Let $K$ be a field of characteristic $p>0$, $A=K[[Y]]$ be a power series ring in one variable and $Q(A)$ be the field of fraction of $A$. Suppose that $R=A[X_1,\ldots,X_n]$ is a standard $\mathbb{N}^n$-graded polynomial ring over $A$, i.e., $\operatorname{deg} (A)=\underline{0}\in \mathbb{N}^n$ and $\operatorname{deg}(X_j)=e_j\in \mathbb{N}^n$. Assume that $M=\bigoplus_{\underline{u}\in \mathbb{Z}^n} M_{\underline{u}}$ is a $\mathbb{Z}^n$-graded $\mathcal{F}$-finite, $\mathcal{F}$-module over $R$. In this article we prove that, $\displaystyle M_{\underline{u}}\cong E(A/YA)^{a(\underline{u})}\oplus Q(A)^{b(\underline{u})}\oplus A^{c(\underline{u})}$ for some finite numbers $a(\underline{u}), b(\underline{u}), c(\underline{u})\geq 0$. Let for a subset of $U$ of $\mathcal{S}=\{1, \ldots, n\}$, define a block to be the set $\displaystyle\mathcal{B}(U)=\{\underline{u} \in \mathbb{Z}^n \mid u_i \geq 0 \mbox{ if } i \in U \mbox{ and } u_i \leq -1 \mbox{ if } i \notin U \}$. Note that $\bigcup_{U\subseteq \mathcal{S}}\mathcal{B}(U)=\mathbb{Z}^n$. We prove that the sets $\{a(\underline{u})\mid \underline{u}\in \mathbb{Z}^n\}$, $\{b(\underline{u})\mid \underline{u}\in \mathbb{Z}^n\}$ and $\{c(\underline{u})\mid \underline{u}\in \mathbb{Z}^n\}$ are constant on $\mathcal{B}(U)$ for each subset $U$ of $\{1,\ldots,n\}$. In particular, these results holds for composition of local cohomology modules of the form $ H^{i_1}_{I_1}(H^{i_2}_{I_2}(\dots H^{i_r}_{I_r}(R)\dots)$ where $I_1,\ldots,I_r$ are $\mathbb{N}^n$-graded ideals of $R$. This provides a positive characteristic analogue of the results proved in \cite{TS-23} by the authors in characteristic zero.
A \emph{multipacking} in an undirected graph $G=(V,E)$ is a set $M\subseteq V$ such that for every vertex $v\in V$ and for every integer $r\geq 1$, the ball of radius $ r $ around $ v $ contains at most $r$ vertices of $M$. The \textsc{Multipacking} problem asks whether a graph contains a multipacking of size at least $k$. For more than a decade, it remained open whether \textsc{Multipacking} is \textsc{NP-complete} or polynomial-time solvable, although it is known to be polynomial-time solvable for some classes (e.g., strongly chordal graphs and grids). Foucaud, Gras, Perez, and Sikora [\textit{Algorithmica} 2021] showed it is \textsc{NP-complete} for directed graphs and \textsc{W[1]-hard} when parameterized by the solution size. We resolve the open question by proving \textsc{Multipacking} is \textsc{NP-complete} for undirected graphs and \textsc{W[2]-hard} when parameterized by the solution size. Furthermore, we show it remains \textsc{NP-complete} and \textsc{W[2]-hard} even for chordal, bipartite, claw-free, regular, CONV, and chordal$\cap\frac{1}{2}$-hyperbolic graphs (a superclass of strongly chordal graphs), and we provide approximation algorithms for cactus, chordal, and $δ$-hyperbolic graphs. Moreover, we study the relationship between multipacking number and broadcast domination number for cactus, chordal, and $δ$-hyperbolic graphs. Further, we prove that for all $r\geq 2$, \textsc{$r$-Multipacking} is \textsc{NP-complete} even for planar bipartite graphs with bounded degree, and also for bounded-diameter chordal and bounded-diameter bipartite graphs. For geometric variants, in $\mathbb{R}^2$ a maximum $1$-multipacking can be computed in polynomial time, but computing a maximum $2$-multipacking is \textsc{NP-hard}, and we provide approximation and parameterized algorithms for the $2$-multipacking problem.
Sarah K. Nelson-Taylor, Jonathan Troost, Courtney Giannini
et al.
Rationale & Objective: Idiopathic nephrotic syndrome (INS) is viewed as a podocyte-specific disease. Recent reports indicate endothelial involvement, but its significance is unclear. Here, we investigated the relationship between the glomerular expression of selected genes relevant to endothelial health and clinical markers of disease severity. Study Design: A cross-sectional study. Setting & Participants: Patients with INS (n = 70 minimal change disease and n = 83 focal segmental glomerulosclerosis) from the Nephrotic Syndrome Study Network cohort study and 53 control participants. Validation studies, including animal and cell culture experiments, were performed. Exposure: Gene expression analysis from micro-dissected human glomeruli. The study is focused on 10 genes highly relevant for endothelial homeostasis and barrier integrity (nitric oxide synthase 3 [NOS3], endothelial cell adhesion molecule, and endothelial cell specific molecule 1 [ESM1]), endothelial glycocalyx remodeling (HPSE, HYAL1, MMP2, MMP9, and ADAMTS1), and endothelial activation (ICAM1 and CAV1). Outcomes: Kidney function, ultrastructural changes in podocytes and glomerular endothelium, interstitial fibrosis and tubular atrophy. Analytical Approach: One-way ANOVA and Tukey’s multiple comparisons test, Pearson Correlation and Cohen’s d statistics. Results: Transcriptomic analysis revealed that all genes of interest were highly expressed in glomeruli from INS patients compared with controls, except for ESM1 and MMP9, which were decreased. Expression of endothelial-specific genes correlated with those of glycocalyx injury and cell activation. HPSE, ADAMTS1, ICAM1, and CAV1 expression was inversely associated with kidney function, whereas ADAMTS1 showed a positive association with proteinuria. NOS3, HPSE, and ADAMTS1 were associated with podocyte foot process effacement, and ICAM1 with podocyte detachment. HPSE and MMP2 were associated with ultrastructural endothelial injury, whereas HPSE, MMP2, ICAM1, and CAV1 were associated with interstitial fibrosis and tubular atrophy. Several genes (ESM1, HPSE, HYAL1, MMP2, and ICAM1) were also dysregulated in experimental INS and validated in cultured glomerular endothelial cells (NOS3 and heparanase) following exposure to INS sera. Limitations: Observational study, selection bias, unmeasured confounders. Conclusions: INS involves dysregulation of genes relevant for endothelial health. Plain-Language Summary: Idiopathic Nephrotic Syndrome (INS) is a common condition associated with massive proteinuria and substantial morbidity. Most attention has focused on injury to the glomerular podocyte in driving the disease, but there is growing evidence that endothelial cells may also be involved. Here we analyzed mRNA levels of selected targets from micro-dissected human glomeruli, combined with validation cell culture studies. We identified an altered expression pattern of genes specific for endothelium or biologically relevant for endothelial health in INS. Some mRNA alterations correlated with clinical or histological features of disease severity. Thus, dysregulation of the vascular lining of glomerular capillaries (endothelial cells) occurs in INS and suggests that INS involves injury to other cells besides the podocyte.
Md Mohayeminul Islam, Ajay Kumar Jha, May Mahmoud
et al.
Library migration is the process of replacing one library with another library that provides similar functionality. Manual library migration is time consuming and error prone, as it requires developers to understand the APIs of both libraries, map them, and perform the necessary code transformations. Large Language Models (LLMs) are shown to be effective at generating and transforming code as well as finding similar code, which are necessary upstream tasks for library migration. Such capabilities suggest that LLMs may be suitable for library migration. Accordingly, this paper investigates the effectiveness of LLMs for migration between Python libraries. We evaluate three LLMs, Llama 3.1, GPT-4o mini, and GPT-4o on PyMigBench, where we migrate 321 real-world library migrations that include 2,989 migration-related code changes. To measure correctness, we (1) compare the LLM's migrated code with the developers' migrated code in the benchmark and (2) run the unit tests available in the client repositories. We find that LLama 3.1, GPT-4o mini, and GPT-4o correctly migrate 89%, 89%, and 94% of the migration-related code changes, respectively. We also find that 36%, 52% and 64% of the LLama 3.1, GPT-4o mini, and GPT-4o migrations pass the same tests that passed in the developer's migration. To ensure the LLMs are not reciting the migrations, we also evaluate them on 10 new repositories where the migration never happened. Overall, our results suggest that LLMs can be effective in migrating code between libraries, but we also identify some open challenges.
Bimanual robot manipulators can achieve impressive dexterity, but typically rely on two full six- or seven- degree-of-freedom arms so that paired grippers can coordinate effectively. This traditional framework increases system complexity while only exploiting a fraction of the overall workspace for dexterous interaction. We introduce the MiniBEE (Miniature Bimanual End-effector), a compact system in which two reduced-mobility arms (3+ DOF each) are coupled into a kinematic chain that preserves full relative positioning between grippers. To guide our design, we formulate a kinematic dexterity metric that enlarges the dexterous workspace while keeping the mechanism lightweight and wearable. The resulting system supports two complementary modes: (i) wearable kinesthetic data collection with self-tracked gripper poses, and (ii) deployment on a standard robot arm, extending dexterity across its entire workspace. We present kinematic analysis and design optimization methods for maximizing dexterous range, and demonstrate an end-to-end pipeline in which wearable demonstrations train imitation learning policies that perform robust, real-world bimanual manipulation.
Zinnat Ara Moni, Zahid Hasan, Md. Shaheen Alam
et al.
ABSTRACT Background Cancer is the second leading cause of human mortality worldwide. Extracellular vesicles (EVs) from liquid biopsy samples are used in early cancer detection, characterization, and surveillance. Exosomes are a subset of EVs produced by all cells and present in all body fluids. They play an important role in the development of cancer because they are active transporters capable of carrying the contents of any type of cell. The objective of this review was to provide a brief overview of the clinical implication of exosomes or exosomal components in cancer diagnosis and prognosis. Methods An extensive review of the current literature of exosomes and their components in cancer diagnosis and prognosis were carried out in the current study. Results Tumor cells release exosomes that contribute to the formation of the pre‐metastatic microenvironment, angiogenesis, invasion, and treatment resistance. On the contrary, tumor cells release more exosomes than normal cells, and these tumor‐specific exosomes can carry the genomic and proteomic signature contents of the tumor cells, which can act as tools for the diagnosis and prognosis of patients with cancers. Conclusion This information may help clinicians to improve the management of cancer patients in clinical settings in the future.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
This paper aims to examine the determinants that influence bank users’ acceptance for Islamic financial technology (FinTech) services by extending the technology acceptance model (TAM) in the Malaysian context.,The survey was conducted using convenience sampling. Moreover, 205 responses were gathered from users of the Islamic bank. On the same note, the literature on determinants of Islamic FinTech acceptance and TAM was reviewed as well in a bid to contribute to the factors that are instrumental in determining the acceptance of FinTech services.,Findings of the study reveal that Islamic FinTech’s services acceptance is determined by perceived ease of use, perceived usefulness and also by another variable, which is consumer innovativeness (CI). On the contrary other factors, self-efficacy and subjective norms are found not to be influential in determining Islamic FinTech’s acceptance by Islamic banking users.,TAM is extended in the context of Islamic FinTech. A new variable, namely, CI is tested using TAM. CI is yet to be tested, therefore, this paper will be a useful reference for the policymakers, academicians and future researchers.
Purpose: The paper aims to review the academic research work done in the area of Islamic financial technology. The Islamic FinTech area has been classified into three broad categories of the Islamic FinTech, Islamic Financial technology opportunities and challenges, Cryptocurrency/Blockchain sharia compliance and law/regulation. Finally, the study identifies and highlights the opportunities and challenges that Islamic Financial institutions can learn from the conventional FinTech organization across the world. Approach/Methodology/Design: The study collected 112 research studies (50 from Social Science Research Network (SSRN), 30 from Research gate, 12 from Google Scholar and 20 from other sources) in the area of Islamic Financial Technology. The study presents the systematic review of the above studies. Findings: The study classifies the Islamic FinTech into three broad categories namely, Islamic FinTech opportunities and challenges, Cryptocurrency/Blockchain sharia compliance and law/regulation. The study identifies that the sharia compliance related to the cryptocurrency/Blockchain is the biggest challenge which Islamic FinTech organizations are facing. During our review we also find that Islamic FinTech organizations are to be considered as partners by the Islamic Financial Institutions (IFI’s) than the competitors. If Islamic Financial institutions want to increase efficiency, transparency and customer satisfaction they have to adopt FinTech and become partners with the FinTech companies. Practical Implications: The study will contribute positively to the understanding of Islamic Fintech for the academia, industry, regulators, investors and other FinTech users. Originality/Value: The study believes to contribute positively to understanding of Fintech based technology like cryptocurrency/Blockchain from sharia perspective.
Y. T. Muryanto, Dona Budi Kharisma, Anjar Sri Ciptorukmi Nugraheni
Purpose This paper aims to explore the prospects and the challenges of Islamic fintech in Indonesia. This study also proposes a comprehensive legal framework to encourage and accelerate the growth of the Islamic economy. Design/methodology/approach This study is the result of legal research with a statute approach and conceptual approach. The types of data used are legal materials consisting of primary legal materials and secondary legal materials. The technique of collecting legal materials is done by using library research techniques. The legal materials were analyzed using the legal norm method. Findings Indonesia is a country with the largest Muslim population in the world. However, the market size of Indonesia’s Islamic fintech is still below Saudi Arabia, Iran, United Arab Emirates (UAE) and Malaysia. Saudi Arabia’s Islamic fintech is the biggest market in the world, with $17.9bn worth of transactions in 2020 while Iran is at $9.2bn, UAE $3.7bn, Malaysia $3.0bn and Indonesia $2.9bn. This condition was due to various challenges in the Islamic fintech industry in Indonesia, including inadequate regulations; complicated permit procedures; misuse of fintech for financing terrorism; rampant occurrence of illegal fintech businesses; and consumer disputes in the fintech sector. These challenges require the construction of a comprehensive legal framework through the formation of an Act on Fintech. Research limitations/implications The focus of this research was limited to the problems occurring in the Islamic fintech sector in Indonesia as a country with the largest Muslim population in the world. Practical implications The results of this research can be used as recommendations for the formulation of comprehensive policies for the growth and development of Islamic fintech. Social implications Islamic fintech requires a comprehensive legal framework that functions to encourage the development of the Islamic fintech industry, digital economy growth and legal mitigation of various legal risks and misuse of fintech for financial crime and financing terrorism. Originality/value This paper proposes an original idea of creating a legal framework in a form of the Islamic Fintech Act. The Act should cover such legal substances as follows: Islamic compliance; an integrated one-stop permit procedure; division of authority, coordination and synergy among authorities; prevention and resolution of digital financial system crisis; criminal sanctions; and consumer dispute resolution mechanisms and alternative institution for fintech consumer dispute resolution.
Deep Neural Networks (DNNs) based semantic segmentation of the robotic instruments and tissues can enhance the precision of surgical activities in robot-assisted surgery. However, in biological learning, DNNs cannot learn incremental tasks over time and exhibit catastrophic forgetting, which refers to the sharp decline in performance on previously learned tasks after learning a new one. Specifically, when data scarcity is the issue, the model shows a rapid drop in performance on previously learned instruments after learning new data with new instruments. The problem becomes worse when it limits releasing the dataset of the old instruments for the old model due to privacy concerns and the unavailability of the data for the new or updated version of the instruments for the continual learning model. For this purpose, we develop a privacy-preserving synthetic continual semantic segmentation framework by blending and harmonizing (i) open-source old instruments foreground to the synthesized background without revealing real patient data in public and (ii) new instruments foreground to extensively augmented real background. To boost the balanced logit distillation from the old model to the continual learning model, we design overlapping class-aware temperature normalization (CAT) by controlling model learning utility. We also introduce multi-scale shifted-feature distillation (SD) to maintain long and short-range spatial relationships among the semantic objects where conventional short-range spatial features with limited information reduce the power of feature distillation. We demonstrate the effectiveness of our framework on the EndoVis 2017 and 2018 instrument segmentation dataset with a generalized continual learning setting. Code is available at~\url{https://github.com/XuMengyaAmy/Synthetic_CAT_SD}.
Purpose: Depth estimation in robotic surgery is vital in 3D reconstruction, surgical navigation and augmented reality visualization. Although the foundation model exhibits outstanding performance in many vision tasks, including depth estimation (e.g., DINOv2), recent works observed its limitations in medical and surgical domain-specific applications. This work presents a low-ranked adaptation (LoRA) of the foundation model for surgical depth estimation. Methods: We design a foundation model-based depth estimation method, referred to as Surgical-DINO, a low-rank adaptation of the DINOv2 for depth estimation in endoscopic surgery. We build LoRA layers and integrate them into DINO to adapt with surgery-specific domain knowledge instead of conventional fine-tuning. During training, we freeze the DINO image encoder, which shows excellent visual representation capacity, and only optimize the LoRA layers and depth decoder to integrate features from the surgical scene. Results: Our model is extensively validated on a MICCAI challenge dataset of SCARED, which is collected from da Vinci Xi endoscope surgery. We empirically show that Surgical-DINO significantly outperforms all the state-of-the-art models in endoscopic depth estimation tasks. The analysis with ablation studies has shown evidence of the remarkable effect of our LoRA layers and adaptation. Conclusion: Surgical-DINO shed some light on the successful adaptation of the foundation models into the surgical domain for depth estimation. There is clear evidence in the results that zero-shot prediction on pre-trained weights in computer vision datasets or naive fine-tuning is not sufficient to use the foundation model in the surgical domain directly. Code is available at https://github.com/BeileiCui/SurgicalDINO.
Riadul Islam, Sri Ranga Sai Krishna Tummala, Joey Mulé
et al.
Smart focal-plane and in-chip image processing has emerged as a crucial technology for vision-enabled embedded systems with energy efficiency and privacy. However, the lack of special datasets providing examples of the data that these neuromorphic sensors compute to convey visual information has hindered the adoption of these promising technologies. Neuromorphic imager variants, including event-based sensors, produce various representations such as streams of pixel addresses representing time and locations of intensity changes in the focal plane, temporal-difference data, data sifted/thresholded by temporal differences, image data after applying spatial transformations, optical flow data, and/or statistical representations. To address the critical barrier to entry, we provide an annotated, temporal-threshold-based vision dataset specifically designed for face detection tasks derived from the same videos used for Aff-Wild2. By offering multiple threshold levels (e.g., 4, 8, 12, and 16), this dataset allows for comprehensive evaluation and optimization of state-of-the-art neural architectures under varying conditions and settings compared to traditional methods. The accompanying tool flow for generating event data from raw videos further enhances accessibility and usability. We anticipate that this resource will significantly support the development of robust vision systems based on smart sensors that can process based on temporal-difference thresholds, enabling more accurate and efficient object detection and localization and ultimately promoting the broader adoption of low-power, neuromorphic imaging technologies. To support further research, we publicly released the dataset at \url{https://dx.doi.org/10.21227/bw2e-dj78}.
M. F. Islam, Kushantha P. K. Withanage, C. M. Canali
et al.
Frustrated triangular molecular magnets (MMs) with anti-ferromagnetic ground states (GS) are an important class of magnetic systems with potential applications in quantum information processing. The two-fold degenerate GS of these molecules, characterized by spin chirality, can be utilized to encode qubits for quantum computing. Furthermore, because of the lack of inversion symmetry in these molecules, an electric field couples directly states of opposite chirality, allowing a very efficient and fast control of the qubits. In this work we present a theoretical method to calculate the spin-electric coupling for triangular MMs with effective {\it local} spins $s$ larger than 1/2, which is amenable to a first-principles implementation based on density functional theory (DFT). In contrast to MMs where the net magnetization at the magnetic atoms is $μ_{\rm B}/2$ ($μ_{\rm B} $ is the Bohr magneton), the DFT treatment of frustrated triangular MMs with larger local magnetizations requires a fully non-collinear approach, which we have implemented in the NRLMOL DFT code. As an example, we have used these methods to evaluate the spin-electric coupling for a spin $s = 5/2$ $\{\mathrm{Fe_3}\}$ triangular MM, where this effect has been observed experimentally for the first time quite recently. Our theoretical and computational methods will help elucidate and further guide ongoing experimental work in the field of quantum molecular spintronics.
Vehicular Ad Hoc Networks (VANETs) play a crucial role in enhancing road safety and traffic efficiency by enabling communication between vehicles (V2V) and between vehicles and infrastructure (V2I). Robust trust management is necessary to ensure the reliability of information in decentralized systems. This paper presents the notion of a ``Trust Field" in VANETs, conceptualized as the behavior of the nodes that represents trust levels evolving in both spatial and temporal dimensions. Using the LogitTrust model, we provide empirical evidence of how trust fields in vehicular networks change over time in different scenarios, including when malicious nodes are present. The results of our study demonstrate that the trust domain can adjust to fluctuations in network conditions, thereby offering a comprehensive metric for assessing the reliability of nodes. This innovative method improves the dependability of VANET applications by efficiently detecting and mitigating malicious actions.
Saahil Islam, Venkatesh N. Murthy, Dominik Neumann
et al.
An accurate detection and tracking of devices such as guiding catheters in live X-ray image acquisitions is an essential prerequisite for endovascular cardiac interventions. This information is leveraged for procedural guidance, e.g., directing stent placements. To ensure procedural safety and efficacy, there is a need for high robustness no failures during tracking. To achieve that, one needs to efficiently tackle challenges, such as: device obscuration by contrast agent or other external devices or wires, changes in field-of-view or acquisition angle, as well as the continuous movement due to cardiac and respiratory motion. To overcome the aforementioned challenges, we propose a novel approach to learn spatio-temporal features from a very large data cohort of over 16 million interventional X-ray frames using self-supervision for image sequence data. Our approach is based on a masked image modeling technique that leverages frame interpolation based reconstruction to learn fine inter-frame temporal correspondences. The features encoded in the resulting model are fine-tuned downstream. Our approach achieves state-of-the-art performance and in particular robustness compared to ultra optimized reference solutions (that use multi-stage feature fusion, multi-task and flow regularization). The experiments show that our method achieves 66.31% reduction in maximum tracking error against reference solutions (23.20% when flow regularization is used); achieving a success score of 97.95% at a 3x faster inference speed of 42 frames-per-second (on GPU). The results encourage the use of our approach in various other tasks within interventional image analytics that require effective understanding of spatio-temporal semantics.