Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Logging-on-Write to Improve Non-Volatile Memory Checkpoints via Processing-in-Memory

NVM architectures must keep consistent data in case of failures, a property called crash consistency. A common way to do so is by checkpoint mechanisms. However, most of the strategies developed have performance and usability problems. Among main limitations are non-software-transparent strategies, the addition of logging operations in the critical execution path, and increased writes to NVM, resulting in significant bandwidth usage between processor and memory. DONUTS solves these problems by a hardware mechanism that provides crash consistency via checkpoints integrated into cache replacement policy using Processing-in-Memory to perform logging operations. Its approach reduces writes from the processor's memory controller and the NVM external bandwidth usage but generates unnecessary log entries. This paper expands DONUTS to a multi-core scenario evaluating two processing-in-memory strategies. The first logs during read operations, and the second uses a new lazy strategy to log data exclusively on write operations when these operations are effectively needed. Finally, we compare runtime performance, log rate, energy consumption, and memory space. Results show that our new logging-on-write strategy maintained the DONUTS runtime performance but reduced energy consumption in multi-core applications by around 33% due to an average reduction of 42% in log operations. Also, the new strategy generates a checkpoint size 5x smaller than the previous system, maximizing the use of NVM. Compared to other systems, DONUTS presented an average overhead of 1% to 3% against up to 7% of previous software-transparent better-performing projects.
Implemented Model for CNN Facial Expressions: Emotion Recognition

By aggregating real and active expressions and motions, the proposed paper obtains useful results that can provide any beneficial result to the useful concept so that the dynamic part of the incoming expression is in the picture in front. This article is able to identify the various manifestations with dynamic platform and then design for the subsequent action as per the situation. The situation has been realized by the natural environment and of course a number of techniques have been produced in many articles and the latest implemented graphs, which are capable of taking images as input values of the running system. Now, a live video is playing in place of the saved images and an objective has been created to achieve accuracy in the result as live video is usually described as a realistic expression of the active object. In a broad sense, the video mainly conveys a varied expression of inner emotion as several changes have been considered at a time to make this research article very functional for others by recognizing the active environment for the current screenplay. In this paper a Convolutional Neural Network (CNN) model is implemented with Python programming for better analysis with different expressions and shows the analysis by comparison with existing techniques.
Adaptive Label Propagation for Group Anomaly Detection in Large-Scale Networks

This paper concentrates on group anomalies in general large-scale networks. Existing algorithms on group anomalies mainly focus on homogeneous or bipartite networks, and thus are difficult to apply to heterogeneous networks directly. Moreover, these algorithms follow the non-overlapping hypothesis of groups implicitly, which is improper in many scenarios. For example, fraud users in Alibaba E-commerce platform may join more than one organization at the same time. In this paper, we introduce a novel algorithm called Adaptive Label Propagation (ALP) to solve these problems. ALP is designed based on label propagation (LP) frameworks, for the reason that LP-based frameworks are simple in thought and easy to scale. ALP is able to find overlapping groups by label propagation with belonging coefficients, and can be applied to heterogeneous networks for its design of adaptive neighbor weighting. Assigning different weights to neighbors in label propagation is a challenging task. Inspired by the combinatorial multi-armed bandit mechanism, ALP views the neighbors of each node as arms to be selected, and iteratively updates their weights by evaluating their expected rewards in following iterations. Experiments are conducted on four real-world networks (including two bipartite ones and two heterogeneous ones). The results show that LP-based methods are effective for detecting group anomalies, and the comparison results with several state-of-the-art label propagation based community detection methods show the effectiveness of the proposed method.
Iterative Sound Speed Tomography for Distributed Aberration Correction

In pulse-echo ultrasound imaging, there exists a strong duality between sound speed estimation and aberration correction. This has led to the development of closed-loop iterative frameworks that address both estimation and correction simultaneously. This work revisits previous work on wave-equation migration velocity analysis (WEMVA) to derive and explain our new closed-loop solution to the problem based on a waveform inversion-style approach: full-waveform iterative model-based phase aberration computed tomography (Full-Waveform IMPACT). Full-Waveform IMPACT reproduces the same velocity-depth ambiguities that many previous works have described. However, by approaching the problem using a waveform inversion-style method, we demonstrate that the velocity-depth ambiguity is intricately tied to the problem of cycle skipping and false local minima.
Complete Cardiorespiratory Monitoring via Wearable Ultra Low Power Ultrasound

The continuous monitoring of heart (HR) and respiration (RR) rates is crucial for long-term health assessment and physical activity tracking. HR can be extracted reliably by wearable sensors based on electrocardiography (ECG) and featuring multi-electrode setups. In turn, photoplethysmography (PPG) offers similar HR detection accuracy levels with minimal setups but is affected by motion artifacts. Furthermore, while PPG and ECG may also be utilized for indirect RR assessment, they typically result in moderate accuracy. In this context, ultrasound (US) is a valid tool for complete cardiorespiratory monitoring with a single sensor. In fact, instead of probing electro-optical signals near the skin’s surface, its penetrating ability allows accurate tracking of both ventilation and heartbeat frequencies. However, existing solutions based on the US do not typically offer concurrent HR and RR detection or are not available in wearable form factors and power budgets. In this paper, we demonstrate full cardiorespiratory assessment (HR and RR measurements) by means of a single chest-worn wearable ultra low power (ULP) US probe. The proposed methodology, based on the frequency analysis of sequences of A-mode scans, enables a reliable HR and RR assessment with less than 3.9% and 4.6% errors when compared to ECG (for HR measurements) and manual counting (for RR measurements), respectively. The proposed approach does not require precise positioning of the probe on the chest, offering robustness with respect to user misplacements of the sensor. The system consumes only 17 mW, thereby enabling long-term monitoring.
Research on Classification and Recognition of Micro Milling Tool Wear Based on Improved DenseNet

In order to detect the wear of micro-milling tools, proposing a deep learning neural network detection method based on improved DenseNet. Firstly, the Inception structure adds a multi-channel fusion of features. Secondly, the SE self-attention blocks are added. Finally, the whole network uses the pre-activation structure. The experiment results validate the effectiveness of the proposed model. The results show that the improved DenseNet can accurately identify and classify the 1 mm micro-milling tool wear state.
A Review of Causal Identifiability Techniques across Different Observational Datasets

We present an aggregation of the causal identifiability solutions techniques and their assumptions as advanced in extant literatures with datasets of odd origins, which do not necessarily conform to the independent and identically distributed (i.i.d) dataset, multinomial datasets and the Gaussian datasets settings; alongside their concomitant assumptions. The transformation process in data generation can sometimes be a desideratum of datasets of the following forms: linear and non-Gaussian, nonlinear & non-Gaussian, datasets with missing values, datasets tainted with selection biases, datasets with whose variables forms cycles, datasets with heterogeneous/nonstationary variables, datasets with confounding or latent variables, time-series datasets, deterministic datasets, etc. The study begins proper in section 2 after the introduction with the basic background into the concept of causality with observational data. The concept of graph as an embodiment of the background knowledge with structural causal model (SCM) is explicated in section 3; followed by the basic assumptions employed especially with common observational data settings in section 4. An exposition into the categorization of the algorithms used in causality is presented in section 4. Section 5 aggregates and expounds the causal identifiability techniques and their associated assumptions athwart varying datasets; which is the crux of the study and a recapitulation of same is presented in table 1. This study’s main contribution is to present an aggregate review of the causal techniques and their assumptions across different data settings especially in data settings of odd origins, as reviews such as this are grossly lacking in extant literatures.
Semantic Mapping in Unstructured Environments: Toward Autonomous Localization of Planetary Robotic Explorers

— Highly accurate localization of planetary robotic ex-plorers is crucial for robust, efﬁcient, and safe exploration and path planning in unknown and unstructured planetary environments. In these environments, where satellite-based radio-navigation systems are unavailable, global localization can be achieved by relying on registration of ground imagery to an orbital map, X-band Doppler radio transmissions, or direct observation of the robotic explorers in satellite imagery. While these methods have proven to be effective, they rely heavily on a human-in-the-loop. This paper studies the feasibility of autonomous visual global localization of planetary robotic ex-plorers in extreme and GPS-denied environments by using a trained convolutional neural network (CNN) to obtain saliency maps from semantic segmentation of ground imagery. The saliency maps are registered to projected views of the terrain elevation maps in the rover’s general region of operation to ﬁnd the optimal match that places tight constraints on the pose of the robot in a Mars body-ﬁxed coordinate system. We provide details on the use of the DeepLab V 3+ framework for semantic image segmentation of Martian landscape imagery, including ﬁne-tune training of existing models on domain speciﬁc data. Furthermore, we provide performance analysis of the proposed method on a Martian landscape dataset obtained by NASA’s Perseverance rover, and discuss the limitations of the proposed method and future research directions.
Veriﬁcation of nonequilibrium thermochemistry models for hypersonic CFD by ﬁrst-principles simulation

: In this paper, we present veriﬁcation studies for the recently proposed Modiﬁed Marrone-Treanor [1] (MMT) thermochemical nonequilibrium model for computational ﬂuid dynamics (CFD). We verify our CFD results using ﬁrst-principles Direct Molecular Simulations (DMS) that rely only on potential energy surfaces (PESs) to determine all collision outcomes. Therefore, DMS is capable of naturally predicting vibrationally coupled excitation and dissociation of the gas under thermochemical nonequilibrium without any tuning parameters. The MMT model was developed us-ing ﬁrst-principles data for nitrogen and oxygen dissociation, but used legacy model parameters for Zel’dovich reactions. The present research performs ﬁrst-principles simulations that now include all important interactions for dissociated air, including Zel’dovich reactions. The results are used to verify the accuracy of the MMT CFD model for a range of post-shock conditions, and suggest further modiﬁcations to improve the model. This research demonstrates how complex nonequilibrium chemistry induced by hypersonic ﬂight can be incorporated into accurate and eﬃcient models for large-scale CFD simulations.
Accelerating Extra Dimensional Page Walks for Confidential Computing

To support highly scalable and fine-grained computing paradigms such as microservices and serverless computing better, modern hardware-assisted confidential computing systems, such as Intel TDX and ARM CCA, introduce permission table to achieve fine-grained and scalable memory isolation among different domains. However, it also adds an extra dimension to page walks besides page tables, leading to significantly more memory references (e.g., 4 12 for RISC-V Sv39)1. We observe that most costs (about 75%) caused by the extra dimension of page walks are used to validate page table pages. Based on this observation, this paper proposes HPMP (Hybrid Physical Memory Protection), a hardware-software co-design (on RISC-V) that protects page table pages using segment registers and normal pages using permission tables to balance scalability and performance. We have implemented HPMP and Penglai-HPMP (a TEE system based on HPMP) on FPGA with two RISC-V cores (both in-order and out-of-order). Evaluation results show that HPMP can reduce costs by 23.1%–73.1% on BOOM and significantly improve performance on real-world applications, including serverless computing (FunctionBench) and Redis.
The Influence of Brand Image and Brand Personality on Brand Loyalty

Several studies in the field of branding management, customer behavior have mentioned that there are variables related to affect customer brand loyalty such as perceived quality, brand awareness, brand equity, among others.However, few studies have attempted to consider the influence of brand image and brand personality. Even if any, they have reported conflicting findings. This paper proposes to examine the relationship between brand image and brand personality on brand loyalty.
Regional Intensity-Duration-Frequency Curves Derived from Ensemble Empirical Mode Decomposition and Scaling Property

This paper proposes deriving regional intensity-duration-frequency (IDF) curves for Edmonton, Canada, based on the scaling property of precipitation data using ensemble empirical mode decomposition (EEMD). Selected sets of annual maximum precipitation data were decomposed by the EEMD to intrinsic mode functions (IMFs), and the scaling property was investigated. Next, representative scale exponents were extracted. The results show that quantiles derived from general extreme value (GEV) probability distribution (PD) with parameters derived by the probability-weighted moment (PWM) are more accurate than those derived from the extreme value type I (EVI) PD with parameters derived by the method of moment (MOM), whose underestimation of rainfall intensity becomes obvious for high return period (greater than 25 years) and short duration (less than 1 h). The results also show that for Edmonton, generally three of four IMFs of the precipitation data showed a simple scaling property, and regional IDF curves derived from the scaling IDF and EEMD approach predict accurate storm intensities for rain-gauging sites at both the calibration and validation stages, but there could be errors associated with predicted storms of high return periods (100 year). DOI: 10.1061/(ASCE)HE.1943-5584.0000612. © 2013 American Society of Civil Engineers.
The Norwegian Fishery for Northern Shrimp (Pandalus borealis) in Skagerrak and the Norwegian Deep (ICES Divisions IIIa and IVa east), 1970-2010

The resource of northern shrimp (Pandalus borealis) in the North Sea and Skagerrak is assessed as three separate stocks: 1) the Skagerrak-Norwegian Deep stock which is largely confined to ICES Divs. IIIa and IVa east, 2) the Fladen Ground stock in ICES Div. IVa west, and 3) the Farn Deep stock in ICES Div. IVb west. Vessels from Norway, Denmark, Sweden, and UK exploit this resource. The Norwegian fishery is catch regulated by individual vessel quotas. Information on the Norwegian shrimp fishery (fleet, gear, and prices) was updated. The recording of twin trawl use is incomplete in the logbooks. In order to correct the logbooks regarding the use of single and twin trawls, interviews have been made with ship owners identified from logbooks each year since 2007. Twentyfive vessels reported the use of twin trawls in this period.
Review on Molecular and Conventional Diagnostic Techniques of Bovine Tuberculosis

: Bovine tuberculosis is a major infectious disease of cattle, other domesticated animals and certain wildlife populations. It is one of the zoonotic diseases and can be diagnosed with different techniques. The currently used techniques are acid fast staining which is a cost-effective tool for diagnosing tuberculosis case and to monitor the progress of treatment and it has also drawbacks such as the low sensitivity in the immune-suppressed individuals; Immunological diagnostic techniques like tuberculin skin tests: Single Intradermal Test, Comparative Intradermal Test, Short Thermal Test and Stormont Test; Blood based diagnostic techniques like gamma interferon assays, Enzyme-Linked Immunosorbent Assays and Lymphocyte Proliferation Assay, Culture of Mycobacterium and Molecular diagnostic techniques which involves Polymerase Chain Reaction, is a method that allows direct identification of the Mycobacterium tuberculosis Complex. Spoligotyping, Restriction Fragment Length Polymorphism, Variable Number Tandem Repeats typing are the techniques used for concurrent detection and typing of mycobacterium species at strain level. Its clinical usefulness over the other techniques is determined by its rapidity, both in identifying causative bacteria and in providing molecular epidemiologic information on strains.However, it holds drawbacks of being expensive and requiring well-equipped laboratory and skilled laboratory personnel, which are not always available in endemic areas. Thus, both conventional and molecular tools should be effectively used to diagnose Tuberculosis.Detection of Bovine Tuberculosis in Ethiopia is carried out most commonly on the basis of tuberculin skin testing, abattoir meat inspection and very rarely on bacteriological techniques.
AN APPROACH FOR FINDING A GENERAL APPROXIMATION TO THE GROUP SEQUENTIAL BOOTSTRAP TEST

Randomized experiments are regarded as the gold standard for estimating causal effects. Commonly, a single test is performed using a ﬁxed sample size. However, observations may also be observed sequentially and because of economical and ethical reasons, it may be desirable to terminate the trial early. The group sequential design allows for interim analyses and early stopping of a trial without the need for continuous monitoring of the accumulating data. The implementation of a group sequential procedure requires that the sampling distribution of the test statistic observed at each wave of testing to have a known or asymptotically known sampling distribution. This thesis investigates an approach for ﬁnding a general approximation to the group sequential bootstrap test for test statistics with unknown or analytically intractable sampling distributions. There is currently no bootstrap version of the group sequential test. The approach implies approximating the covariance structure of the test statistics over time, but not the marginal sampling distribution, with that of a normal test statistic. The evaluation is performed with a Monte Carlo simulation study where the achieved signiﬁcance level is compared to the nominal. Evidence from the Monte Carlo simulations suggests that the approach performs well for test statistics with sampling distributions close to a normal distribution.
Investigating the Role of Subaerial Bioﬁlms in Cultural Heritage Conservation with Infrared Thermography

: Understanding the impact of subaerial bioﬁlms (SABs) on geomaterial weathering is crucial for the sustainable conservation of cultural heritage belements. As SABs can either exacerbate or mitigate sudden thermal or hygroscopic stress, it is essential to identify their protective or deterio-rative role on a case-by-case basis. This study aims to validate the utility of infrared thermography in examining the impact of bioﬁlms on stone and exploring the water-related behaviors of porous materials colonized by SABs.
Look Before Leap: Look-Ahead Planning with Uncertainty in Reinforcement Learning

Model-based reinforcement learning (MBRL) has demonstrated superior sample efficiency compared to model-free reinforcement learning (MFRL). However, the presence of inaccurate models can introduce biases during policy learning, resulting in misleading pseudo samples. The challenge lies in obtaining accurate models due to limited diverse training data, particularly in regions with limited visits (uncertain regions). Existing approaches passively quantify uncertainty after sample generation, failing to actively collect uncertain samples that could enhance state coverage and improve model accuracy. Moreover, MBRL often faces difficulties in making accurate multi-step predictions, thereby impacting overall performance. To address these limitations, we propose a novel framework that combines MBRL and MFRL while actively considering uncertainty on both fronts. In the MBRL component, we introduce a k-step lookahead planning approach that incorporates uncertainty to guide action selection. This process involves a trade-off analysis between model uncertainty and value function error, effectively enhancing policy performance. In the MFRL component, we leverage an uncertainty-driven exploratory policy to actively collect diverse training samples, resulting in improved model accuracy and overall performance of the RL agent. Our approach offers flexibility and applicability to tasks with varying state/action spaces and reward structures. We validate its effectiveness through experiments on challenging robotic manipulation tasks and Atari games, surpassing state-of-the-art methods with fewer interactions, thereby leading to significant performance improvements.
Gene Cloning and Expression of Pyridoxal 5'-Phosphate-Dependent L-threo-3-Hydroxyaspartate Dehydratase from Pseudomonas sp. T62, and Characterization of the Recombinant Enzyme

L - threo -3-Hydroxyaspartate dehydratase ( L -THA DH, EC 4.3.1.16), which catalyzes the cleavage of L - threo -3-hydroxyaspartate ( L -THA) to oxalacetate and ammonia, has been purified from the soil bacterium Pseudomonas sp. T62. In this report, the gene encoding L -THA DH was cloned and expressed in Escherichia coli , and the gene product was purified and characterized in detail. A 957 bp nucleotide fragment was confirmed to be the gene encoding L -THA DH, based on the agreement of internal amino acid sequences. The deduced amino acid sequence, which belongs to the serine/threonine dehydratase family, shows similarity to YKL218c from Saccharomyces cerevisiae (64%), serine racemase from Schizosaccharomyces pombe (64%), and Mus musculus (36%), and biodegradative threonine dehydratase from E. coli (38%). Site-directed mutagenesis experiments revealed that lysine at position 53 is an important residue for enzymatic activity. This enzyme exhibited dehydratase activity specific only to L -THA ( K m = 0.54 mM, V max = 39.0  mol min -1 [mg protein] -1 ), but not to other 3-hydroxyaspartate isomers, and exhibited no detectable serine/aspartate racemase activity. This is the first report of an amino acid sequence of the bacterial enzyme that acts on L -THA.