Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical objective assessment of diagnostic anaesthesia and investigation of compensatory lameness in the horse

Objectives: Objective assessment of lameness in a clinical setting has been limited by the need for complex equipment. The introduction of a commercially available inertial sensor-based system of lameness diagnosis has made objective lameness assessment clinically available. The objective of the first part of this study was to validate the use of an inertial sensor-based system of lameness diagnosis to objectively identify a positive response to diagnostic anaesthesia of the equine foot. The second part of the study objectively examined clinical compensatory lameness, investigating the relationship between primary and compensatory lameness. Study design: A retrospective study of data obtained from horses that underwent clinical diagnostic anaesthesia while instrumented with an inertial sensor-based system of lameness diagnosis between August 2011 and October 2014 was performed. Method: Horses were grouped as positive or negative (referring to the change to lameness) depending on the response to diagnostic anaesthesia. Those horses categorized as positive were further grouped into those with forelimb lameness only, hindlimb lameness only, ipsilateral limb lameness, or contralateral limb lameness. Kinematic parameters of head and pelvic movement asymmetry were measured and the change in the parameters was calculated. The effect of diagnostic anaesthesia was determined using the Mann-Whitney rank sum test. Results: Assessment of local anaesthesia of the foot in horses with forelimb lameness: A positive response to diagnostic anaesthesia resulted in a significant improvement to the symmetry of movement in the affected limb. ROC curve analysis showed that the change in head movement asymmetry (vector sum) is an excellent diagnostic test (AUC=1.0). Forelimb compensatory lameness study: Improvement in forelimb lameness resulted in a significant decrease in pelvic movement asymmetry associated with the contralateral hindlimb (p<0.05). This was associated with improvement in push-off from the contralateral limb (p<0.01). Hindlimb compensatory lameness study: Improvement in hindlimb lameness resulted in a significant decrease in head movement asymmetry associated with the ipsilateral forelimb (p<0.05). Conclusions: It is possible to classify changes that occur and assess the response following a diagnostic anaesthesia procedure using an inertial sensor-based system of lameness diagnosis. Significant change to hindlimb movement following diagnostic anaesthesia of the forelimb in horses with forelimb lameness was demonstrated. In addition, significant change to forelimb movement following diagnostic anaesthesia of the hindlimb of horses with hindlimb lameness was demonstrated. In summary, this study supports the use of the inertial sensor-based system of lameness diagnosis in objective assessment of lameness in the horse, and provides significant evidence to support the “law of sides” in horses with naturally occurring lameness. Furthermore, the objective assessment of lameness is possible in a range of clinical settings.
Not a Robot,” or am I?: Micro-Labor and the Immanent Subsumption of the Social in the Human Computation of ReCAPTCHAs

This article analyzes Google’s reCAPTCHA as one instance of what Hardt and Negri have conceptualized as a subsumption of the social as opposed to the extracting taking place under conditions of formal and real subsumption, which occurs in a punctualized production process (e.g., manufacture or fordist factories). Like Hardt and Negri, we see digital networks as having enabled a new commons that capital is trying to subsume. We trace the subsequent development of reCAPTCHA as an ever-evolving form of subsumption of the social that varyingly reconfigures elements of hybrid, formal, and real subsumption. We distinguish between two phases in the subsumption of the social. In a first phase, dispersed micro-labors are captured, aggregated, and put to use to improve the use value of Google’s free Web services. In a second phase, we see that reCAPTCHA tools evolve into a tracking technology, allowing for an immanent subsumption of the social. We observe that thus processes of autonomous and cooperative work in the digital commons are brought under capitalist relations.
BioPharmaView Software as a Robust Tool for Automated Quantitation of Oxidation Sites in Monoclonal Antibody Characterization Oxidation studies at peptide level using benchtop X500B QTOF and BioPharmaViewTM Software

Oxidation of methionine is one of the most common posttranslational modifications known to occur in monoclonal antibodies during manufacturing, formulation and storage processes. The monitoring of such critical qualities attributes is of major concern as it can lead to a product with altered binding properties. Peptide based mass spectrometry methods are generally preferred for the site specific quantitation of oxidation levels under forced conditions. However, peptide level studies are often restricted by the lack of adequate software tools for the automated and accurate quantitation of sites susceptible to oxidation.
Parallel Locality Sensitive Hashing for Network Discovery from Time Series

Similarity operations on time series are a vital area in data mining research. Science and systems applications require a scalable solution that is fast enough to work with streaming of real data and provide reliable results in the presence of noise, high dimensionality and time dependency. Locality sensitive hashing (LSH) has shown advancement in limiting the number of comparisons required for similarity operations, thus reducing the overall time, by pre-processing the data into buckets as candidates for approximate nearest neighbours. This thesis proposes a scalable system based on locality sensitive hashing by implementing it in parallel with independent hash functions. The parallel system we present is implemented in a message-passing framework for four locality sensitive hashing methods – minhash, approximate binary correlation (ABC), symbolic aggregate approximation (SAX), and “sketch, shingle and hash” (SSH), each with its own preprocessing, hash creation, and similarity measure. A preliminary investigation implements minhash with a bag-of-words representation of text data to validate our proposed framework. The experimental part of the thesis focuses on comparing the other three LSH methods (ABC, SAX, SSH) on a real flight data set processed in a streaming fashion, flexible to the size of the time series used. The output of our parallel system is a similarity network discovered from the data, that we use to detect an anomaly present in the data set. The LSH methods are evaluated with respect to the time of execution, amount of communication, computation complexity, tuning of parameters and the required number of similarity operations. Our results indicate the feasibility of the implemented methods and proposed framework for this type of application and this sort of real-life time series. Our thesis concludes with discussion of the impact of the similarity measures on the network discovery results, as well as proposing further investigations into other parts of the parameter space.
Antimicrobial Effect of Mango (Mangifera Indica) Leave Extract on Some Pathogenic Bacteria

Mangifera indica, commonly called mango, is an edible fruit that are also used for juice and wine production. Fresh leaves of M. indica were collected from the premises of Adekunle Ajasin University, Akungba Akoko Ondo State, Nigeria. Materials used, including Gram staining reagents, for the study are properly sterilized. Similarly, the culture media used, including Nutrient agar, MacConkey agar, Eosin methylene blue agar, Salmonella-Shigella agar, Mannitol salt agar, Mueller-Hinton agar and Nutrient broth, were properly sterilized. The bacterial isolates identified and used for the study were Staphylococcus aureus, Salmonella typhi Klebsiella spp. , Pseudomonas aeruginosa, Escherichia coli, and Citrobacter spp. Antimicrobial susceptibility test of the Mango (Mangifera indica) leaves extract against the test bacteria shows that 200 mg/ml and 175 mg/ml extract of the plant source had observable effect against test organisms, particularly Escherichia coli, Salmonella typhi, and to a varying extent on others. The zones of inhibition formed at 200 mg/ml were 20 mm, 11 mm, 10.8 mm, 12 mm 16.3 mm and 7 mm for Escherichia coli, Klebsiella spp, Staphylococcus aureus, Citrobacter spp Salmonella typhi and Pseudomonas aeruginosa respectively. Thereafter, the negative control of the 25% DMSO used for dissolving the extract was done and the result was all negative compared with known active positive control – Ampliclox that is an effective antibiotics. With this current observation, Mango, apart from its nutritious component as food complement, coupled with it good fiber contents as fruit, is also found valuable for some antimicrobial purposes. Which means it can be formulated as food supplement or concentrates for some clinical and pharmaceutical purposes.
Factors And Implications Management Discussion And Analysis

The purpose of this study is to identify the factors that influence the extent of disclosure in management discussion and analysis, as well as the implications for the company's profit growth. The object of this research is a manufacturing company in the consumer goods industry sector from 20152020. With the purposive sampling method obtained a sample of 180 units of analysis. The analysis technique in this study uses two analytical techniques, namely multiple linear regression analysis to examine the factors that influence discussion management and analysis and simple regression to see the implications for profit growth. Regression test results show that Financial Performance, Firm Age, and Leverage have no significant effect on management discussion and analysis, while firm size has a positive effect. while management discussion and analysis do not affect profit growth. With the low delivery of discussion and analysis management reports in Indonesia, it is hoped that the regulator will provide strict rules so that companies increase the information needed by investors.
Basic elements for simulations of standard-model physics with quantum annealers: Multigrid and clock states

We explore the potential of D-Wave’s quantum annealers for computing some of the basic com-ponents required for quantum simulations of Standard Model physics. By implementing a basic multigrid (including “zooming”) and specializing Feynman-clock algorithms, D-Wave’s Advantage is used to study harmonic and anharmonic oscillators relevant for lattice scalar ﬁeld theories and eﬀective ﬁeld theories, the time evolution of a single plaquette of SU(3) Yang-Mills lattice gauge ﬁeld theory, and the dynamics of ﬂavor entanglement in four-neutrino systems.
Site independent strong phonon-vacancy scattering in high temperature ceramics ZrB$_2$ and HfB$_2$

Vrishali Sonar, Rohan Dehankar, K. P. Vijayalakshmi, Natalio Mingo, and Ankita Katre 1 Department of Scientific Computing, Modeling and Simulation, SP Pune University, Pune-411007 India 2 Dept. of Physics, Adam Mickiewicz University, 61-614 Poznàn, Poland 3 Vikram Sarabhai Space Centre, Kochuveli, Thiruvananthapuram-695022, India 4 Université Grenoble Alpes, CEA, LITEN, 17 rue des Martyrs, 38054 Grenoble Cedex 9, France
JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus

Most current machine translation models are mainly trained with parallel corpora, and their translation accuracy largely depends on the quality and quantity of the corpora. Although there are billions of parallel sentences for a few language pairs, effectively dealing with most language pairs is difficult due to a lack of publicly available parallel corpora. This paper creates a large parallel corpus for English-Japanese, a language pair for which only limited resources are available, compared to such resource-rich languages as English-German. It introduces a new web-based English-Japanese parallel corpus named JParaCrawl v3.0. Our new corpus contains more than 21 million unique parallel sentence pairs, which is more than twice as many as the previous JParaCrawl v2.0 corpus. Through experiments, we empirically show how our new corpus boosts the accuracy of machine translation models on various domains. The JParaCrawl v3.0 corpus will eventually be publicly available online for research purposes.
HipBone: A performance-portable GPU-accelerated C++ version of the NekBone benchmark

We present hipBone, an open source performance-portable proxy application for the Nek5000 (and NekRS) CFD applications. HipBone is a fully GPU-accelerated C++ implementation of the original NekBone CPU proxy application with several novel algorithmic and implementation improvements which optimize its performance on modern fine-grain parallel GPU accelerators. Our optimizations include a conversion to store the degrees of freedom of the problem in assembled form in order to reduce the amount of data moved during the main iteration and a portable implementation of the main Poisson operator kernel. We demonstrate near-roofline performance of the operator kernel on three different modern GPU accelerators from two different vendors. We present a novel algorithm for splitting the application of the Poisson operator on GPUs which aggressively hides MPI communication required for both halo exchange and assembly. Our implementation of nearest-neighbor MPI communication then leverages several different routing algorithms and GPU-Direct RDMA capabilities, when available, which improves scalability of the benchmark. We demonstrate the performance of hipBone on three different clusters housed at Oak Ridge National Laboratory, namely the Summit supercomputer and the Frontier early-access clusters, Spock and Crusher. Our tests demonstrate both portability across different clusters and very good scaling efficiency, especially on large problems.
Visibility Maximization Controller for Robotic Manipulation

Occlusions caused by a robot’s own body is a common problem for closed-loop control methods employed in eye-to-hand camera setups. We propose an optimization-based reactive controller that minimizes self-occlusions while achieving a desired goal pose. The approach allows coordinated control between the robot’s base, arm and head by encoding the line-of-sight visibility to the target as a soft constraint along with other task-related constraints, and solving for feasible joint and base velocities. The generalizability of the approach is demonstrated in simulated and real-world experiments, on robots with fixed or mobile bases, with moving or fixed objects, and multiple objects. The experiments revealed a trade-off between occlusion rates and other task metrics. While a planning-based baseline achieved lower occlusion rates than the proposed controller, it came at the expense of highly inefficient paths and a significant drop in the task success. On the other hand, the proposed controller is shown to improve visibility to the target object(s) without sacrificing too much from the task success and efficiency. Videos and code can be found at: rhys-newbury.github. io/projects/vmc/.
Bagpipe: Accelerating Deep Recommendation Model Training

Deep learning based recommendation models (DLRM) are widely used in several business critical applications. Training such recommendation models efficiently is challenging because they contain billions of embedding-based parameters, leading to significant overheads from embedding access. By profiling existing systems for DLRM training, we observe that around 75% of the iteration time is spent on embedding access and model synchronization. Our key insight in this paper is that embedding access has a specific structure which can be used to accelerate training. We observe that embedding accesses are heavily skewed, with around 1% of embeddings representing more than 92% of total accesses. Further, we also observe that during offline training we can lookahead at future batches to determine which embeddings will be needed at what iteration in the future. Based on these insights, we develop Bagpipe, a system for training deep recommendation models that uses caching and prefetching to overlap remote embedding accesses with the computation. We design an Oracle Cacher, a new component that uses a lookahead algorithm to generate optimal cache update decisions while providing strong consistency guarantees against staleness. We also design a logically replicated, physically partitioned cache and show that our design can reduce synchronization overheads in a distributed setting. Finally, we propose a disaggregated system architecture and show that our design can enable low-overhead fault tolerance. Our experiments using three datasets and four models show that Bagpipe provides a speed up of up to 5.6x compared to state of the art baselines, while providing the same convergence and reproducibility guarantees as synchronous training.
A Dynamic Low-Rank Fast Gaussian Transform

The \emph{Fast Gaussian Transform} (FGT) enables subquadratic-time multiplication of an $n\times n$ Gaussian kernel matrix $\mathsf{K}_{i,j}= \exp ( - \| x_i - x_j \|_2^2 ) $ with an arbitrary vector $h \in \mathbb{R}^n$, where $x_1,\dots, x_n \in \mathbb{R}^d$ are a set of \emph{fixed} source points. This kernel plays a central role in machine learning and random feature maps. Nevertheless, in most modern data analysis applications, datasets are dynamically changing (yet often have low rank), and recomputing the FGT from scratch in (kernel-based) algorithms incurs a major computational overhead ($\gtrsim n$ time for a single source update $\in \mathbb{R}^d$). These applications motivate a \emph{dynamic FGT} algorithm, which maintains a dynamic set of sources under \emph{kernel-density estimation} (KDE) queries in \emph{sublinear time} while retaining Mat-Vec multiplication accuracy and speed. Assuming the dynamic data-points $x_i$ lie in a (possibly changing) $k$-dimensional subspace ($k\leq d$), our main result is an efficient dynamic FGT algorithm, supporting the following operations in $\log^{O(k)}(n/\varepsilon)$ time: (1) Adding or deleting a source point, and (2) Estimating the ``kernel-density'' of a query point with respect to sources with $\varepsilon$ additive accuracy. The core of the algorithm is a dynamic data structure for maintaining the \emph{projected} ``interaction rank'' between source and target boxes, decoupled into finite truncation of Taylor and Hermite expansions.
Core-periphery detection in hypergraphs

Core-periphery detection is a key task in exploratory network analysis where one aims to find a core, a set of nodes well-connected internally and with the periphery, and a periphery, a set of nodes connected only (or mostly) with the core. In this work we propose a model of core-periphery for higher-order networks modeled as hypergraphs and we propose a method for computing a core-score vector that quantifies how close each node is to the core. In particular, we show that this method solves the corresponding non-convex core-periphery optimization problem globally to an arbitrary precision. This method turns out to coincide with the computation of the Perron eigenvector of a nonlinear hypergraph operator, suitably defined in term of the incidence matrix of the hypergraph, generalizing recently proposed centrality models for hypergraphs. We perform several experiments on synthetic and real-world hypergraphs showing that the proposed method outperforms alternative core-periphery detection algorithms, in particular those obtained by transferring established graph methods to the hypergraph setting via clique expansion.
Complexity of Deliberative Coalition Formation

Elkind et al. (AAAI'21) introduced a model for deliberative coalition formation, where a community wishes to identify a strongly supported proposal from a space of alternatives, in order to change the status quo. In their model, agents and proposals are points in a metric space, agents' preferences are determined by distances, and agents deliberate by dynamically forming coalitions around proposals that they prefer over the status quo. The deliberation process operates via k-compromise transitions, where agents from k (current) coalitions come together to form a larger coalition in order to support a (perhaps new) proposal, possibly leaving behind some of the dissenting agents from their old coalitions. A deliberation succeeds if it terminates by identifying a proposal with the largest possible support. For deliberation in d dimensions, Elkind et al. consider two variants of their model: in the Euclidean model, proposals and agent locations are points in R^d and the distance is measured according to ||. ||_2; and in the hypercube model, proposals and agent locations are vertices of the d-dimensional hypercube and the metric is the Hamming distance. They show that in the Euclidean model 2-compromises are guaranteed to succeed, but in the hypercube model for deliberation to succeed it may be necessary to use k-compromises with k >= d. We complement their analysis by (1) proving that in both models it is hard to find a proposal with a high degree of support, and even a 2-compromise transition may be hard to compute; (2) showing that a sequence of 2-compromise transitions may be exponentially long; (3) strengthening the lower bound on the size of the compromise for the d-hypercube model from d to 2^Ω(d).
Benchmarking Filtering Techniques for Entity Resolution

Entity Resolution is the task of identifying pairs of entity profiles that represent the same real-world object. To avoid checking a quadratic number of entity pairs, various filtering techniques have been proposed that fall into two main categories: (i) blocking workflows group together entity profiles with identical or similar signatures, and (ii) nearest-neighbor methods convert all entity profiles into vectors and identify the closest ones to every query entity. Unfortunately, the main techniques from these two categories have rarely been compared in the literature and, thus, their relative performance is unknown. We perform the first systematic experimental study that investigates the relative performance of the main representatives per category over numerous established datasets. Comparing techniques from different categories turns out to be a non-trivial task due to the various configuration parameters that are hard to fine-tune, but have a significant impact on performance. We consider a plethora of parameter configurations, optimizing each technique with respect to recall and precision targets. Both schema-agnostic and schema-based settings are evaluated. The experimental results provide novel insights into the effectiveness, the time efficiency and the scalability of the considered techniques.
Finite-Sum Compositional Stochastic Optimization: Theory and Applications

This paper studies stochastic optimization for a sum of compositional functions, where the inner-level function of each summand is coupled with the corresponding summation index. We refer to this family of problems as ﬁnite-sum coupled compositional optimization (FCCO). It has broad applications in machine learning for optimizing non-convex or convex compositional measures/objectives such as average precision (AP), p -norm push, listwise ranking losses, neighborhood component analysis (NCA), deep survival analysis, deep latent variable models, softmax functions, and model agnostic meta-learning, which deserves ﬁner analysis. Yet, existing algorithms and analysis are restricted in one or other aspects. The contribution of this paper is to provide a comprehensive analysis of a simple stochastic algorithm for both non-convex and convex objectives. The key results are improved oracle complexities with the parallel speed-up by the moving-average based stochastic estimator with mini-batching. Our theoretical analysis also exhibits new insights for improving the practical implementation by sampling the batches of equal size for the outer and inner levels. Numerical experiments on AP maximization and p -norm push optimization corroborate some aspects of the theory.