Miracle-dz/UniFAR-FTUs huggingface dataset:
Retail Risk Assessment and Lethality of Listeria monocytogenes and E . coli O 157 in Naturally Fermented Sauerkraut

Introduction Recent interest in traditional foods and probiotics has brought fermented sauerkraut back in vogue among both consumers and retail food service operators. As a fermented food, sauerkraut is designated as a special process under the Food and Drug Administration model Food Code (U.S. Department of Health and Human Services [HHS], 2017). Specifically, fermentation is considered a special process where a food additive (e.g., bacterial culture) is used to make a potentially hazardous (i.e., time/temperature control for safety [TCS]) food into a nonpotentially hazardous (non-TCS) food (HHS, 2017). As a Food Code special process, fermentation requires a documented and implemented hazard analysis critical control point (HACCP) food safety plan (HHS, 2017).
Practical and Fast Algorithm for Character Segmentation of License Plate

Aiming at the character segmentation of license plate recognition system,an algorithm based on geometrical characteristics of plate characters is presented.Firstly,the mathematical morphology is applied to the binary image of the plate to remove some useless information and to make greater distances between characters and plate frames and between characters.The algorithm is convenient for lining out the vertical boundaries.A new and fast method for measuring the gradient of plates is used secondly.The experimental results show that the principle of the new algorithm is simple,the segmenting process is fast,and the quality of segmented characters is good for character recognition.
Object Gathering with a Tethered Robot Duo

We devise a cooperative planning framework to generate optimal trajectories for a tethered robot duo, who is tasked to gather scattered objects spread in a large area using a flexible net. Specifically, the proposed planning framework first produces a set of dense waypoints for each robot, serving as the initialization for optimization. Next, we formulate an iterative optimization scheme to generate smooth and collision-free trajectories while ensuring cooperation within the robot duo to efficiently gather objects and properly avoid obstacles. We validate the generated trajectories in simulation and implement them in physical robots using Model Reference Adaptive Controller (MRAC) to handle unknown dynamics of carried payloads. In a series of studies, we find that: (i) a U-shape cost function is effective in planning cooperative robot duo, and (ii) the task efficiency is not always proportional to the tethered net's length. Given an environment configuration, our framework can gauge the optimal net length. To our best knowledge, ours is the first that provides such estimation for tethered robot duo.
Stochastic Gradient Descent for Barycenters in Wasserstein Space

We present and study a novel algorithm for the computation of 2-Wasserstein population barycenters of absolutely continuous probability measures on Euclidean space. The proposed method can be seen as a stochastic gradient descent procedure in the 2-Wasserstein space, as well as a manifestation of a Law of Large Numbers therein. The algorithm aims at finding a Karcher mean or critical point in this setting, and can be implemented ``online", sequentially using i.i. d. random measures sampled from the population law. We provide natural sufficient conditions for this algorithm to a.s. converge in the Wasserstein space towards the population barycenter, and we introduce a novel, general condition which ensures uniqueness of Karcher means and moreover allows us to obtain explicit, parametric convergence rates for the expected optimality gap. We furthermore study the mini-batch version of this algorithm, and discuss examples of families of population laws to which our method and results can be applied. This work expands and deepens ideas and results introduced in an early version of \cite{backhoff2018bayesian}, in which a statistical application (and numerical implementation) of this method is developed in the context of Bayesian learning.
Implementation of a digitally encoded multigrid algorithm on a quantum computer

Multigrid has become a popular method for solving some of the most challenging real-world computational problems, such as computational fluid dynamics (CFD). The reason for this is the very good scaling properties of multigrid, which is often linear, or close to linear, with respect to problem size. In this paper a method is presented, which can be used to implement a quantum version of the multigrid algorithm. The method relies upon a quantum state that is maintained in a equal superposition throughout the calculation, and where information is encoded digitally in the qubits in a way more similar to a classical computer. This differs from many existing quantum algorithms where information is encoded in the amplitudes of the quantum states in the superposition. At the core of the method is an algorithm for sharing information between the states in the superposition. An exponential speedup is provided for classes of problems where the solution vector can be compressed efficiently, and where a quantum compiler can reduce the quantum circuit depth efficiently.
ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation

Automatic segmentation of lung lesions associated with COVID-19 in CT images requires large amount of annotated volumes. Annotations mandate expert knowledge and are time-intensive to obtain through fully manual segmentation methods. Additionally, lung lesions have large inter-patient variations, with some pathologies having similar visual appearance as healthy lung tissues. This poses a challenge when applying existing semi-automatic interactive segmentation techniques for data labelling. To address these challenges, we propose an efficient convolutional neural networks (CNNs) that can be learned online while the annotator provides scribble-based interaction. To accelerate learning from only the samples labelled through user-interactions, a patch-based approach is used for training the network. Moreover, we use weighted cross-entropy loss to address the class imbalance that may result from user-interactions. During online inference, the learned network is applied to the whole input volume using a fully convolutional approach. We compare our proposed method with state-of-the-art and show that it outperforms existing methods on the task of annotating lung lesions associated with COVID-19, achieving 16% higher Dice score while reducing execution time by 3$\times$ and requiring 9000 lesser scribbles-based labelled voxels. Due to the online learning aspect, our approach adapts quickly to user input, resulting in high quality segmentation labels. Source code will be made available upon acceptance.
Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning

Glioblastoma Multiforme (GBM) is a malignant brain cancer forming around 48% of all brain and Central Nervous System (CNS) cancers. It is estimated that annually over 13,000 deaths occur in the US due to GBM, making it crucial to have early diagnosis systems that can lead to predictable and effective treatment. The most common treatment after GBM diagnosis is chemotherapy, which works by sending rapidly dividing cells to apoptosis. However, this form of treatment is not effective when the MGMT promoter sequence is unmethy-lated, and instead leads to severe side effects decreasing patient survivability. Therefore, it is important to be able to identify the MGMT promoter methylation status before deciding on the treatment plan. In this research, we aim to detect the methylation status through non-invasive magnetic resonance imaging (MRI) based machine learning (ML) models. This is accomplished using the Brain Tumor Segmentation (BraTS) 2021 dataset, which was recently used for an international Kaggle competition. We built a novel ML model termed as the Intermediate State Generator which was used to normalize the slice thicknesses of all MRI scans. With further improvements, our model was able to achieve performance significantly (p < 0.05) better than the best performing Kaggle model with a 5.5% increase in average cross-validation accuracy. This improvement could potentially lead to a more informed choice of chemotherapy as a treatment option, prolonging lives of thousands of patients with GBM each year.
Matching-based Service Offloading for Compute-less Driven IoT Networks

With the advent of the Internet of Things (IoT) and 5G networks, edge computing is offering new opportunities for business model and use cases innovations. Service providers can now virtualize the cloud beyond the data center to meet the latency, data sovereignty, reliability, and interoperability requirements. Yet, many new applications (e.g., augmented reality, virtual reality, artificial intelligence) are computation-intensive and delay-sensitivity. These applications are invoked heavily with similar inputs that could lead to the same output. Compute-less networks aim to implement a network with a minimum amount of computation and communication. This can be realized by offloading prevalent services to the edge and thus minimizing communication in the core network and eliminating redundant computations using the computation reuse concept. In this paper, we present matching-based services offloading schemes for compute-less IoT networks. We adopt the matching theory to match service offloading to the appropriate edge server(s). Specifically, we design, WHISTLE, a vertical many-to-many offloading scheme that aims to offload the most invoked and highly reusable services to the appropriate edge servers. We further extend WHISTLE to provide horizontal one-to-many computation reuse sharing among edge servers which leads to bouncing less computation back to the cloud. We evaluate the efficiency and effectiveness of WHISTLE with a real-world dataset. The obtained findings show that WHISTLE is able to accelerate the tasks completion time by 20%, reduce the computation up to 77%, and decrease the communication up to 71%. Theoretical analyses also prove the stability of the designed schemes.
Enlightening Flash Storage to Stream Writes by Objects

For a write request, today flash storage cannot distinguish the logical object it comes from. In such object-oblivious flash devices, concurrent writes from different objects are simply packed in their arrival order to flash memory blocks; hence objects with different lifetimes are multiplexed onto the same flash blocks. This multiplexing incurs write amplification, worsening the performance. Tackling the multiplexing problem, we propose a novel interface for flash storage, FlashAlloc. It is used to pass the logical address ranges of logical objects to the flash storage and thus enlighten the storage to stream writes by objects. The object-aware flash storage can de-multiplex writes from different objects with distinct deathtimes into per-object dedicated flash blocks. Given that popular data stores separate writes using objects (e.g., SSTables in RocksDB), we can achieve, unlike the existing solutions, transparent write streaming just by calling FlashAlloc upon object creation. Our experimental results using an open-source SSD prototype demonstrate that FlashAlloc can reduce write amplification factor (WAF) in RocksDB, F2FS, and MySQL by 1.5, 2.5, and 0.3, respectively and thus improve throughput by 2x, 1.8x, and 1.2x, respectively. In particular, FlashAlloc will mitigate the interference among multitenants. When RocksDB and MySQL were run together on the same SSD, FlashAlloc decreased WAF from 4.2 to 2.5 and doubled their throughputs.
Path Transitions Tell More: Optimizing Fuzzing Schedules via Runtime Program States

Coverage-guided Greybox Fuzzing (CGF) is one of the most successful and widely-used techniques for bug hunting. Two major approaches are adopted to optimize CGF: (i) to reduce search space of inputs by inferring relationships between input bytes and path constraints; (ii) to formulate fuzzing processes (e.g., path transitions) and build up probability distributions to optimize power schedules, i.e., the number of inputs generated per seed. However, the former is subjective to the inference results which may include extra bytes for a path constraint, thereby limiting the efficiency of path constraints resolution, code coverage discovery, and bugs exposure; the latter formalization, concentrating on power schedules for seeds alone, is inattentive to the schedule for bytes in a seed. In this paper, we propose a lightweight fuzzing approach, Truzz, to optimize existing Coverage-guided Greybox Fuzzers (CGFs). To address two aforementioned challenges, Truzz identifies the bytes related to the validation checks (i.e., the checks guarding error-handling code), and protects those bytes from being frequently mutated, making most generated inputs examine the functionalities of programs, in lieu of being rejected by validation checks. The byte-wise relationship determination mitigates the problem of loading extra bytes when fuzzers infer the byte-constraint relation. Furthermore, the proposed path transition within Truzz can efficiently prioritize the seed as the new path, harvesting many new edges, and the new path likely belongs to a code region with many undiscovered code lines. To evaluate our approach, we implemented 6 state-of-the-art fuzzers, AFL, AFLFast, NEUZZ, MOPT, FuzzFactory and GreyOne, in Truzz. The experimental results show that on average, Truzz can generate 16.14% more inputs flowing into functional code, in addition to 24.75% more new edges than the vanilla fuzzers. Finally, our approach exposes 13 bugs in 8 target programs, and 6 of them have not been identified by the vanilla fuzzers.
Epidemiology and genomic analysis of Shiga toxin-producing Escherichia coli clonal complex 165 in the UK

Introduction Shiga toxin-producing Escherichia coli (STEC) is a zoonotic, foodborne gastrointestinal pathogen that has the potential to cause severe clinical outcomes, including haemolytic uraemic syndrome (HUS). STEC-HUS is the leading cause of renal failure in children and can be fatal. Over the last decade, STEC clonal complex 165 (CC165) has emerged as a cause of STEC-HUS. Gap statement There is a need to understand the pathogenicity and prevalence of this emerging STEC clonal complex in the UK, to facilitate early diagnosis, improve clinical management, and prevent and control outbreaks. Aim The aim of this study was to characterize CC165 through identification of virulence factors (VFs) and antimicrobial resistance (AMR) determinants in the genome and to integrate the genome data with the available epidemiological data to better understand the incidence and pathogenicity of this clonal complex in the UK. Methodology All isolates belonging to CC165 in the archives at the UK public health agencies were sequenced and serotyped, and the virulence gene and AMR profiles were derived from the genome using PHE bioinformatics pipelines and the Centre for Genomic Epidemiology virulence database. Results There were 48 CC165 isolates, of which 43 were STEC, four were enteropathogenic E. coli (EPEC) and one E. coli . STEC serotypes were predominately O80:H2 (n=28), and other serotypes included O45:H2 (n=9), O55:H9 (n=4), O132:H2 (n=1) and O180:H2 (n=1). All but one STEC isolate had Shiga toxin (stx) subtype stx2a or stx2d and 47/48 isolates had the eae gene encoding intimin involved in the intimate attachment of the bacteria to the human gut mucosa. We detected extra-intestinal virulence genes including those associated with iron acquisition (iro) and serum resistance (iss), indicating that this pathogen has the potential to translocate to extra-intestinal sites. Unlike other STEC clonal complexes, a high proportion of isolates (93%, 40/43) were multidrug-resistant, including resistance to aminoglycosides, beta-lactams, chloramphenicol, sulphonamides, tetracyclines and trimethoprim. Conclusion The clinical significance of this clonal complex should not be underestimated. Exhibiting high levels of AMR and a combination of STEC and extra-intestinal pathogenic E. coli (ExPEC) virulence profiles, this clonal complex is an emerging threat to public health.
Detecting Ransomware Execution in a Timely Manner

Ransomware has been an ongoing issue since the early 1990s. In recent times ransomware has spread from traditional computational resources to cyber-physical systems and industrial controls. We devised a series of experiments in which virtual instances are infected with ransomware. We instrumented the instances and collected resource utilization data across a variety of metrics (CPU, Memory, Disk Utility). We design a change point detection and learning method for identifying ransomware execution. Finally we evaluate and demonstrate its ability to detect ransomware efficiently in a timely manner when trained on a minimal set of samples. Our results represent a step forward for defense, and we conclude with further remarks for the path forward.
Predicting Alzheimer's Disease Using 3DMgNet

Alzheimer’s disease (AD) is an irreversible neurode generative disease of the brain. The disease may causes memory loss, difficulty communicating and disorientation. For the diagnosis of Alzheimer's disease, a series of scales are often needed to evaluate the diagnosis clinically, which not only increases the workload of doctors, but also makes the results of diagnosis highly subjective. Therefore, for Alzheimer's disease, imaging means to find early diagnostic markers has become a top priority. In this paper, we propose a novel 3DMgNet architecture which is a unified framework of multigrid and convolutional neural network to diagnose Alzheimer’s disease (AD). The model is trained using an open dataset (ADNI dataset) and then test with a smaller dataset of ours. Finally, the model achieved 92.133% accuracy for AD vs NC classification and significantly reduced the model parameters.
How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets

A central question in natural language understanding (NLU) research is whether high performance demonstrates the models’ strong reasoning capabilities. We present an extensive series of controlled experiments where pre-trained language models are exposed to data that have undergone specific corruption transformations. These involve removing instances of specific word classes and often lead to non-sensical sentences. Our results show that performance remains high on most GLUE tasks when the models are fine-tuned or tested on corrupted data, suggesting that they leverage other cues for prediction even in non-sensical contexts. Our proposed data transformations can be used to assess the extent to which a specific dataset constitutes a proper testbed for evaluating models’ language understanding capabilities.
A finite element method to a periodic steady-state problem for an electromagnetic field system using the space-time finite element exterior calculus

This paper proposes a finite element method for solving the periodic steady-state problem for the scalar-valued and vector-valued Poisson equations, a simple reduction model of the Maxwell equations under the Coulomb gauge. Introducing a new potential variable, we reformulate two systems composed of the scalar-valued and vector-valued Poisson problems to a single Hodge-Laplace problem for the 1-form in $\mathbb{R}^4$ using the standard de Rham complex. Consequently, we can directly apply the Finite Element Exterior Calculus (FEEC) theory in $\mathbb{R}^4$ to deduce the well-posedness, stability, and convergence. Numerical examples using the cubical element are reported to validate the theoretical results.
Joint Transmit and Reflective Beamformer Design for Secure Estimation in IRS-Aided WSNs

Wireless sensor networks (WSNs) are vulnerable to eavesdropping as the sensor nodes (SNs) communicate over an open radio channel. Intelligent reflecting surface (IRS) technology can be leveraged for physical layer security in WSNs. In this letter, we propose a joint transmit and reflective beamformer (JTRB) design for secure parameter estimation at the fusion center (FC) in the presence of an eavesdropper (ED) in a WSN. We develop a semidefinite relaxation (SDR)-based iterative algorithm, which alternately yields the transmit beamformer at each SN and the corresponding reflection phases at the IRS, to achieve the minimum mean-squared error (MSE) parameter estimate at the FC, subject to transmit power and ED signal-to-noise ratio constraints. Our simulation results demonstrate robust MSE and security performance of the proposed IRS-based JTRB technique.
Performance comparisons between OSPF and EIG RP in tactical IP networks

Tactical IP networks have special requirem e nts for a routing protocol. The most important ones are fast convergence, short end-to-end delay and the ability to recover from emergencies quickly. With OPN ET, this paper compared the performances of EIGRP and OSPF in the above aspects with the result that the convergence of EIGRP is about 2 times faster than OSPF and pea k traffic in the former is about 50% of the latter. The end-to-end delay with EIGRP is about 20% less than with OSPF. The response time is less either in EIGR P. In summery, EIGRP is more suitable for tactical IP networks than OSPF. Some suggestions were put forward to improve the performance of OSPF.
Heme-Oxygenase-1 Attenuates Oxidative Functions of Antigen Presenting Cells and Promotes Regulatory T Cell Differentiation during Fasciola hepatica Infection

Fasciola hepatica is a fluke that infects livestock and humans causing fasciolosis, a zoonotic disease of increasing importance due to its worldwide distribution and high economic losses. The parasite regulates the host immune system by inducing a strong Th2 and regulatory T (Treg) cell immune response through mechanisms that might involve the expression or activity of heme-oxygenase-1 (HO-1), the rate-limiting enzyme in the catabolism of free heme that also has immunoregulatory and antioxidant properties. In this paper, we show that F. hepatica-infected mice upregulate HO-1 on peritoneal antigen-presenting cells (APC), which produce decreased levels of both reactive oxygen and nitrogen species (ROS/RNS). The presence of these cells was associated with increased levels of regulatory T cells (Tregs). Blocking the IL-10 receptor (IL-10R) during parasite infection demonstrated that the presence of splenic Tregs and peritoneal APC expressing HO-1 were both dependent on IL-10 activity. Furthermore, IL-10R neutralization as well as pharmacological treatment with the HO-1 inhibitor SnPP protected mice from parasite infection and allowed peritoneal APC to produce significantly higher ROS/RNS levels than those detected in cells from infected control mice. Finally, parasite infection carried out in gp91phox knockout mice with inactive NADPH oxidase was associated with decreased levels of peritoneal HO-1+ cells and splenic Tregs, and partially protected mice from the hepatic damage induced by the parasite, revealing the complexity of the molecular mechanisms involving ROS production that participate in the complex pathology induced by this helminth. Altogether, these results contribute to the elucidation of the immunoregulatory and antioxidant role of HO-1 induced by F. hepatica in the host, providing alternative checkpoints that might control fasciolosis.