Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-term conservation tillage with reduced nitrogen fertilization intensity can improve winter wheat health via positive plant–microorganism feedback in the rhizosphere

Abstract Microbiome-based solutions are regarded key for sustainable agroecosystems. However, it is unclear how agricultural practices affect the rhizosphere microbiome, plant–microorganism interactions and crop performance under field conditions. Therefore, we installed root observation windows in a winter wheat field cultivated either under long-term mouldboard plough (MP) or cultivator tillage (CT). Each tillage practice was also compared at two nitrogen (N) fertilization intensities, intensive (recommended N-supply with pesticides/growth regulators) or extensive (reduced N-supply, no fungicides/growth regulators). Shoot biomass, root exudates and rhizosphere metabolites, physiological stress indicators, and gene expression were analyzed together with the rhizosphere microbiome (bacterial/archaeal 16S rRNA gene, fungal ITS amplicon, and shotgun metagenome sequencing) shortly before flowering. Compared to MP, the rhizosphere of CT winter wheat contained more primary and secondary metabolites, especially benzoxazinoid derivatives. Potential copiotrophic and plant-beneficial taxa (e.g. Bacillus, Devosia, and Trichoderma) as well as functional genes (e.g. siderophore production, trehalose synthase, and ACC deaminase) were enriched in the CT rhizosphere, suggesting that tillage affected belowground plant–microorganism interactions. In addition, physiological stress markers were suppressed in CT winter wheat compared to MP. In summary, tillage practice was a major driver of crop performance, root deposits, and rhizosphere microbiome interactions, while the N-fertilization intensity was also relevant, but less important.
Findings of Angiography and Carotid Vessel Wall Imaging Associated with Post-Procedural Clinical Events after Carotid Artery Stenting

Purpose Vessel wall imaging (VWI) for carotid plaque is better for detecting unstable carotid plaque such as intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), and thin/ruptured fibrous cap. However, the role of VWI before carotid artery stenting (CAS) is unclear. Thus, this study aimed to determine the findings of symptomatic carotid stenosis before CAS on angiography and carotid VWI and to evaluate the imaging findings associated with post-procedural clinical events after CAS. Materials and Methods This retrospective study included 173 consecutive patients who underwent carotid VWI, CAS, and post-procedural diffusion-weighted imaging (DWI) after CAS. Findings of unstable plaque on carotid VWI and unstable findings on angiography were analyzed. We also analyzed the incidence of post-procedural clinical events, any stroke, myocardial infarction (MI), and death within 30 days of CAS. Results Of 173 patients, 101 (58.4%) had initial ischemic symptoms and positive findings on DWI. Symptomatic patients were significantly higher in patients with IPH than in patients without IPH (62.4% vs. 45.8%, P=0.031). Degree of stenosis, thrombus of the stenotic lesion, flow delay of internal carotid artery, and flow arrest by filter thrombus had significantly higher prevalence in the symptomatic group. Twenty patients (11.6%) had post-procedural clinical events such as any stroke, clinical symptoms, and/or MI. Hyperlipidemia and intraluminal thrombus on angiography were identified as significant factors influencing post-procedural events after CAS. Conclusion An intraluminal thrombus on angiography was identified as a significant factor influencing post-procedural clinical events after CAS.
Serum Prohepcidin and Serum Ferritin The Diagnostic And Prognostic Markers in Primary Abdominal Solid Tumours of Paediatric Patients

MATERIAL amp METHODS- This study conducted in the Department of Paediatric Surgery and the Department of Immunology amp Molecular Medicine Sher-I-Kashmir Institute of Medical Sciences Srinagar, is a prospective study. The study comprised of twenty patients with primary abdominal solid tumours between the age group of 2 to 15 years and twenty normal controls between the age group of 2 to 15 years from Kashmir valley. Complete surgical excision of resectable tumours was done, while in unresectable tumour cases, only biopsy was taken amp tissue diagnosis was made by histopathology. In all the cases, venous blood samples were taken before and after surgery upon proper consent. We took venous blood samples in all the controls upon proper consent. From the samples, the serum after separation was stored at minus20o C until processed.nbsp We used only once thawed samples to measure serum prohepcidin and serum ferritin levels according to proper procedures and protocol prescribed in the kit. Results shows correlation of post-operative serum prohepcidin with hematologic and iron parameters and inflammatory markers in solid tumours. The positively correlation between pre-operative serum prohepcidin level with haemoglobin and the negative correlation between post-operative serum prohepcidin level with ESR and post-operative serum ferritin. Conclusion serum ferritin and Prohepcidin are important biochemical marker in intra-abdominal tumors and its estimation is helpful in diagnostic and prognostic estimation of inta abdominal tumors but the thing is normal range of prohepcidin estimation which is to be elucidated yet.
Anti-Context-Free languages

Context-free languages can be characterized in several ways. This article studies projective linearisations of languages of simple dependency trees, i.e., dependency trees in which a node can govern at most one node with a given syntactic function. We prove that the projective linearisations of local languages of simple dependency trees coincide with the context-free languages. Simple dependency trees suggest alternative dual notions of locality and projectivity, which permits defining a dual language for each context-free language. We call this new class of languages anti-context-free. These languages are related to some linguistic constructions exhibiting the so-called cross-serial dependencies that were historically important for the development of computational linguistics. We propose that this duality could be a relevant linguistic phenomenon.
Spectral properties of the strongly assortative stochastic block model and their application to hitting times of random walks

We analyze hitting times of simple random walk on realizations of the stochastic block model. We show that under some natural assumptions the average starting hitting time as well as the average target hitting time are asymptotically almost surely given by $N(1+o(1))$. We also show a central limit theorem for the average target hitting time. Our main techniques are a spectral decomposition of these hitting times, a spectral analysis of the adjacency matrix and the graph Laplacian, respectively, as well as a form of the Delta method.
Deep Fuzzy Framework for Emotion Recognition using EEG Signals and Emotion Representation in Type-2 Fuzzy VAD Space

Recently, the representation of emotions in the Valence, Arousal and Dominance (VAD) space has drawn enough attention. However, the complex nature of emotions and the subjective biases in self-reported values of VAD make the emotion model too specific to a particular experiment. This study aims to develop a generic model representing emotions using a fuzzy VAD space and improve emotion recognition by utilizing this representation. We partitioned the crisp VAD space into a fuzzy VAD space using low, medium and high type-2 fuzzy dimensions to represent emotions. A framework that integrates fuzzy VAD space with EEG data has been developed to recognize emotions. The EEG features were extracted using spatial and temporal feature vectors from time-frequency spectrograms, while the subject-reported values of VAD were also considered. The study was conducted on the DENS dataset, which includes a wide range of twenty-four emotions, along with EEG data and subjective ratings. The study was validated using various deep fuzzy framework models based on type-2 fuzzy representation, cuboid probabilistic lattice representation and unsupervised fuzzy emotion clusters. These models resulted in emotion recognition accuracy of 96.09\%, 95.75\% and 95.31\%, respectively, for the classes of 24 emotions. The study also included an ablation study, one with crisp VAD space and the other without VAD space. The result with crisp VAD space performed better, while the deep fuzzy framework outperformed both models. The model was extended to predict cross-subject cases of emotions, and the results with 78.37\% accuracy are promising, proving the generality of our model. The generic nature of the developed model, along with its successful cross-subject predictions, gives direction for real-world applications in the areas such as affective computing, human-computer interaction, and mental health monitoring.
Quantum Advantage Actor-Critic for Reinforcement Learning

Quantum computing offers efficient encapsulation of high-dimensional states. In this work, we propose a novel quantum reinforcement learning approach that combines the Advantage Actor-Critic algorithm with variational quantum circuits by substituting parts of the classical components. This approach addresses reinforcement learning's scalability concerns while maintaining high performance. We empirically test multiple quantum Advantage Actor-Critic configurations with the well known Cart Pole environment to evaluate our approach in control tasks with continuous state spaces. Our results indicate that the hybrid strategy of using either a quantum actor or quantum critic with classical post-processing yields a substantial performance increase compared to pure classical and pure quantum variants with similar parameter counts. They further reveal the limits of current quantum approaches due to the hardware constraints of noisy intermediate-scale quantum computers, suggesting further research to scale hybrid approaches for larger and more complex control tasks.
Comparison of Frequency-Fusion Mechanisms for Binaural Direction-of-Arrival Estimation for Multiple Speakers

To estimate the direction of arrival (DOA) of multiple speakers with methods that use prototype transfer functions, frequency-dependent spatial spectra (SPS) are usually constructed. To make the DOA estimation robust, SPS from different frequencies can be combined. According to how the SPS are combined, frequency fusion mechanisms are categorized into narrowband, broadband, or speaker-grouped, where the latter mechanism requires a speaker-wise grouping of frequencies. For a binaural hearing aid setup, in this paper we propose an interaural time difference (ITD)-based speaker-grouped frequency fusion mechanism. By exploiting the DOA dependence of ITDs, frequencies can be grouped according to a common ITD and be used for DOA estimation of the respective speaker. We apply the proposed ITD-based speaker-grouped frequency fusion mechanism for different DOA estimation methods, namely the multiple signal classification, steered response power and a recently published method based on relative transfer function (RTF) vectors. In our experiments, we compare DOA estimation with different fusion mechanisms. For all considered DOA estimation methods, the proposed ITD-based speaker-grouped frequency fusion mechanism results in a higher DOA estimation accuracy compared with the narrowband and broadband fusion mechanisms.
Curator: Efficient Indexing for Multi-Tenant Vector Databases

Vector databases have emerged as key enablers for bridging intelligent applications with unstructured data, providing generic search and management support for embedding vectors extracted from the raw unstructured data. As multiple data users can share the same database infrastructure, multi-tenancy support for vector databases is increasingly desirable. This hinges on an efficient filtered search operation, i.e., only querying the vectors accessible to a particular tenant. Multi-tenancy in vector databases is currently achieved by building either a single, shared index among all tenants, or a per-tenant index. The former optimizes for memory efficiency at the expense of search performance, while the latter does the opposite. Instead, this paper presents Curator, an in-memory vector index design tailored for multi-tenant queries that simultaneously achieves the two conflicting goals, low memory overhead and high performance for queries, vector insertion, and deletion. Curator indexes each tenant's vectors with a tenant-specific clustering tree and encodes these trees compactly as sub-trees of a shared clustering tree. Each tenant's clustering tree adapts dynamically to its unique vector distribution, while maintaining a low per-tenant memory footprint. Our evaluation, based on two widely used data sets, confirms that Curator delivers search performance on par with per-tenant indexing, while maintaining memory consumption at the same level as metadata filtering on a single, shared index.
PATSMA: Parameter Auto-tuning for Shared Memory Algorithms

Programs with high levels of complexity often face challenges in adjusting execution parameters, particularly when these parameters vary based on the execution context. These dynamic parameters significantly impact the program's performance, such as loop granularity, which can vary depending on factors like the execution environment, program input, or the choice of compiler. Given the expensive nature of testing each case individually, one viable solution is to automate parameter adjustments using optimization methods. This article introduces PATSMA, a parameter auto-tuning tool that leverages Coupled Simulated Annealing (CSA) and Nelder-Mead (NM) optimization methods to fine-tune existing parameters in an application. We demonstrate how auto-tuning can contribute to the real-time optimization of parallel algorithms designed for shared memory systems. PATSMA is a C++ library readily available under the MIT license.
Drell-Yan Bounds on Gapped Continuum Spectra

Theories with gapped continuum spectra have gotten some attention, either as pure 4D models like unparticles, or in 5D realizations as certain soft walls constructions. In this paper, we derive experimental bounds from Drell-Yan processes ($pp \to \ell^+\ell^-$, $pp \to \ell^\pm \nu$) in a particular scenario where the electroweak bosons propagate in an extra dimension that produces a propagator with a continuum spectrum, on top of the isolated corresponding Standard Model pole. Using current LHC data we put a lower bound on the gap of 4.2 TeV (expected), 6.2 TeV (observed, bins with $<10$ events combined) at 95\% CL, with some dependence in the observed limit on how low statistics bins are treated. We also study the limits for HL-LHC.
Reconstruction of Paleoceanography Significance in the Western Pacific and Atlantic Oceans during the Neogene Based on Calcareous Nannofossil Productivity and Size Variations, Related to the Global Tectonic Events

We investigated the calcareous nannofossils in the western Pacific and Bahama Bank of Caribbean Sea to reconstruct the paleoceanography and correlate with the global events. The absolute abundant of coccolith (number/g) are gradually increased from NN6 throughout NN19 Zone, while the relative abundance of Discoaster is decreased at Sites 782 in the western Pacific Ocean. The changes of the modal and maximum size of Reticulofenestra which are strongly reflected the collapse of sea surface stability, show four times in 8.8 Ma, 6.4 Ma, 5.4 Ma and 3.75 Ma at Site 782. On the basis of relationship between the changes of maximum sizes of Reticulofenestra and nutrient condition, these eutrophication events are clearly traceable to the western Pacific, Bahama Bank, northwestern Pacific Ocean and to the Indian Ocean. Two paleoceanographic events found in 8.8 Ma and 3.75 Ma are interpreted as change to high nutrient condition resulted in the intensification of Asian Monsoon and closure of Panama Isthmus.
H-CMRH: a novel inner product free hybrid Krylov method for large-scale inverse problems

This study investigates the iterative regularization properties of two Krylov methods for solving large-scale ill-posed problems: the changing minimal residual Hessenberg method (CMRH) and a novel hybrid variant called the hybrid changing minimal residual Hessenberg method (H-CMRH). Both methods share the advantages of avoiding inner products, making them efficient and highly parallelizable, and particularly suited for implementations that exploit randomization and mixed precision arithmetic. Theoretical results and extensive numerical experiments suggest that H-CMRH exhibits comparable performance to the established hybrid GMRES method in terms of stabilizing semiconvergence, but H-CMRH has does not require any inner products, and requires less work and storage per iteration.
Quantum many-body scars from unstable periodic orbits

Unstable periodic orbits (UPOs) play a key role in the theory of chaos, constituting the"skeleton"of classical chaotic systems and"scarring"the eigenstates of the corresponding quantum system. Recently, nonthermal many-body eigenstates embedded in an otherwise thermal spectrum have been identified as a many-body generalization of quantum scars. The latter, however, are not clearly associated to a chaotic phase space, and the connection between the single- and many-body notions of quantum scars remains therefore incomplete. Here, we find the first quantum many-body scars originating from UPOs of a chaotic phase space. Remarkably, these states verify the eigenstate thermalization hypothesis, and we thus refer to them as thermal quantum many-body scars. While they do not preclude thermalization, their spectral structure featuring approximately equispaced towers of states yields an anomalous oscillatory dynamics preceding thermalization for wavepackets initialized on an UPO. Remarkably, our model hosts both types of scars, thermal and nonthermal, and allows to study the crossover between the two. Our work illustrates the fundamental principle of classical-quantum correspondence in a many-body system, and its limitations.
Design-Based Estimation and Central Limit Theorems for Local Average Treatment Effects for RCTs

There is a growing literature on design-based methods to estimate average treatment effects for randomized controlled trials (RCTs) using the underpinnings of experiments. In this article, we build on these methods to consider design-based regression estimators for the local average treatment effect (LATE) estimand for RCTs with treatment noncompliance. We prove new finite-population central limit theorems for a range of designs, including blocked and clustered RCTs, allowing for baseline covariates to improve precision. We discuss consistent variance estimators based on model residuals and conduct simulations that show the estimators yield confidence interval coverage near nominal levels. We demonstrate the methods using data from a private school voucher RCT in New York City USA.
COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy

We address the challenge of learning safe and robust decision policies in presence of uncertainty in context of the real scientific problem of adaptive resource oversubscription to enhance resource efficiency while ensuring safety against resource congestion risk. Traditional supervised prediction or forecasting models are ineffective in learning adaptive policies whereas standard online optimization or reinforcement learning is difficult to deploy on real systems. Offline methods such as imitation learning (IL) are ideal since we can directly leverage historical resource usage telemetry. But, the underlying aleatoric uncertainty in such telemetry is a critical bottleneck. We solve this with our proposed novel chance-constrained imitation learning framework, which ensures implicit safety against uncertainty in a principled manner via a combination of stochastic (chance) constraints on resource congestion risk and ensemble value functions. This leads to substantial ($\approx 3-4\times$) improvement in resource efficiency and safety in many oversubscription scenarios, including resource management in cloud services.
UAV-assisted Emergency Integrated Sensing and Communication Networks: A CNN-based Rapid Deployment Approach

UAV-assisted integrated sensing and communication (ISAC) network is crucial for post-disaster emergency rescue. The speed of UAV deployment will directly impact rescue results. However, the ISAC UAV deployment in emergency scenarios is difficult to solve, which contradicts the rapid deployment. In this paper, we propose a two-stage deployment framework to achieve rapid ISAC UAV deployment in emergency scenarios, which consists of an offline stage and an online stage. Specifically, in the offline stage, we first formulate the ISAC UAV deployment problem and define the ISAC utility as the objective function, which integrates communication rate and localization accuracy. Secondly, we develop a dynamic particle swarm optimization (DPSO) algorithm to construct an optimized UAV deployment dataset. Finally, we train a convolutional neural network (CNN) model with this dataset, which replaces the time-consuming DPSO algorithm. In the online stage, the trained CNN model can be used to make quick decisions for the ISAC UAV deployment. The simulation results indicate that the trained CNN model achieves superior ISAC performance compared to the classic particle swarm optimization algorithm. Additionally, it significantly reduces the deployment time by more than 96%.
AI and Generative AI for Research Discovery and Summarization

AI and generative AI tools, including chatbots like ChatGPT that rely on large language models (LLMs), have burst onto the scene this year, creating incredible opportunities to increase work productivity and improve our lives. Statisticians and data scientists have begun experiencing the benefits from the availability of these tools in numerous ways, such as the generation of programming code from text prompts to analyze data or fit statistical models. One area that these tools can make a substantial impact is in research discovery and summarization. Standalone tools and plugins to chatbots are being developed that allow researchers to more quickly find relevant literature than pre-2023 search tools. Furthermore, generative AI tools have improved to the point where they can summarize and extract the key points from research articles in succinct language. Finally, chatbots based on highly parameterized LLMs can be used to simulate abductive reasoning, which provides researchers the ability to make connections among related technical topics, which can also be used for research discovery. We review the developments in AI and generative AI for research discovery and summarization, and propose directions where these types of tools are likely to head in the future that may be of interest to statistician and data scientists.
The Pulse of Fileless Cryptojacking Attacks: Malicious PowerShell Scripts

Fileless malware predominantly relies on PowerShell scripts, leveraging the native capabilities of Windows systems to execute stealthy attacks that leave no traces on the victim's system. The effectiveness of the fileless method lies in its ability to remain operational on victim endpoints through memory execution, even if the attacks are detected, and the original malicious scripts are removed. Threat actors have increasingly utilized this technique, particularly since 2017, to conduct cryptojacking attacks. With the emergence of new Remote Code Execution (RCE) vulnerabilities in ubiquitous libraries, widespread cryptocurrency mining attacks have become prevalent, often employing fileless techniques. This paper provides a comprehensive analysis of PowerShell scripts of fileless cryptojacking, dissecting the common malicious patterns based on the MITRE ATT&CK framework.