Miracle-dz/UniFAR-FTUs huggingface dataset:
Urinary biomarkers of exposure to toxic and essential elements: A comparison of infants fed with human milk or formula

Background: Early-life exposure to nonessential (toxic) and essential trace elements can influence child development. Although infant formula powders and the water used to reconstitute them can contain higher concentrations of many elements compared with human milk, the influence of feeding mode on reliable biomarkers of infant exposure has rarely been demonstrated. Methods: We evaluated associations between urinary biomarkers and feeding mode (exclusively human milk, exclusively formula, or combination-fed) for four toxic (arsenic, cadmium, nickel, and uranium) and three essential elements (cobalt, molybdenum, and selenium) using general linear models. Results: A total of 462 participants from the rural New Hampshire Birth Cohort Study were on average 6 weeks old between July 2012 and March 2019 and had urine samples, 3-day food diaries, and relevant covariate data available. In adjusted models, urinary arsenic was 5.15 (95% confidence interval = 4.04, 6.58), molybdenum was 19.02 (14.13–25.59), and selenium was 1.51 (1.35–1.68) times higher in infants fed exclusively with formula compared with infants fed exclusively with human milk. By contrast, urinary uranium was 0.59 (0.46–0.75) and cobalt was 0.78 (0.65–0.95) times lower with formula feeding than human milk feeding. Conclusion: Our findings suggest that infant exposure to several potentially toxic elements varies by feeding mode, as concentrations of reliable urinary biomarkers were higher with formula or human milk, depending on the element. Importantly, exposure to arsenic increased with household tap water arsenic regardless of feeding mode, suggesting that all infants could be at risk in populations with high concentrations of arsenic in drinking water.
Pronghorn: Effective Checkpoint Orchestration for Serverless Hot-Starts

Serverless computing allows developers to deploy and scale stateless functions in ephemeral workers easily. As a result, serverless computing has been widely used for many applications, such as computer vision, video processing, and HTML generation. However, we find that the stateless nature of serverless computing wastes many of the important benefits modern language runtimes have to offer. A notable example is the extensive profiling and Just-in-Time (JIT) compilation effort that runtimes implement to achieve acceptable performance of popular high-level languages, such as Java, JavaScript, and Python. Unfortunately, when modern language runtimes are naively adopted in serverless computing, all of these efforts are lost upon worker eviction. Checkpoint-restore methods alleviate the problem by resuming workers from snapshots taken after initialization. However, production-grade language runtimes can take up to thousands of invocations to fully optimize a single function, thus rendering naive checkpoint-restore policies ineffective. This paper proposes Pronghorn, a snapshot serverless orchestrator that automatically monitors the function performance and decides (1) when it is the right moment to take a snapshot and (2) which snapshot to use for new workers. Pronghorn is agnostic to the underlying platform and JIT runtime, thus easing its integration into existing runtimes and worker deployment environments (container, virtual machine, etc.). On a set of representative serverless benchmarks, Pronghorn provides end-to-end median latency improvements of 37.2% across 9 out of 13 benchmarks (20-58% latency reduction) when compared to state-of-art checkpointing policies.
Beyond Boundaries: A Novel Data-Augmentation Discourse for Open Domain Generalization

The problem of Open Domain Generalization (ODG) is multifaceted, encompassing shifts in domains and labels across all source and target domains. Existing approaches have encountered challenges such as style bias towards training domains, insufficient feature-space disentanglement to highlight semantic features, and discriminativeness of the latent space. Additionally, they rely on a confidence-based target outlier detection approach, which can lead to misclassifications when target open samples visually align with the source domain data. In response to these challenges, we present a solution named ODG-N ET . We aim to create a direct open-set classifier within a discriminative , unbiased , and disentangled semantic embedding space. To enrich data density and diversity, we introduce a generative augmentation framework that produces style-interpolated novel domains for closed-set images and novel pseudo-open images by interpolating the contents of paired training images . Our augmentation strategy skillfully utilizes disentangled style and content information to synthesize images effectively. Furthermore, we tackle the issue of style bias by representing all images in relation to all source domain properties, which effectively accentuates complementary visual features. Consequently, we train a multi-class semantic object classifier, incorporating both closed and open class classification capabilities, along with a style classifier to identify style primitives. The joint use of style and semantic classifiers facilitates the disentanglement of the latent space, thereby enhancing the generalization performance of the semantic classifier. To ensure discriminativeness in both
Cookie Consent Has Disparate Impact on Estimation Accuracy

Cookies are designed to enable more accurate identiﬁcation and tracking of user behavior, in turn allowing for more personalized ads and better performing ad campaigns. Given the additional information that is recorded, questions related to privacy and fairness naturally arise. How does a user’s consent decision inﬂuence how much the system can learn about their demographic and tastes? Is the impact of a user’s consent decision on the recommender system’s ability to learn about their latent attributes uniform across demographics? We investigate these questions in the context of an engagement-driven recommender system using simulation. We empirically demonstrate that when consent rates exhibit demographic-dependence, user consent has a disparate impact on the recommender agent’s ability to estimate users’ latent attributes. In particular, we ﬁnd that when consent rates are demographic-dependent, a user disagreeing to share their cookie may counter-intuitively cause the recommender agent to know more about the user than if the user agreed to share their cookie. Furthermore, the gap in base consent rates across demographics serves as an ampliﬁer: users from the lower consent rate demographic who agree to cookie sharing generally experience higher estimation errors than the same users from the higher consent rate demographic, and conversely for users who choose to disagree to cookie sharing, with these differences increasing in consent rate gap. We discuss the need for new notions of fairness that encourage consistency between a user’s privacy decisions and the system’s ability to estimate their latent attributes.
On-the-ﬂy Deformations for Keyword Spotting

Modern Keyword Spotting systems rely on deep learning approaches to build eﬀective neural networks which provide state-of-the-art results. Despite their evident success, these deep models have proven to be sensitive with respect to the input images; a small deformation, almost indistinguishable to the human eye, may considerably alter the resulting retrieval list. To address this issue, we propose a novel “on-the-ﬂy” approach which deforms an input image to better match the query image, aiming to stabilize the aforementioned sensitivity. Results on the IAM dataset verify the eﬀectiveness of the proposed method, which out-performs existing Query-by-Example approaches. Code is available at https://github.com/georgeretsi/defKWS/ .
SST_GPU: An Execution -Driven CUDA Kernel Scheduler and Streaming-Multiprocessor Compute Model.

Programmable accelerators have become commonplace in modern computing systems. Advances in programming models and the availability of massive amounts of data have created a space for massively parallel acceleration where the context for thousands of concurrent threads are resident on-chip. These threads are grouped and interleaved on a cycle-by-cycle basis among several massively parallel computing cores. The design of future supercomputers relies on an ability to model the performance of these massively parallel cores at scale. To address the need for a scalable, decentralized GPU model that can model large GPUs, chipletbased GPUs and multi-node GPUs, this report details the first steps in integrating the open-source, execution driven GPGPU-Sim into the SST framework. The first stage of this project, creates two elements: a kernel scheduler SST element accepts work from SST CPU models and schedules it to an SM-collection element that performs cycle-by-cycle timing using SSTs MemHierarchy to model a flexible memory system.
Causation, Norms, and Cognitive Bias

Extant research has shown that ordinary causal judgments are sensitive to normative factors. For instance, agents who violate a norm are standardly deemed more causal than norm-conforming agents in identical situations. In this paper, we explore two competing explanations for the Norm Effect: the Responsibility View and the Bias View. According to the former, the Norm Effect arises because ordinary causal judgment is intimately intertwined with moral responsibility. According to the alternative view, the Norm Effect is the result of a blame-driven bias. In a series of five preregistered experiments (N = 2688), we present evidence in favour of the Bias View. In particular, and against predictions made by the Responsibility View, we show that participants deem agents who violate nonpertinent or silly norms – norms that do not relate to the outcome at hand – as more causal, and that this effect cannot be explained in terms of participants ascribing foreknowledge, desire, or foreseeability of harm to the norm-violating agent. We close with a discussion of these findings and point to important implications for the just assessment of proximate cause in the law.
Using Deep Learning Technology for Real-Time Sign Language Detection and Recognition at Public Libraries in Egypt : An Experimental Study

: Sign Language is the most expressive way for communication between deaf and hearing-impaired people, where information is majorly conveyed through the hand gestures. Despite this, one of the most issues that facing people and public domains that are outside the deaf community is understanding sign language, they are need an interpreter to understand this language. Technologies including deep learning and computer vision aid this issue by providing several technical apps and other platforms. In this paper, The study proposes a real-time sign language detection model using TensorFlow and OpenCV. The main aim of this study is developing model that recognize sign language, and that can be used offline. Through this model, the study can detect a common words and sentences that are expressed by hand gestures, like among American and Arabic sign languages like good (thumbs up), bad (thumbs down), thanks, and live long. This study is belonging to the experimental and applicable research. The results of this study were that the model succeed to detect the hand gestures and recognize it with accuracy 100 %.
The Role of Transaction Cost Economics in Information The Role of Transaction Cost Economics in Information Technology Outsourcing Research: A Meta-Analysis of the Choice Technology Outsourcing Research: A Meta-Analysis of the Choice of Contract Type of Contract Type

Two recent reviews report that the empirical findings in information technology outsourcing (ITO) research are frequently inconsistent with the prevailing dominant analytical framework of transaction cost economics (TCE). While employing similar methodologies, the two reviews propose different strategies to resolve the inconsistencies. One is to improve the methodological rigor, specifically, the operationalization of TCE constructs. The other is to abandon TCE in favor of a new analytical framework. This paper presents a meta-analysis of the empirical findings on the choice of contract type as a function of task uncertainty. The results support both strategies. Refining the operationalization of TCE constructs, specifically of task uncertainty, would have improved the reliability of findings on TCE-based relationships between task uncertainty and the choice of contract type. However, independent of such methodological improvements, TCE is of limited relevance in recent ITO research for predicting the choice of contract type. Generalizing these findings, we conclude that ITO research requires a new analytical framework to further develop the theory of ITO and to provide sound guidance to the ITO industry.
Origin of seismic repeaters in a deep mine: what do we learn from in-situ investigation coupling geology, geomechanics and geophysics?

: Seismic repeaters (i.e. seismic events with highly similar waveforms) have been observed in the deep mine of Garpenberg, Sweden. In natural seismology, repeaters are commonly associated with the presence of aseismic slip (i.e. creep) on fault planes, loading seismogenic asperities. This raises questions regarding what these asperities and creeping zone consist of and how they behave from a mechanical point of view. A local monitoring network has been installed in situ, close to the source area of one family of repeaters at 1km depth, with the objective of identifying the origin of these repeaters. The network comprises boreholes, geophones and strain cells. We show that the presence of a fault plane with creeping portions and an asperity is compatible with field data. We also discuss the difficulty of clearly identifying the origin of the repeaters, as we couldn’t reach the seismic asperity itself, even with such a local network.
PROSPECTIVE COMPARATIVE STUDY TO EVALUATE THE FUNCTIONAL OUTCOME FOLLOWING PHILOS PLATING VERSUS TRANSCUTANEOUS FIXATION IN THE FRACTURES OF PROXIMAL HUMERUS IN ELDERLY PATIENTS

Background: Fractures of the proximal humerus represent the second most common (29.1%) fracture type in the upper extremity. The choice of treatment depends on the pattern of the fracture, the quality of the bone encountered, the patient’s requirement, and the surgeon’s familiarity with the procedures. various modalities of fixations have evolved for the proximal humerus fractures (percutaneous pinning, tension band wiring, plating, inter lock nailing, and arthroplasty). The available literature is flooded with researches having equivocal results regarding use of the implants, with their various advantages and disadvantages; which prompted us to do this comparative research. Aims: To evaluate the functional outcome of the PHILOS plating versus transcutaneous fixation in displaced fracture of proximal humerus in elderly (> 50 years), To assess the stability of fixation in osteoporotic proximal humerus and to preserve the biological integrity of Humeral head and to secure anatomical reduction with angular stability with multiple k wires. Material and Methods: A total number of 30 patients were selected for this study. The first patient was selected for group A (ODD number) and the second patient for Group B (EVEN number) by fulfilling their exclusion and inclusion criteria. 15 patients (Group A) were operated on with proximal Humerus internal locking system (PHILOS) and 15 patients (Group B) with Transcutaneous K wires fixation. Out of 30 patients, 4 patients with 2-part fractures were treated with PHILOS and 4 with K-wire. 8 patients with 3-part fractures were treated with PHILOS and 9 with K-wire. 3 with Neer's 4-part were treated with PHILOS and 2 with K-wire. Regular follow up was done by using NEER SCORE at every week up to 6 weeks and After 6 weeks fortnightly up to 12 weeks was done
EFFECTIVENESS OF VIDEO ASSISTED DEMONSTRATION VERSUS CONVENTIONAL DEMONSTRATION IN TEACHING HEMATOLOGY PRACTICAL EXERCISES TO PHASE II MBBS STUDENTS

Background: Conventional demonstrations of practical exercises gives a live experience for the students but the crowding often makes it difficult to watch the procedures properly. In this era of e-learning using videos to complement teaching is considered more effective than traditional teaching methods and the advances in technology made video demonstrations easier. This study was done to find the effectiveness of the use of video demonstrations in teaching hematology practical exercises. The primary objective was to compare the effectiveness of Video assisted demonstrations over conventional demonstrations for teaching hematology practical exercises to Phase II MBBS students as assessed by post test scores and the secondary objective was to find out the perception of the students regarding the use of Video assisted demonstrations in teaching hematology practical exercises. Materials and Methods: This is a quasi-experimental study done among Phase II MBBS students of Travancore Medical College, Kollam. A total of 70 students were selected and divided into 2 groups of 35 students each. One group was taught Hematology practical exercises using conventional methods and the other group was taught the same using Video assisted demonstrations. 4 interventions with 8 exposures were conducted for both the groups over a period of one month. At the completion of the interventions, Objective structured practical exams (0SPE) were conducted for both the groups followed by which there was a cross-over for both the batches. The feedback from the students regarding the two instructional methods were also assessed. Results: The data collected comprised of the spotter exam scores and feed-back by Likert’s scale were analysed using SPSS software. The mean scores of the OSPE exam showed that the students taught by video assisted demonstrations performed the exercises slightly better than the students that were taught by conventional demonstration methods. This improvement was statistically significant with p value <0.05. The feedback from students reflected that most students preferred video demonstrations as a teaching method for hematology practical exercises. Conclusion: Video assisted demonstrations are effective teaching learning methods for teaching hematology practical exercises to Phase II MBBS students.
Time-Domain Physical Limitations on the Response of a Class of Time-Invariant Systems

—We propose time-domain (TD) physical limitations on the system function of a linear, time-invariant system. Two approaches to derive TD physical bounds are presented. The ﬁrst approach is based on Cauchy’s integral of the Laplace transform of a causal transfer function, while the second one relies on an integral representation of positive real (PR) functions. The analysis yields straightforward bounds on the output signal that can be readily used to benchmark the performance of time-varying electromagnetic (EM) structures. The results are illustrated on selected transmission-line problems and on the pulsed EM plane wave scattering by thin high-contrast sheets.
A NEW ERA OF BRAIN TUMOR MANAGEMENT: MRS AS A NON-INVASIVE DIAGNOSTIC TOOL

Background: The primary goal is to improve the diagnosis and treatment of brain tumors by using MRI to identify key characteristics. This includes identifying the conventional MRI characteristic findings of brain tumors, evaluating the accuracy of MRI spectroscopy in grading brain tumors, determining the degree of infiltration of the tumor into surrounding tissues, and identifying biochemical markers of brain tumors using MR spectroscopy. Achieving these objectives is critical to developing more effective treatments for brain tumors and improving patient outcomes. By accurately identifying the characteristics of brain tumors using MRI, one can better diagnose and monitor the progression of the disease, leading to more targeted and effective treatments. Materials and Methods: This study aimed to evaluate brain tumors using 3T MRS in a prospective cross-sectional study conducted at the Department of Radiology in Narayana Medical College Hospital from January 2021 to August 2022. The study population consisted of thirty patients who underwent MRI brain with clinically suspected metastasis or brain tumors. MRS imaging was performed using a 3T MRI machine with automatic dynamic high-order shimming and chemical-shift selective water suppression, and the relative signal strengths of metabolites were evaluated. The data was analysed using SPSS, and cases were followed clinically and radiologically as indicated. Result: The current study involved 35 instances. Tumor grades were categorized according to WHO criteria and included 5.7% of grade I, 17.1% of grade II, 48.6% of grade III, and 28.6% of grade IV tumors. The analysis of metabolite ratios indicated that NAA/Cr levels were similar in HGG and isolated metastatic lesions in the intratumoral and contralateral regions. However, individuals with a single metastatic lesion in the pertumoral region had considerably higher NAA/Cr levels. MRS correctly identified 26 cases of HGG and 6 cases with other pathologies (LGG and metastatic lesions) but misidentified 3 cases without HGG as having HGG. The study reported the diagnostic accuracy of MRS as 85.7% with a sensitivity of 92.9%, specificity of 57.1%, positive predictive value of 89.7%, and negative predictive value of 66.7%. Conclusion: The study found that MRS had a diagnostic accuracy of 85.7%, with a sensitivity of 92.9% and specificity of 5.17%. Patients with a single metastatic lesion had higher levels of NAA/Cr, while those with HGG had higher levels of Cho/Cr and Cho/NAA compared to solitary lesions. Patients with HGG also had higher levels of rCBV in the peritumoral region. The combination of MRS and brain SPECT provided accurate pre-surgical diagnosis and resolved a significant diagnostic puzzle. A multi-modality approach to brain tumor diagnosis could benefit both patients and doctors involved in the treatment plan.
Probabilistic Analysis of Rumor Spreading Time

The context of this work is the well studied dissemination of information in large scale distributed networks through pairwise interactions. This problem, originally called rumor mongering , and then rumor spreading has mainly been investigated in the synchronous model. This model relies on the assumption that all the nodes of the network act in synchrony, that is, at each round of the protocol, each node is allowed to contact a random neighbor. In this paper, we drop this assumption under the argument that it is not realistic in large scale systems. We thus consider the asynchronous variant, where at random times, nodes successively interact by pairs exchanging their information on the rumor. In a previous paper, we performed a study of the total number of interactions needed for all the nodes of the network to discover the rumor. While most of the existing results involve huge constants that do not allow us to compare different protocols, we provided a thorough analysis of the distribution of this total number of interactions together with its asymptotic behavior. In this paper we extend this discrete time analysis by solving a conjecture proposed previously and we consider the continuous time case, where a Poisson process is associated to each node to determine the instants at which interactions occur. The rumor spreading time is thus more realistic since it is the real time needed for all the nodes of the network to discover the rumor. Once again, as most of the existing results involve huge constants, we provide tight bound and equivalent of the complementary distribution of the rumor spreading time. We also give the exact asymptotic behavior of the complementary distribution of the rumor spreading time around its expected value when the number of nodes tends to inﬁnity.