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Shape memory alloys that can deform and then spring back to their original shape, have found a wide range of applications in the medical field, from heart valves to stents. As we push the boundaries of technology creating smaller, more precise tools for delicate surgery treatments, the behavior of these alloys at tiny scales becomes increasingly crucial. In this study, we discover that the size effect of critical stress required for stress-induced phase transformation is not universal. We propose an orientation-dependent power decay law, indicating a specific increase in critical stress for pillars smaller than 1 micron meter for the nominally soft [001] and hard [111] orientations. Additionally, we observe high transformability with 11\% recoverable strain under high stress (2 GPa) through lattice frustration at 200nm scale. This research opens new avenues for exploring the superior elastic behavior of shape memory alloys for nanodevices., Comment: 6 pages, 5 figures

Here, we explore the formation of primordial black holes (PBHs) within a scalar field inflationary model coupled to the Gauss-Bonnet (GB) term, incorporating low-scale spontaneously broken supersymmetric (SB SUSY) potential. The coupling function amplifies the curvature perturbations, consequently leading to the formation of PBHs and the production of detectable secondary gravitational waves (GWs). Through the adjustment of model parameters, the inflaton can be decelerated during the ultra-slow-roll (USR) phase, thereby augmenting curvature perturbations. Swampland criteria are also investigated. Our computations forecast the formation of PBHs with masses on the order of ${\cal O}(10)M_{\odot}$, aligning with observational data from LIGO-Virgo and PBHs with masses ${\cal O}(10^{-6})M_{\odot}$, offering potential explanation for ultrashort-timescale microlensing events recorded in OGLE data. Additionally, our proposed mechanism can generate PBHs with masses around ${\cal O}(10^{-13})M_{\odot}$, constituting roughly 99$\%$ of the dark matter. The density parameter of produced GWs ($\Omega_{\rm GW_0}$) intersects with the sensitivity curves of GW detectors. Two cases of our model fall within the nano-Hz regime. One of these satisfies the $\Omega_{\rm{GW}}(f) \sim f^{5-\gamma}$ fit, with the derived $\gamma = 3.51$ matching the NANOGrav 15-year data., Comment: 30 pages, 6 figures, 3 tables

With a high rate of morbidity and mortality, colorectal cancer (CRC) ranks third in mortality among cancers. By analyzing the texture properties of images and quantifying the heterogeneity of tumors, radiomics and radiogenomics are non-invasive methods to determine the biological properties of CRC. Recently, several articles have discussed the application of radiomics in different aspects of CRC. Therefore, given the large amount of data published, this review aims to discuss how radiomics can be used for distinguishing benign and malignant colorectal lesions, diagnosing, staging, predicting prognosis and treatment response, and predicting lymph node and hepatic metastasis of CRC, based on radiomic features extracted from magnetic resonance imaging (MRI), computed tomography (CT), esophageal ultrasonography (EUS), and positron emission tomography-CT (PET-CT). Challenges in bringing radiomics to clinical application and future solutions have also been discussed. With the progress made in radiomics and the application of deep and machine learning in this area, radiomics can become one of the main tools for the personalized management of CRC patients shortly.

In this work, a new fast-roll (FR) mechanism to generate primordial black holes (PBHs) and their coeval gravitational waves (GWs) in generalized non-canonical natural inflation is introduced. In this model, choosing a suitable function for non-canonical mass scale parameter $M(\phi)$ gives rise to produce a cliff-like region in field evolution path. When inflaton rolls down the steep cliff, its kinetic energy during the FR stage is amplified in comparison with slow-roll (SR) stage. Hence, seeds of PBHs production are born in this transient FR stage. Depending on the position of the cliff, appropriate cases of PBHs for explaining total dark matter (DM), microlensing effects, LIGO-VIRGO events and NANOGrav 15 year data can be formed. The density spectrum of GWs related to one case of the model lies in the NANOGrav 15 year domain and behaves like $\Omega_{\rm GW_0}\sim f^{5-\gamma}$. The spectral index $\gamma=3.42$ for this case satisfies the NANOGrav 15 year constraint. Moreover, regarding reheating considerations, it is demonstrated that PBHs are born in radiation-dominated (RD) era. Furthermore, viability of the model in light of theoretical swampland criteria and observational constraints on cosmic microwave background (CMB) scales are illustrated., Comment: 29 pages, 6 figures, 5 tables

Immunotherapy is an effective precision medicine treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma. A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis. Results Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed. Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability., Comment: Published in Neuro-Oncology Advances 2024

Machine Learning (ML) operators are the building blocks to design ML models with various target applications. GEneral Matrix Multiplication (GEMM) operators are the backbone of ML models. They are notorious for being computationally expensive requiring billions of multiply-and-accumulate. Therefore, significant effort has been put to study and optimize the GEMM operators in order to speed up the execution of ML models. GPUs and accelerators are widely deployed to accelerate ML workloads by optimizing the execution of GEMM operators. Nonetheless, the performance of NonGEMM operators have not been studied as thoroughly as GEMMs. Therefore, this paper describes \bench, a benchmark to study NonGEMM operators. We first construct \bench using popular ML workloads from different domains, then perform case studies on various grade GPU platforms to analyze the behavior of NonGEMM operators in GPU accelerated systems. Finally, we present some key takeaways to bridge the gap between GEMM and NonGEMM operators and to offer the community with potential new optimization directions.

Here, the quartic inflationary potential $V(\phi)=\frac{\lambda}{4}\phi^4$ in non-canonical framework with a power-law Lagrangian is investigated. We demonstrate that the predictions of this potential in non-canonical framework align with the observational data of Planck 2018. We determine how the predictions of the model vary with changes in the non-canonical parameter $\alpha$ and the number of $e$-folds $N$. The sound speed, non-Gaussianity parameter, scalar spectral index, and tensor-to-scalar ratio are all influenced by $\alpha$. However, the scalar spectral index exhibits degeneracy with respect to changes in $\alpha$. It is found that, this degeneracy does not break through reheating consideration. But the length of the reheating epoch constrains the length of the inflation period and the value of $\alpha$. Therefore, we investigate relic gravitational waves (GWs) and show that, the degeneracy of the model with respect to the $\alpha$ parameter can be broken using the relic GWs. Additionally, the density of relic GWs falls within the sensitivity range of GWs detectors for the specific $e$-folds number between 55 and 55.7., Comment: 25 pages, 6 figures, 3 tables

Skeleton-based video anomaly detection (SVAD) is a crucial task in computer vision. Accurately identifying abnormal patterns or events enables operators to promptly detect suspicious activities, thereby enhancing safety. Achieving this demands a comprehensive understanding of human motions, both at body and region levels, while also accounting for the wide variations of performing a single action. However, existing studies fail to simultaneously address these crucial properties. This paper introduces a novel, practical and lightweight framework, namely Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection (GiCiSAD) to overcome the challenges associated with SVAD. GiCiSAD consists of three novel modules: the Graph Attention-based Forecasting module to capture the spatio-temporal dependencies inherent in the data, the Graph-level Jigsaw Puzzle Maker module to distinguish subtle region-level discrepancies between normal and abnormal motions, and the Graph-based Conditional Diffusion model to generate a wide spectrum of human motions. Extensive experiments on four widely used skeleton-based video datasets show that GiCiSAD outperforms existing methods with significantly fewer training parameters, establishing it as the new state-of-the-art., Comment: Accepted at the Winter Conference on Applications of Computer Vision (WACV). 17 pages, 6 figures, 6 tables

Here, we investigate the formation of primordial black holes (PBHs) in non-minimal coupling Gauss-Bonnet inflationary model in the presence of power-law potentials. We employ a two part coupling function to enhance primordial curvatures at small scales as well as satisfy Planck measurements at the CMB scale. Moreover, our model satisfies the swampland criteria. We find PBHs with different mass scales and demonstrate that PBHs with masses around $\mathcal{O}(10^{-14})M_{\odot}$ can account for almost all of the dark matter in the universe. In addition, we investigate the implications of the reheating stage and show that the PBHs in our model are generated during the radiation-dominated era. Furthermore, we investigate the production of scalar-induced gravitational waves (GWs). More interestingly enough is that, for the specific cases $D_{\rm n}$ in our model, the GWs can be considered as a source of PTA signal. %evaluate the idea that the induced GWs propagating concurrently with the PBH production are the source of NANOGrav signal. Also, we conclude that the GWs energy density parameter at the nano-Hz regime can be parameterized as $\Omega_{\rm GW_0} (f) \sim f^{5-\gamma}$, where the obtained $\gamma$ is consistent with the PTA Observations., Comment: 39 pages, 10 figures, 5 tables

In the rapidly evolving field of deep learning, the demand for models that are both expressive and computationally efficient has never been more critical. This paper introduces Orchid, a novel architecture designed to address the quadratic complexity of traditional attention mechanisms without compromising the ability to capture long-range dependencies and in-context learning. At the core of this architecture lies a new data-dependent global convolution layer, which contextually adapts its kernel conditioned on input sequence using a dedicated conditioning neural network. We design two simple conditioning networks that maintain shift equivariance in our data-dependent convolution operation. The dynamic nature of the proposed convolution kernel grants Orchid high expressivity while maintaining quasilinear scalability for long sequences. We evaluate the proposed model across multiple domains, including language modeling and image classification, to highlight its performance and generality. Our experiments demonstrate that this architecture not only outperforms traditional attention-based architectures such as BERT and Vision Transformers with smaller model sizes, but also extends the feasible sequence length beyond the limitations of the dense attention layers. This achievement represents a significant step towards more efficient and scalable deep learning models for sequence modeling.

Data annotation generally refers to the labeling or generating of raw data with relevant information, which could be used for improving the efficacy of machine learning models. The process, however, is labor-intensive and costly. The emergence of advanced Large Language Models (LLMs), exemplified by GPT-4, presents an unprecedented opportunity to automate the complicated process of data annotation. While existing surveys have extensively covered LLM architecture, training, and general applications, we uniquely focus on their specific utility for data annotation. This survey contributes to three core aspects: LLM-Based Annotation Generation, LLM-Generated Annotations Assessment, and LLM-Generated Annotations Utilization. Furthermore, this survey includes an in-depth taxonomy of data types that LLMs can annotate, a comprehensive review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation. Serving as a key guide, this survey aims to assist researchers and practitioners in exploring the potential of the latest LLMs for data annotation, thereby fostering future advancements in this critical field.

Direct numerical simulation of a turbulent thermal boundary layer (TTBL) can perform the role of an analogy to simulate bushfires that can serve as a testbed for artificial intelligence (AI) enhanced remote sensing of bushfire propagation. By solving the Navier-Stokes equations for a turbulent flow, DNS predicts the flow field and allows for a detailed study of the interactions between the turbulent flow and thermal plumes. In addition to potentially providing insights into the complex bushfire behaviour, direct numerical simulation (DNS) can generate synthetic remote sensing data to train AI algorithms such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which can process large amounts of remotely sensed data associated with bushfire. Using the results of DNS as training data can improve the accuracy of AI remote sensing in predicting firefront propagation of bushfires. DNS can also test the accuracy of the AI remote sensing algorithms by generating synthetic remote sensing data that allows their performance assessment and uncertainty quantification in predicting the evolution of a bushfire. The combination of DNS and AI can improve our understanding of bushfire dynamics, develop more accurate prediction models, and aid in bushfire management and mitigation., Comment: 16 pages, 8 figures; aspects of this paper presented as a Plenary Lecture at: International Symposium on Unmanned Systems: AI, Design and Efficiency (ISUDEF23), June 7-9, 2023, KTH, Stockholm, Sweden

Irregular sampling of time series in electronic health records (EHRs) is one of the main challenges for developing machine learning models. Additionally, the pattern of missing data in certain clinical variables is not at random but depends on the decisions of clinicians and the state of the patient. Point process is a mathematical framework for analyzing event sequence data that is consistent with irregular sampling patterns. Our model, TEE4EHR, is a transformer event encoder (TEE) with point process loss that encodes the pattern of laboratory tests in EHRs. The utility of our TEE has been investigated in a variety of benchmark event sequence datasets. Additionally, we conduct experiments on two real-world EHR databases to provide a more comprehensive evaluation of our model. Firstly, in a self-supervised learning approach, the TEE is jointly learned with an existing attention-based deep neural network which gives superior performance in negative log-likelihood and future event prediction. Besides, we propose an algorithm for aggregating attention weights that can reveal the interaction between the events. Secondly, we transfer and freeze the learned TEE to the downstream task for the outcome prediction, where it outperforms state-of-the-art models for handling irregularly sampled time series. Furthermore, our results demonstrate that our approach can improve representation learning in EHRs and can be useful for clinical prediction tasks.

Time series in Electronic Health Records (EHRs) present unique challenges for generative models, such as irregular sampling, missing values, and high dimensionality. In this paper, we propose a novel generative adversarial network (GAN) model, TimEHR, to generate time series data from EHRs. In particular, TimEHR treats time series as images and is based on two conditional GANs. The first GAN generates missingness patterns, and the second GAN generates time series values based on the missingness pattern. Experimental results on three real-world EHR datasets show that TimEHR outperforms state-of-the-art methods in terms of fidelity, utility, and privacy metrics.

3C84 is a nearby radio source with a complex total intensity structure, showing linear polarisation and spectral patterns. A detailed investigation of the central engine region necessitates the use of VLBI above the hitherto available maximum frequency of 86GHz. Using ultrahigh resolution VLBI observations at the highest available frequency of 228GHz, we aim to directly detect compact structures and understand the physical conditions in the compact region of 3C84. We used EHT 228GHz observations and, given the limited (u,v)-coverage, applied geometric model fitting to the data. We also employed quasi-simultaneously observed, multi-frequency VLBI data for the source in order to carry out a comprehensive analysis of the core structure. We report the detection of a highly ordered, strong magnetic field around the central, SMBH of 3C84. The brightness temperature analysis suggests that the system is in equipartition. We determined a turnover frequency of $\nu_m=(113\pm4)$GHz, a corresponding synchrotron self-absorbed magnetic field of $B_{SSA}=(2.9\pm1.6)$G, and an equipartition magnetic field of $B_{eq}=(5.2\pm0.6)$G. Three components are resolved with the highest fractional polarisation detected for this object ($m_\textrm{net}=(17.0\pm3.9)$%). The positions of the components are compatible with those seen in low-frequency VLBI observations since 2017-2018. We report a steeply negative slope of the spectrum at 228GHz. We used these findings to test models of jet formation, propagation, and Faraday rotation in 3C84. The findings of our investigation into different flow geometries and black hole spins support an advection-dominated accretion flow in a magnetically arrested state around a rapidly rotating supermassive black hole as a model of the jet-launching system in the core of 3C84. However, systematic uncertainties due to the limited (u,v)-coverage, however, cannot be ignored., Comment: 15 pages, 6 figures, published in A&A

We show that cube-connected cycles graphs $CCC_n$ are distance-balanced, and nicely distance-balanced if and only if $n$ is even., Comment: 10 pages, 3 figures

A nonlinear cross-diffusion--fluid system with chemical terms describing the dynamics of predator-prey living in a Newtonian fluid is proposed in this paper. The existence of a weak solution for the proposed macro-scale system is proved based on the Schauder fixed-point theory, a priori estimates, and compactness arguments. The proposed system is derived from the underlying description delivered by a kinetic-fluid theory model by a multiscale approach. Finally, we discuss the computational results for the proposed macro-scale system in two-dimensional space.