Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating machine learning algorithms to predict lameness in dairy cattle

Dairy cattle lameness represents one of the common concerns in intensive and commercial dairy farms. Lameness is characterized by gait-related behavioral changes in cows and multiple approaches are being utilized to associate these changes with lameness conditions including data from accelerometers, and other precision technologies. The objective was to evaluate the use of machine learning algorithms for the identification of lameness conditions in dairy cattle. In this study, 310 multiparous Holstein dairy cows from a herd in Northern Colorado were affixed with a leg-based accelerometer (Icerobotics® Inc, Edinburg, Scotland) to obtain the lying time (min/d), daily steps count (n/d), and daily change (n/d). Subsequently, study cows were monitored for 4 months and cows submitted for claw trimming (CT) were differentiated as receiving corrective claw trimming (CCT) or as being diagnosed with a lameness disorder and consequent therapeutic claw trimming (TCT) by a certified hoof trimmer. Cows not submitted to CT were considered healthy controls. A median filter was applied to smoothen the data by reducing inherent variability. Three different machine learning (ML) models were defined to fit each algorithm which included the conventional features (containing daily lying, daily steps, and daily change derived from the accelerometer), slope features (containing features extracted from each variable in Conventional feature), or all features (3 simple features and 3 slope features). Random forest (RF), Naive Bayes (NB), Logistic Regression (LR), and Time series (ROCKET) were used as ML predictive approaches. For the classification of cows requiring CCT and TCT, ROCKET classifier performed better with accuracy (> 90%), ROC-AUC (> 74%), and F1 score (> 0.61) as compared to other algorithms. Slope features derived in this study increased the efficiency of algorithms as the better-performing models included All features explored. However, further classification of diseases into infectious and non-infectious events was not effective because none of the algorithms presented satisfactory model accuracy parameters. For the classification of observed cow locomotion scores into severely lame and moderately lame conditions, the ROCKET classifier demonstrated satisfactory accuracy (> 0.85), ROC-AUC (> 0.68), and F1 scores (> 0.44). We conclude that ML models using accelerometer data are helpful in the identification of lameness in cows but need further research to increase the granularity and accuracy of classification.
Energy efficient multiuser scheduling: Statistical guarantees on bursty packet loss

In this paper, we consider energy efficient multiuser scheduling. Packet loss tolerance of the applications is exploited to minimize average system energy. There is a constraint on average packet drop rate and maximum number of packets dropped successively (bursty loss). A finite buffer size is assumed. We propose a scheme which schedules the users opportunistically according to the channel conditions, packet loss constraints and buffer size parameters. We assume imperfect channel state information at the transmitter side and analyze the scheme in large user limit using stochastic optimization techniques. First, we optimize system energy for a fixed buffer size which results in a corresponding statistical guarantee on successive packet drop. Then, we determine the minimum buffer size to achieve a target (improved) energy efficiency for the same (or better) statistical guarantee. We show that buffer size can be traded effectively to achieve system energy efficiency for target statistical guarantees on packet loss parameters.
Enhancing Cancer Cell Classification through Dataset Augmentation using Conditional Variational Autoencoder (CVAE)

This paper uses Conditional Variational Autoencoder (CVAE) as a data augmentation technique for cancer cell classification using four different classification algorithm; Support Vector Machine (SVM), XGBoost, Decision Tree, and Logistic Regression. The main aim of the study is to enhance the accuracy of the classification models by generating synthetic data using CVAE. The results obtain shows that there is significant improvement when CVAE is used to generate additional data. The approach used indicates a promise in accurate cancer classification, even in the absence of adequate dataset. This will help to improve cancer diagnosis and treatment in the health industry
Another Look at Search-Based Drama Management

A drama manager (DM) monitors an interactive experience, such as a computer game, and intervenes to shape the global experience so it satisfies the author's expressive goals without decreasing a player's interactive agency. In declarative optimization-based drama management (DODM), the author declaratively specifies desired properties of the experience; the DM optimizes its interventions to maximize that metric. The initial DODM approach used online search to optimize an experience-quality function. Subsequent work questioned whether online search could perform well in general, and proposed alternative optimization frameworks such as reinforcement learning. Recent work on targeted trajectory distribution Markov decision processes (TTD-MDPs) replaced the experience-quality metric with a metric and associated algorithm based on targeting experience distributions. We argue that optimizing an experience-quality function does not destroy interactive agency, as has been claimed, and that in fact it can capture that goal directly. We further show that, though apparently quite different on the surface, the original search approach and TTD-MDPs actually use variants of the same underlying search algorithm, and that offline cached search, as is done by the TTD-MDP algorithm, allows the search-based systems to achieve similar results to TTD-MDPs.
Digital Repository @ Maurer Law Digital Repository @ Maurer Law

Evolutionary theory and empirical studies suggest that many animals, including humans, have a genetic pre-disposition to acquire and retain property. This is hardly surprising because survival is closely bound up with the acquisition of things: food, shelter, tools and territory. But the root of these general urges may also run to quite specific and detailed rules about property acquisition, retention and disposition. The great variation in property-related behaviours across species may mask some important commonalities grounded in adaptive utility. Experiments and observations in the field and laboratory suggest that the legal rules of temporal priority and possession are grounded in what were evolutionarily stable strategies in the ancestral environment. Moreover, the preferences that humans exhibit in disposing of their property on their deaths, both by dispositions made in wills and by the laws of intestacy, tend to advance reproductive success as a result of inclusive fitness pay-offs.
Desalination and Water

According to Directive 2020/2184, each water supplier is required to develop a water supply risk management system, such as the World Health Organization’s recommended Water Safety Plan, implement it and apply it to daily water supply practices. An important element of risk management in water supply systems (WSS) is to ensure the supply of water that is healthy and clean, that is, free of all microorganisms and parasites and any substances in quantities or concentrations that pose a potential danger to human health. Ensuring the microbiological safety of water, while minimizing the risks associated with disinfection by-products, is particularly important in extensive WSS, where water can take days to transport, and where hydraulic parameters and sediments deposited in water supply networks favor the growth of microorganisms. This paper proposes an analytical method to support the determination of areas sensitive to the loss of microbiological safety of water, while using the model to optimize the dose of disinfectant at water treatment plants and water chlorination points in the distribution subsystem. The developed methodology is part of a review of operating procedures, especially with regard to minimizing the risk of secondary microbiological contamination of water. The research model took into account the chemical as well as microbiological variability of water quality, its age, and the operational data of the WSS operation collected by the Geographic Information System (location of water quality monitoring points, spatial location of water supply infrastructure facilities), as well as the size of the population potentially exposed to water of uncertain microbiological quality. The result of the analyses is a spatial interpretation of the loss of microbiological safety of water presented in the form of risk maps.
BDNF Increases Survival and Neuronal Differentiation of Human Neural Precursor Cells Cotransplanted with a Nanofiber Gel to the Auditory Nerve in a Rat Model of Neuronal Damage

Objectives. To study possible nerve regeneration of a damaged auditory nerve by the use of stem cell transplantation. Methods. We transplanted HNPCs to the rat AN trunk by the internal auditory meatus (IAM). Furthermore, we studied if addition of BDNF affects survival and phenotypic differentiation of the grafted HNPCs. A bioactive nanofiber gel (PA gel), in selected groups mixed with BDNF, was applied close to the implanted cells. Before transplantation, all rats had been deafened by a round window niche application of β-bungarotoxin. This neurotoxin causes a selective toxic destruction of the AN while keeping the hair cells intact. Results. Overall, HNPCs survived well for up to six weeks in all groups. However, transplants receiving the BDNF-containing PA gel demonstrated significantly higher numbers of HNPCs and neuronal differentiation. At six weeks, a majority of the HNPCs had migrated into the brain stem and differentiated. Differentiated human cells as well as neurites were observed in the vicinity of the cochlear nucleus. Conclusion. Our results indicate that human neural precursor cells (HNPC) integration with host tissue benefits from additional brain derived neurotrophic factor (BDNF) treatment and that these cells appear to be good candidates for further regenerative studies on the auditory nerve (AN).
SmartDIMM: In-Memory Acceleration of Upper Layer Protocols

—There has been significant focus on offloading upper-layer network protocols (ULPs) to accelerators located on CPUs and SmartNICs. However, restricting accelerator placement to these locations limits both the variety of ULPs that can be accelerated and the overall performance. In particular, it overlooks the opportunity to accelerate ULPs running atop a stateful transport protocol in the face of high cache contention. That is, at high network rates, the frequent DRAM accesses and SmartNIC-CPU synchronizations outweigh the benefits of hardware acceleration. This work introduces SmartDIMM, which unlocks the opportunity for accelerating ULPs running atop stateful transport protocols that primarily operate on data stored in DRAM. We prototyped SmartDIMM using Samsung’s AxDIMM and implemented end-to-end offloading of (de/en)cryption and (de)compression– two ULPs widely employed in datacenters. We then compared the performance of SmartDIMM with accelerator placements on the CPU, SmartNIC, and PCIe cards. Our results demonstrate that ULP offloading on SmartDIMM outperforms CPU, SmartNIC and PCIe-based offload configurations. In comparison to a server executing (de/en)cryption and (de)compression on the CPU, SmartDIMM achieves 21.0% to 10.28 × higher requests per second and 36.3% to 88.9% lower memory bandwidth utilization.
Robust estimation of ultrasound pulses using outlier-resistant de-noising

A different approach to the problem of estimation of the ultrasound pulse spectrum, which usually arises as a part of ultrasound image restoration algorithms, is presented. It is shown that this estimation problem can be reformulated in terms of a de-noising problem. In this formulation, the log-spectrum of a radio-frequency line (RF-line) is viewed as a noisy measurement of the signal that needs to be estimated, i.e., the ultrasound pulse log-spectrum. The log-spectrum of the tissue reflectivity function (i.e., tissue response) is considered as the noise to be rejected. The contribution of the paper is twofold. First, it provides statistical description of the reflectivity function log-spectrum for the case, when the samples of the reflectivity function are independent identically distributed (i.i.d.) Gaussian random variables. Moreover, it is shown that the problem of the pulse spectrum recovery is essentially a de-noising problem. Consequently, it is suggested to solve the problem within the framework of the de-noising by wavelet shrinkage. Second, a computationally efficient algorithm is proposed for the pulse-spectrum estimation, which can be viewed as a modified version of the classical Donoho's three-step de-noising procedure. This modification is necessary, because of specific properties of the noise to be rejected. It is shown, that whenever the samples of the reflectivity function can be assumed to be i.i. d. Gaussian random variables, the samples of its log-spectrum obey the Fisher-Tippet distribution. For this type of noise, straightforward implementation of the standard de-noising can cause serious estimation errors. In order to overcome this difficulty, an outlier-resistant de-noising is performed. The unique properties of this modified de-noising algorithm allow estimating the pulse spectrum adaptively to its properties, as they are continuously influenced by the frequency-dependent attenuation process. The performance of the proposed algorithm is examined in a series of computer-simulations. It is shown that this algorithm, developed on the assumption of the "Gaussian" reflectivity function, remains applicable for broader classes of distributions. The results obtained in a series of in vivo experiments reveal superior performance of the novel approach over some of alternative estimation techniques, e.g., cepstrum-based estimation.
Spit and Inlet Morphodynamics of a Tropical Coastal Lagoon

Abstract The morphological changes of spits and inlets of the Chilika lagoon, the largest brackish water tropical coastal lagoon in Asia, are investigated using real-time kinematic GPS observation and numerical models during 2009–2013. The seasonal/interannual variations of the spit and inlet cross-sectional areas with varying widths and depths are recorded in association with different physical processes. The results show significant changes in spit morphology: particularly, the south spit accreted continuously, while the middle and north spits eroded. The cross-sectional depth of inlets becomes narrower and deeper during summer and winter seasons, while they are wider and shallower during the monsoon. The model results show that sediment transport rate is larger during monsoon and summer, while it is relatively less during the winter. Alongshore, sediment transport is predominantly northward throughout the study period. The result shows that gain/loss of the spits and closure/opening of inlets are significantly controlled by the high wave power, longshore drifts, and river discharge. The study demonstrates that the combined use of observational and numerical models is very effective to understand the changes of spit and inlet morphology and their impact on ecological conditions of the lagoon environment.
Perspectives in Home TeleHealthCare System: Daily Routine Nycthemeral Rhythm Monitoring from Location Data

Free of most social constraints, elderly people tend to perform activities of daily living following the same sequence. This paper proposes a method for medical telesurveillance to detect and quantify a nycthemeral shift in this daily routine. While this common phenomenon is mostly mild, in acute cases, however, it may reveal a pathological behavior requiring a rapid medical examination. This method allows to compare two sequences of activities using the Hamming distance and to interpret the result according to the Gumbel distribution. It may be used to compare either consecutive sequences thereby taking into account evolution in the habits or a sequence to the person’s individual activity profile to detect dementia’s onset. In this early stage, only elementary activities were considered. That is the reason why location data were used to monitor the person’s nycthemeral rhythm of activity. IR sensors placed in her own flat allowed us to follow-up the inhabitant’s successive activities. Improvements of this method have already been planned. They include the use of a multi-sensors network to monitor both actimetric (location, movement, posture) and physiological nycthemeral rhythms (ECG, respiratory frequency) and to detect the use of particular items (fridge, chairs, bed). This more sophisticated sensors network will allow us to monitor more complex tasks execution and then to detect pathological behaviors such as perseveration in a task or wandering. On the other hand, multiplying sensors will require more storage capacities and the use of time-consuming data fusion tools. Therefore, a classification phase will be necessary to reduce as possible the number of relevant sensors.
Inference in Hybrid Bayesian Networks Using Mixtures of Gaussians

The main goal of this paper is to describe a method for exact inference in general hybrid Bayesian networks (BNs) (with a mixture of discrete and continuous chance variables). Our method consists of approximating general hybrid Bayesian networks by a mixture of Gaussians (MoG) BNs. There exists a fast algorithm by Lauritzen-Jensen (LJ) for making exact inferences in MoG Bayesian networks, and there exists a commercial implementation of this algorithm. However, this algorithm can only be used for MoG BNs. Some limitations of such networks are as follows. All continuous chance variables must have conditional linear Gaussian distributions, and discrete chance nodes cannot have continuous parents. The methods described in this paper will enable us to use the LJ algorithm for a bigger class of hybrid Bayesian networks. This includes networks with continuous chance nodes with non-Gaussian distributions, networks with no restrictions on the topology of discrete and continuous variables, networks with conditionally deterministic variables that are a nonlinear function of their continuous parents, and networks with continuous chance variables whose variances are functions of their parents.
The role of digital intervention in the success of Micro-finance through SIDBI-PRAYAAS scheme: A case study

Microfinance emerges as a pivotal catalyst within India's micro, medium, and small-scale industries. A notable exemplar is the SIDBI-PRAYAAS initiative, under the auspices of SIDBI, strategically engineered to empower the lower echelons of the financial pyramid. The latest study unveils a compelling triumph, spotlighting an impressive 6.02% Compound Annual Growth Rate (CAGR) surge in the commercial ventures of beneficiaries who availed themselves of this pioneering scheme. In this context, the infusion of digital mechanisms, including B-POS and B-Bank , has admirably streamlined the application, disbursement, and meticulous execution of loan reimbursements. The efficacy of these interventions reverberates triumphantly. Notably, the synergy of the Patanjali Ayurved Limited collaboration has further amplified the scheme's resonance, as eager borrowers harness its potential to nurture the expansion of Gramin Arogya Kendra. Considering these advancements, the current case study stands as a compelling testament to the efficacy of the PRAYAAS schemes, seamlessly interwoven with the digital dexterity of contemporary banking paradigms. This holistic approach underscores not only the scheme's operational prowess but also engenders profound customer gratification, thus forging a harmonious synthesis of financial inclusion and technological ingenuity.
Speaker, Noise, and Acoustic Space Adaptation for Emotion Recognition in the Automotive Environment

Emotional surveillance of drivers possesses significant potential for increased security within passenger transport. In an automotive setting the interaction can further be improved by social awareness of an MMI. Within this scope the detection of security relevant behavior patterns as aggressiveness or sadness is discussed. The focus lies on real-life usage respecting online processing, subject independency, and noise robustness. The approach introduced employs multivariate time-series analysis by brute-force feature generation. Extensive results are reported on two public standard corpora. The influence of noise is discussed by representative car-noise overlay. Thereby impact per low-level-descriptor is considered.
System level memory optimization for hardware-software co-design

Application studies in the areas of image and video processing systems indicate that between 50 and 80% of the area cost in (application-specific) architectures for real-time multi-dimensional signal processing (RMSP) is due to data storage and transfer of array signals. This is true for both singleand multi-processor realizations, both customized and (embedded) programmable targets. This paper has two main contributions. First, to reduce this dominant cost, we propose to address the system-level storage organization for the multi-dimensional signals as a first step in the overall methodology to map these applications, before the hardware/software partitioning decision. Secondly, we demonstrate the usefulness of this novel approach based on a realistic test vehicle, namely a quad-tree based image coding application.
Palm oil fuel ash (POFA) as a future partial cement replacement material in housing construction

This paper is an attempt to highlight the potential use of palm oil fuel ash (POFA) as partial cement replacement material in mortar and concrete in housing construction. The ash tested satisfies the chemical and physical requirements for a reactive pozzolanic ash as specified by ASTM C618-84 and hence classified into class F fly ash. Mortar cube samples of OPC/POFA containing 10% to 40% ash replacement level were analysed and tested for strength characteristics in accordance with the procedures outlined in the BS 12 part 2 :1971. The mortar samples were also tested for the ash reactivity and pozzolanicity using thermogravimetry apparatus as well as the water permeability and porosity using water permeability apparatus and mercury intrusion porosimeter respectively. The results indicate that both engineering and chemical and microstructural characteristics are very promising. POFA replacement level up to 20% is possible without causing adverse effect on strength characteristic. This is enhanced by the TG test results which indicate that POFA is positively reacting to produce more gel hence offsetting the loss of strength due to the OPC replaced. The hydrating products are found to be more dense as shown by the mercuryporosimetry test results. Water permeability and construction material durability shows at least a comparable performance to the OPC control results.