight 4 and 5. In addition, we provide the first direct analytic computation of a three-point form factor of the operator $\mathrm{Tr}(\phi^2)$ in planar $\mathcal{N}=4$ sYM and find agreement with numerical and bootstrapped results., Comment: 45 pages, 7 figures, 3 tables. Electronic files with results are available under https://zenodo.org/records/13987766; v2 expanded Introduction

Cloud servers use accelerators for common tasks (e.g., encryption, compression, hashing) to improve CPU/GPU efficiency and overall performance. However, users' Service-level Objectives (SLOs) can be violated due to accelerator-related contention. The root cause is that existing solutions for accelerators only focus on isolation or fair allocation of compute and memory resources; they overlook the contention for communication-related resources. Specifically, three communication-induced challenges drive us to re-think the problem: (1) Accelerator traffic patterns are diverse, hard to predict, and mixed across users, (2) communication-related components lack effective low-level isolation mechanism to configure, and (3) computational heterogeneity of accelerators lead to unique relationships between the traffic mixture and the corresponding accelerator performance. The focus of this work is meeting SLOs in accelerator-rich systems. We present \design{}, treating accelerator SLO management as traffic management with proactive traffic shaping. We develop an SLO-aware protocol coupled with an offloaded interface on an architecture that supports precise and scalable traffic shaping. We guarantee accelerator SLO for various circumstances, with up to 45% tail latency reduction and less than 1% throughput variance.

As the signatories of this manifesto, we denounce the attention economy as inhumane and a threat to our sociopolitical and ecological well-being. We endorse policymakers' efforts to address the negative consequences of the attention economy's technology, but add that these approaches are often limited in their criticism of the systemic context of human attention. Starting from Buddhist philosophy, we advocate a broader approach: an ecology of attending, that centers on conceptualizing, designing, and using attention (1) in an embedded way and (2) focused on the alleviating of suffering. With 'embedded' we mean that attention is not a neutral, isolated mechanism but a meaning-engendering part of an 'ecology' of bodily, sociotechnical and moral frameworks. With 'focused on the alleviation of suffering' we explicitly move away from the (often implicit) conception of attention as a tool for gratifying desires., Comment: 21 pages, 1 figure

The streaming instability (SI) is a leading mechanism for concentrating solid particles into regions dense enough to form planetesimals. Its efficiency in clumping particles depends primarily on the dimensionless stopping time ($\tau_s$, a proxy for particle size) and dust-to-gas surface density ratio ($Z$). Previous simulations identified a critical $Z$ ($Z_{\rm{crit}}$) above which strong clumping occurs, where particle densities exceed the Hill density (thus satisfying a condition for gravitational collapse), over a wide range of $\tau_s$. These works found that for $\tau_s \leq 0.01$, $Z_{\rm{crit}}$ was above the ISM value $(\sim 0.01)$. In this work, we reexamine the clumping threshold using 2D axisymmetric, stratified simulations at high resolution and with relatively large (compared to many previous simulations) domain sizes. Our main results are as follows: First, when $\tau_s = 0.01$, strong clumping occurs even at $Z \lesssim 0.01$, lower than $Z_{\rm{crit}}$ found in all previous studies. Consequently, we revise a previously published fit to the $Z_{\rm{crit}}$ curve to account for this updated $Z_{\rm{crit}}$. Second, higher resolution results in a thicker dust layer, which may result from other instabilities manifesting, such as the vertical shearing streaming instability. Third, despite this thicker layer, higher resolution can lead to strong clumping even with lower midplane dust-to-gas density ratios (which results from the thicker particle layer) so long as $Z \gtrsim Z_{\rm{crit}}$. Our results demonstrate the efficiency of the SI in clumping small particles at $Z \sim 0.01$, which is a significant refinement of the conditions for planetesimal formation by the SI., Comment: 25 pages, 12 figures, submitted to the Astrophysical Journal

Let $R$ be a commutative ring with unity and let $X$ be an indeterminate over $R$. The \textit{Anderson ring} of $R$ is defined as the quotient ring of the polynomial ring $R[X]$ by the set of polynomials that evaluate to $1$ at $0$. Specifically, the Anderson ring of $R$ is $R[X]_A$, where $A=\{f\in R[X]\mid f(0)=1\}$. In this paper, we aim to investigate the transfer of various ring-theoretic properties between the ring $R$ and its Anderson ring $R[X]_A$. Interesting results are established, accompanied by applications and illustrative examples., Comment: 20 pages

Imitation learning, which enables robots to learn behaviors from demonstrations by human, has emerged as a promising solution for generating robot motions in such environments. The imitation learning-based robot motion generation method, however, has the drawback of depending on the demonstrator's task execution speed. This paper presents a novel temporal ensemble approach applied to imitation learning algorithms, allowing for execution of future actions. The proposed method leverages existing demonstration data and pre-trained policies, offering the advantages of requiring no additional computation and being easy to implement. The algorithms performance was validated through real-world experiments involving robotic block color sorting, demonstrating up to 3x increase in task execution speed while maintaining a high success rate compared to the action chunking with transformer method. This study highlights the potential for significantly improving the performance of imitation learning-based policies, which were previously limited by the demonstrator's speed. It is expected to contribute substantially to future advancements in autonomous object manipulation technologies aimed at enhancing productivity., Comment: This paper was initially submitted to The Journal of Korea Robotics Society on Oct. 22, 2024, and a revised version was submitted on Nov. 13, 2024. It is currently under review

Traditional machine translation methods typically involve training models directly on large parallel corpora, with limited emphasis on specialized terminology. However, In specialized fields such as patent, finance, or biomedical domains, terminology is crucial for translation, with many terms that needs to be translated following agreed-upon conventions. In this paper we introduce a methodology that efficiently trains models with a smaller amount of data while preserving the accuracy of terminology translation. We achieve this through a systematic process of term extraction and glossary creation using the Trie Tree algorithm, followed by data reconstruction to teach the LLM how to integrate these specialized terms. This methodology enhances the model's ability to handle specialized terminology and ensures high-quality translations, particularly in fields where term consistency is crucial. Our approach has demonstrated exceptional performance, achieving the highest translation score among participants in the WMT patent task to date, showcasing its effectiveness and broad applicability in specialized translation domains where general methods often fall short., Comment: Accepted to WMT 2024