Miracle-dz/UniFAR-FTUs huggingface dataset:
Sizes and Mass Profiles of Candidate Massive Galaxies Discovered by JWST at 7 < z < 9: Evidence for Very Early Formation of the Central ∼100 pc of Present-day Ellipticals

The first JWST data revealed an unexpected population of red galaxies that appear to have redshifts of z ∼ 7–9 and high masses of M * ≳ 1010 M ☉. Here we fit Sérsic profiles to the F200W NIRCam images of the 13 massive galaxy candidates of Labbé et al., to determine their structural parameters. Satisfactory fits were obtained for nine galaxies. We find that their effective radii are extremely small, ranging from r e ∼ 80 pc to r e ∼ 300 pc, with a mean of 〈r e〉 ≈ 150 pc. For their apparent stellar masses, the galaxies are smaller than any other galaxy population that has been observed at any other redshift. We use the fits to derive circularized three-dimensional stellar mass profiles of the galaxies, and compare these to the mass profiles of massive quiescent galaxies at z ∼ 2.3 and nearby elliptical galaxies. Despite the fact that the high-redshift galaxies have 10–20 times smaller half-light radii than their putative descendants, the central stellar densities are very similar. The most straightforward interpretation is that the dense compact inner regions of the most massive ellipticals today were already in place ∼600 Myr after the Big Bang. We caution that the redshifts and masses of the galaxies remain to be confirmed, and that the complex NIRCam point-spread function is not yet fully characterized.
Hierarchical Neural Memory Network for Low Latency Event Processing

This paper proposes a low latency neural network architecture for event-based dense prediction tasks. Conventional architectures encode entire scene contents at a fixed rate regardless of their temporal characteristics. Instead, the proposed network encodes contents at a proper temporal scale depending on its movement speed. We achieve this by constructing temporal hierarchy using stacked latent memories that operate at different rates. Given low latency event steams, the multi-level memories gradually extract dynamic to static scene contents by propagating information from the fast to the slow memory modules. The architecture not only reduces the redundancy of conventional architectures but also exploits long-term dependencies. Further-more, an attention-based event representation efficiently encodes sparse event streams into the memory cells. We conduct extensive evaluations on three event-based dense prediction tasks, where the proposed approach outperforms the existing methods on accuracy and latency, while demonstrating effective event and image fusion capabilities. The code is available at https://hamarh.github. io/hmnet/.
SAP HANA Data Volume Management

Today information technology is a data-driven environment. The role of data is to empower business leaders to make decisions based on facts, trends, and statistical numbers. SAP is no exception. In modern days many companies use business suites like SAP on HANA S/4 or ERP or SAP Business Warehouse and other non-SAP applications and run those on HANA databases for faster processing. While HANA is an extremely powerful in-memory database, growing business data has an impact on the overall performance and budget of the organization. This paper presents best practices to reduce the overall data footprint of HANA databases for three use cases like SAP Business Suite on HANA, SAP Business Warehouse, and Native HANA database.
Image Quality Is Not All You Want: Task-Driven Lens Design for Image Classification

In computer vision, it has long been taken for granted that high-quality images obtained through well-designed camera lenses would lead to superior results. However, we find that this common perception is not a"one-size-fits-all"solution for diverse computer vision tasks. We demonstrate that task-driven and deep-learned simple optics can actually deliver better visual task performance. The Task-Driven lens design approach, which relies solely on a well-trained network model for supervision, is proven to be capable of designing lenses from scratch. Experimental results demonstrate the designed image classification lens (``TaskLens'') exhibits higher accuracy compared to conventional imaging-driven lenses, even with fewer lens elements. Furthermore, we show that our TaskLens is compatible with various network models while maintaining enhanced classification accuracy. We propose that TaskLens holds significant potential, particularly when physical dimensions and cost are severely constrained.
Ghost Noise for Regularizing Deep Neural Networks

Batch Normalization (BN) is widely used to stabilize the optimization process and improve the test performance of deep neural networks. The regularization effect of BN depends on the batch size and explicitly using smaller batch sizes with Batch Normalization, a method known as Ghost Batch Normalization (GBN), has been found to improve generalization in many settings. We investigate the effectiveness of GBN by disentangling the induced ``Ghost Noise'' from normalization and quantitatively analyzing the distribution of noise as well as its impact on model performance. Inspired by our analysis, we propose a new regularization technique called Ghost Noise Injection (GNI) that imitates the noise in GBN without incurring the detrimental train-test discrepancy effects of small batch training. We experimentally show that GNI can provide a greater generalization benefit than GBN. Ghost Noise Injection can also be beneficial in otherwise non-noisy settings such as layer-normalized networks, providing additional evidence of the usefulness of Ghost Noise in Batch Normalization as a regularizer.
On the structure of high-Reynolds-number supersonic turbulent boundary layers

Experimental results are presented that reveal key features of the large-scale organized structures in a supersonic, turbulent boundary layer. Measurements were obtained in a Mach 3 zero-pressure-gradient boundary layer using a crossed-wire probe and arrays of normal hot wires with vertical, spanwise, and streamwise separations ranging from 0.1 to 0. 6δ. Space–time correlation results indicate the existence of large-scale structures of a size comparable to δ, with a spanwise extent only slightly less than the vertical scale. The convection velocity of the large-scale motions is nearly constant across 80% of the boundary layer and is equal to approximately 0.9U∞. It is shown that positive events detected with the VITA conditional sampling technique correspond to steep gradients in the streamwise mass flux which extend across most of the boundary layer. These sharp gradients appear to be the upstream interfaces of large-scale turbulent ‘bulges’, similar to those seen in incompressible boundary layers. In a reference frame moving with the convection speed of the sharp gradients, low-momentum fluid is observed rotating on a large-scale, while high-momentum fluid forms a saddle point on the upstream edge of the large-scale motion. These motions are associated with elevated levels of the shear product, emphasizing their role in the dynamics of the boundary layer.
Versatile, open-access opto-mechanics platform for optical microscopes prototyping

Prototype optical microscopes, built to pursue developments in advanced imaging techniques, need specific optomechanical constructions: preferably with high flexibility in the elements arrangement, easy access to the optical paths, straightforward integration with external optical subsystems - light sources and detectors - as well as good mechanical stability. Typically they are either built around an adapted commercial microscope body or as a home-built setup, based on standard optomechanical elements, and neither solution delivers the desired characteristics. We developed a series of versatile platforms for prototyping optical microscopes in various configurations that use folding mirror(s) to maintain the optical paths horizontal throughout most of the setup, thus enabling the use of standard optical components in the excitation and detection paths and, last but not least, increasing the laser safety of the optical system.
Negation and the causative verb shi in Han period Chinese

In this paper, a synchronic study of the syntax and the semantics of the causative verb shi in Han period Chinese, particularly in combination with negatives, is at issue. Two different structures involving negation of this verb exist in Han period Chinese: first, (NP1) NEGshi NP2 V2P and second (NP1) shi NP2NEG V2P. A thorough analysis of the first structure ‘(NP1) NEGshi NP2 V2P' shows that two different semantic interpretations are required to account for the syntactic differences between the two structures. The article proposes that the first structure has to be analysed as an analytic causative construction with a visible causative verb which, in its negated form, contrasts with an affirmative causative construction. The negative has scope over the entire causative construction including V1 and V2P, whereas in the second structure the negative has only scope over V2P
Assessment of Spatial Distribution Uniformity in 2-Dimensional Discrete Space

This paper proposes an algorithm to assess the spatial distribution uniformity in 2-dimensional discrete space. Spatial distribution uniformity refers to how uniform some objects distribute in a space. The algorithm takes a matrix of logical values that represents the distribution pattern as the input and outputs a number that represents the degree of the uniformity. A pseudo energy attenuation model and the uniformity coefficients in the irrigation field are leveraged in this algorithm. Experiment results show that this algorithm is effective in assessing spatial distribution uniformity in 2-dimensional discrete space.
Association of unmet basic resource needs with frailty and quality of life among older adults with cancer—Results from the CARE registry

Basic resource needs related to transportation, housing, food, and medications are important social determinants of health and modifiable indicators of poverty, but their role in modifying the risk of frailty and health‐related quality of life (HRQoL) remains unknown. The goal of our study was to examine the prevalence of unmet basic needs and their association with frailty and HRQoL in a cohort of older adults with cancer.
Wnt-β-catenin in hepatobiliary homeostasis, injury, and repair

Wnt-β-catenin signaling has emerged as an important regulatory pathway in the liver, playing key roles in zonation and mediating contextual hepatobiliary repair after injuries. In this review, we will address the major advances in understanding the role of Wnt signaling in hepatic zonation, regeneration, and cholestasis-induced injury. We will also touch on some important unanswered questions and discuss the relevance of modulating the pathway to provide therapies for complex liver pathologies that remain a continued unmet clinical need.
A multiparametric alternative to short inversion‐time inversion recovery for imaging inflammation: T2water and fat fraction measurement using chemical shift–encoded turbo spin‐echo MRI

Short‐inversion‐time inversion‐recovery MRI is used widely for imaging bone and soft‐tissue inflammation in rheumatic inflammatory diseases, but there is no widely available quantitative equivalent of this sequence. This limits our ability to objectively assess inflammation and distinguish it from other processes. To address this, we investigate the use of the widely available Dixon turbo spin echo (TSE Dixon) sequence as a practical approach to simultaneous water‐specific T2 (T2water) and fat fraction (FF) measurement.
A Two-Stage Image Segmentation Model for Multi-Channel Images

This paper introduces a two-stage model for multi-channel image segmentation, which is motivated by minimal surface theory. Indeed, in the first stage, we acquire a smooth solution u from a convex variational model related to minimal surface property and different data fidelity terms are considered. This minimization problem is solved efficiently by the classical primal-dual approach. In the second stage, we adopt thresholding to segment the smoothed image u . Here, instead of using K-means to determine the thresholds, we propose a more stable hill-climbing procedure to locate the peaks on the 3D histogram of u as thresholds, in the meantime, this algorithm can also detect the number of segments. Finally, numerical results demonstrate that the proposed method is very robust against noise and superior to other image segmentation approaches.
Impact of corticosteroids use on midterm sequelae in survivors of COVID‐19 admitted to hospital: A prospective cohort study

An understanding of the midterm sequelae in COVID‐19 and their association with corticosteroids use are needed. Between March and July 2020, we evaluated 1227 survivors of COVID‐19, 3 months posthospitalization, of whom 213 had received corticosteroids within 7 days of admission. Main outcome was any midterm sequelae (oxygen therapy, shortness of breath, one major clinical sign, two minor clinical signs or three minor symptoms). Association between corticosteroids use and midterm sequelae was assessed using inverse propensity‐score weighting models. Our sample included 753 (61%) male patients, and 512 (42%) were older than 65 years. We found a higher rate of sequelae among users than nonusers of corticosteroids (42% vs. 35%, odds ratio [OR] 1.40 [1.16–1.69]). Midterm sequelae were more frequent in users of low‐dose corticosteroids than nonusers (64% vs. 51%, OR 1.60 [1.10–2.32]), whereas no association between higher doses (≥20 mg/day equivalent of dexamethasone) and sequelae was evidenced (OR 0.95 [0.56–1.61]). Higher risk of sequelae with corticosteroids use was observed among subjects with propensity score below the 90th percentile. Our study suggest that corticosteroids use during hospitalization for COVID‐19 is associated with higher risk of midterm sequelae.
Serial Block Face‐Scanning Electron Microscopy as a Burgeoning Technology

Serial block face scanning electron microscopy (SBF‐SEM), also referred to as serial block‐face electron microscopy, is an advanced ultrastructural imaging technique that enables three‐dimensional visualization that provides largerx‐ and y‐axis ranges than other volumetric EM techniques. While SEM is first introduced in the 1930s, SBF‐SEM is developed as a novel method to resolve the 3D architecture of neuronal networks across large volumes with nanometer resolution by Denk and Horstmann in 2004. Here, the authors provide an accessible overview of the advantages and challenges associated with SBF‐SEM. Beyond this, the applications of SBF‐SEM in biochemical domains as well as potential future clinical applications are briefly reviewed. Finally, the alternative forms of artificial intelligence‐based segmentation which may contribute to devising a feasible workflow involving SBF‐SEM, are also considered.
The multifaceted role of aquaporins in physiological cell migration.

Cell migration is an essential process that underlies many physiological processes, including the immune response, organogenesis in the embryo, and angiogenesis, as well as pathological processes such as cancer metastasis. Cells have at their disposal a variety of migratory behaviors and mechanisms that seem to be specific to cell type and the microenvironment. Research over the past two decades has elucidated the water channel protein family of aquaporins (AQPs) as a regulator of many cell migration-related processes, from physical phenomena to biological signaling pathways. The roles that AQPs play in cell migration are both cell type- and isoform-specific; thus, a large swath of information has accumulated as researchers seek to identify the responses across these distinct variables. There does not seem to be a universal role that AQPs play in cell migration; the complex interplay between AQPs and cell volume management, signaling pathway activation, and in a few identified circumstances, gene expression regulation, has shown the intricate, and perhaps paradoxical, role of AQPs in cell migration. The objective of this review is to provide an organized and integrated collection of recent work that has elucidated the many mechanisms by which AQPs regulate cell migration.
Turbulence measurements in positive surges and bores

A positive surge results from a sudden change in flow that increases the flow depth. New experiments were conducted in a large channel. Most positive surge tests were conducted with a horizontal bed slope, a constant flow rate and uncontrolled flow conditions. The only dependant variable was the downstream gate opening after closure. Detailed turbulence measurements were performed with high-temporal resolution using side-looking acoustic Doppler velocimetry. Two types of positive surge were observed: undular surge for Froude numbers less than 1.7, and weak (breaking) surges above. Instantaneous velocity measurements beneath advancing surges showed a marked effect of the surge passage on the velocity field. Streamwise velocities showed rapid flow deceleration at all vertical elevations. Large fluctuations of longitudinal and transverse velocities were recorded beneath the surges, including some unsteady flow recirculation beneath a weak surge front. Turbulent stresses were deduced from high-pass filtered data. The results showed large normal and tangential Reynolds stresses beneath the surges. A comparison between undular and weak surges suggested some major difference. In weak surge flows, the data showed rapid flow separation beneath the surge front. In undular surges, maximum Reynolds stresses were observed beneath and just before each wave crest behind the leading wave.
A combination predicting methodology based on T-LSTNet_Markov for short-term wind power prediction

ABSTRACT Wind power has been valued by countries for its renewability and cleanness and has become most of the focus of energy development in all countries. However, due to the uncertainty and volatility of wind power generation, making the grid-connected wind power system presents some serious challenges. Improving the accuracy of wind power prediction has become the focus of current research. Therefore, this paper proposes a combined short-term wind power prediction model based on T-LSTNet_markov to improve prediction accuracy. First, perform data cleaning and data preprocessing operations on the original data. Second, forecast using T-LSTNet model in original wind power data. Finally, calculate the error between the forecast value and the actual value. The k-means++ method and Weighted Markov process are used to correct errors and to get the result of the final prediction. The data that are collected from a wind farm in Inner Mongolia Autonomous Region, China, are selected as a case study to demonstrate the effectiveness of the proposed combined models. The empirical results show that the prediction accuracy is further improved after correcting errors.
Explaining Boomerang Effects in Persuasive Health Communication: How Psychological Reactance to Healthy Eating Messages Elevates Attention to Unhealthy Food

While psychological reactance is often invoked to explain the unintended boomerang effects of persuasive health messages, underlying processes that might explain how reactance affects behavior are rarely studied. We investigated whether messages that elicit reactance can bias attention by increasing the perception of information that potentially facilitates adverse behavior. Participants (N = 998) were assigned to one of three experimental conditions: reading an aggressive and emotional text asking them to stop eating meat (appeal condition); reading a neutral text about the nativeness and benefits of eating less meat (information condition); or completing an unrelated word count task (control condition). After assessing their reactance, participants were asked to identify as many words as possible in a word grid in which some words related to meat. Compared to the other conditions, the appeal condition elicited the greatest reactance. Furthermore, omnivore participants in this condition identified significantly more meat-related words when they reported higher levels of reactance. By showing that psychological reactance elicited by forceful health appeals increases attention to information that may facilitate the admonished behaviors, our findings contribute to an improved understanding of effective health communication.
A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer

In this paper, we propose a novel Hadamard Transform (HT)-based neural network layer for hybrid quantum-classical computing. It implements the regular convolutional layers in the Hadamard transform domain. The idea is based on the HT convolution theorem which states that the dyadic convolution between two vectors is equivalent to the element-wise multiplication of their HT representation. Computing the HT is simply the application of a Hadamard gate to each qubit individually, so the HT computations of our proposed layer can be implemented on a quantum computer. Compared to the regular Conv2D layer, the proposed HT-perceptron layer is computationally more efficient. Compared to a CNN with the same number of trainable parameters and 99.26\% test accuracy, our HT network reaches 99.31\% test accuracy with 57.1\% MACs reduced in the MNIST dataset; and in our ImageNet-1K experiments, our HT-based ResNet-50 exceeds the accuracy of the baseline ResNet-50 by 0.59\% center-crop top-1 accuracy using 11.5\% fewer parameters with 12.6\% fewer MACs.
Large deviations for diffusions: Donsker-Varadhan meet Freidlin-Wentzell

We consider a diffusion process on $\mathbb R^n$ and prove a large deviation principle for the empirical process in the joint limit in which the time window diverges and the noise vanishes. The corresponding rate function is given by the expectation of the Freidlin-Wentzell functional per unit of time. As an application of this result, we obtain a variational representation of the rate function for the Gallavotti-Cohen observable in the small noise and large time limits.
Association of Low-Dose Colchicine With Incidence of Knee and Hip Replacements

BACKGROUND Osteoarthritis is a major contributor to pain and disability worldwide. Given that inflammation plays an important role in the development of osteoarthritis, anti-inflammatory drugs may slow disease progression. OBJECTIVE To examine whether colchicine, 0.5 mg daily, reduces incident total knee replacements (TKRs) and total hip replacements (THRs). DESIGN Exploratory analysis of the LoDoCo2 (Low-Dose Colchicine 2) randomized, controlled, double-blind trial. (Australian New Zealand Clinical Trials Registry: ACTRN12614000093684). SETTING 43 centers in Australia and the Netherlands. PATIENTS 5522 patients with chronic coronary artery disease. INTERVENTION Colchicine, 0.5 mg, or placebo once daily. MEASUREMENTS The primary outcome was time to first TKR or THR since randomization. All analyses were performed on an intention-to-treat basis. RESULTS A total of 2762 patients received colchicine and 2760 received placebo during a median follow-up of 28.6 months. During the trial, TKR or THR was performed in 68 patients (2.5%) in the colchicine group and 97 (3.5%) in the placebo group (incidence rate, 0.90 vs. 1.30 per 100 person-years; incidence rate difference, -0.40 [95% CI, -0.74 to -0.06] per 100 person-years; hazard ratio, 0.69 [CI, 0.51 to 0.95]). In sensitivity analyses, similar results were obtained when patients with gout at baseline were excluded and when joint replacements that occurred in the first 3 and 6 months of follow-up were omitted. LIMITATION LoDoCo2 was not designed to investigate the effect of colchicine in osteoarthritis of the knee or hip and did not collect information specifically on osteoarthritis. CONCLUSION In this exploratory analysis of the LoDoCo2 trial, use of colchicine, 0.5 mg daily, was associated with a lower incidence of TKR and THR. Further investigation of colchicine therapy to slow disease progression in osteoarthritis is warranted. PRIMARY FUNDING SOURCE None.