Miracle-dz/UniFAR-FTUs huggingface dataset:
An Example-Centric Tool for Context-Driven Design of Biomedical Devices.

Engineering is one of the most global professions, with design teams developing technologies for an increasingly interconnected and borderless world. In order for engineering students to be proficient in creating viable solutions to the challenges faced by diverse populations, they must receive an experiential education in rigorous engineering design processes as well as identify the needs of customers living in communities radically different from their own. Acquainting students with the unique context and constraints of developing countries is difficult because of the breadth of pertinent considerations and the time constraints of academic semesters. This article describes a tool called Global Biomedical Device Design, or GloBDD, that facilitates simultaneous instruction in design methodology and global context considerations. GloBDD espouses an example-centric approach to educate students in the user-centered and context-driven design of biomedical devices. The tool employs real-world case studies to help students understand the importance of identifying external considerations early in the design process: issues like anthropometric, contextual, social, economic, and manufacturing considerations amongst many others. This article presents the rationale for the tool, its content and organization, and evaluation results from integration into a junior-level biomedical device design class. Results indicate that the tool engages students in design space exploration, leads them to making sound design decisions, and teaches them how to defend these decisions with a well-informed rationale.
Classifier Data Quality: A Geometric Complexity Based Method for Automated Baseline And Insights Generation

Testing Machine Learning (ML) models and AI-Infused Ap- plications (AIIAs), or systems that contain ML models, is highly challenging. In addition to the challenges of testing classical software, it is acceptable and expected that statistical ML models sometimes output incorrect results. A ma- jor challenge is to determine when the level of incorrectness, e.g., model accuracy or F1 score for classiﬁers, is acceptable and when it is not. In addition to business requirements that should provide a threshold, it is a best practice to require any proposed ML solution to out-perform simple baseline models, such as a decision tree. We have developed complexity measures, which quantify how difﬁcult given observations are to assign to their true class label; these measures can then be used to automatically determine a baseline performance threshold. These measures are superior to the best practice baseline in that, for a linear computation cost, they also quantify each observation’ classiﬁcation complexity in an explainable form, regardless of the classiﬁer model used. Our experiments with both numeric synthetic data and real natural language chatbot data demon- strate that the complexity measures effectively highlight data regions and observations that are likely to be misclassﬁed.
Off Environment Evaluation Using Convex Risk Minimization

Applying reinforcement learning (RL) methods on robots typically involves training a policy in simulation and deploying it on a robot in the real world. Because of the model mismatch between the real world and the simulator, RL agents deployed in this manner tend to perform suboptimally. To tackle this problem, researchers have developed robust policy learning algorithms that rely on synthetic noise disturbances. However, such methods do not guarantee performance in the target environment. We propose a convex risk minimization algorithm to estimate the model mismatch between the simulator and the target domain using trajectory data from both environments. We show that this estimator can be used along with the simulator to evaluate performance of an RL agents in the target domain, effectively bridging the gap between these two environments. We also show that the convergence rate of our estimator to be of the order of $n^{-1/4}$, where $n$ is the number of training samples. In simulation, we demonstrate how our method effectively approximates and evaluates performance on Gridworld, Cartpole, and Reacher environments on a range of policies. We also show that the our method is able to estimate performance of a 7 DOF robotic arm using the simulator and remotely collected data from the robot in the real world.
Two Stream Network for Stroke Detection in Table Tennis

This paper presents a table tennis stroke detection method from videos. The method relies on a two-stream Convolutional Neural Network processing in parallel the RGB Stream and its computed optical flow. The method has been developed as part of the MediaEval 2021 benchmark for the Sport task. Our contribution did not outperform the provided baseline on the test set but has performed the best among the other participants with regard to the mAP metric.
SlFHY3 and SlHY5 act compliantly to enhance cold tolerance through the integration of myo-inositol and light signaling in tomato.

Plants have evolved sophisticated regulatory networks to cope with dynamically changing light and temperature environments during day-night and seasonal cycles. However, the integration mechanisms of light and low temperature remain largely unclear. Here, we show that low red:far-red ratio (L-R/FR) induces FAR-RED ELONGATED HYPOCOTYL3 (SlFHY3) transcription under cold stress in tomato (Solanum lycopersicum). Reverse genetic approaches revealed that knocking out SlFHY3 decreases myo-inositol accumulation and increases cold susceptibility, while overexpressing SlFHY3 induces myo-inositol accumulation and enhances cold tolerance in tomato plants. SlFHY3 physically interacts with ELONGATED HYPOCOTYL5 (SlHY5) to promote the transcriptional activity of SlHY5 on MYO-INOSITOL-1-PHOSPHATE SYNTHASE 3 (SlMIPS3) and induce myo-inositol accumulation in tomato plants under cold stress. Disruption of SlHY5 and SlMIPS3 largely suppresses the cold tolerance of SlFHY3-overexpressing plants and myo-inositol accumulation in tomato. Furthermore, silencing of SlMIPS3 drastically reduces myo-inositol accumulation and compromises L-R/FR-induced cold tolerance in tomato. Together, our results reveal a crucial role of SlFHY3 in L-R/FR-induced cold tolerance in tomato, and unravel a novel regulatory mechanism whereby plants integrate dynamic environmental light signals and internal cues (inositol biosynthesis) to induce and control cold tolerance in tomato plants.
Application of Information Engineering in Enterprise Management

Information engineering has been widely used in project management. Implementation of enterprise information technology can overcome various inconveniences caused by geographic or time gap and make up the gap with partners or competitors. The purpose of this paper is to use the mature and advanced technology to design and realization of high reliability, scalability, flexibility, economy, and be able to meet the management requirements of modern enterprises, to improve enterprise information-sharing capabilities, and to support enterprises in high-level enterprise information management platform.
Predicting Noncontact Lower Limb Injury Using Lumbar Morphology in Professional Australian Football and Rugby League Players

ABSTRACT Introduction Noncontact lower limb injuries are common within the Australian Football League (AFL) and National Rugby League (NRL). Smaller (<8.5 cm2) lumbar multifidus at the fifth vertebra (LM L5) and larger (>8.2 cm2) quadratus lumborum (QL) cross-sectional area (CSA) have been associated with increased noncontact lower limb injury risk in AFL players. These associations have not been explored in an NRL cohort. This study will attempt to replicate previous research findings by confirming that muscle morphology is associated with noncontact lower limb injury. Methods AFL (n = 87) and NRL (n = 151) players underwent LM L2–L5 and QL CSA ultrasound measures during preseason. Each club’s medical staff reported all noncontact lower limb injuries sustained in the subsequent regular season. LM and QL CSA, age, body mass index, and noncontact lower limb injuries were analyzed using multivariable logistic regression. Results Seventy-two players sustained a noncontact lower limb injury in the 2020 regular season (AFL = 21, NRL = 51). The multivariable logistic regression (odds ratio (OR) = 1.36; 95% confidence interval (CI), 1.02–1.85; P = 0.038) identified AFL players with larger QL CSA at increased risk of sustaining a noncontact lower limb injury during the regular season, but no relationship was found for LM CSA and noncontact lower limb injuries in the AFL (OR = 1.01; 95% CI, 0.36–2.78; P = 0.591) or NRL (OR = 0.63; 95% CI, 0.29–1.33; P = 0.149). Conclusions AFL players who sustained regular season noncontact lower limb injuries had larger QL CSA in preseason tests. No significant associations between either LM L5 CSA or LM L5 to QL ratio and regular season noncontact lower limb injuries were found.
Optimalmould - part II: Global optimization of the injection moulding cycle time

The time required to produce a plastic part is a key issue in injection moulding process as it strongly determine the cost per part. Several approaches have been proposed to address this problem. However they do not cover all aspects related to the complete cycle time of the injection moulding process, only focusing on steps such as filling time, cooling time and packing time. This paper presents a global optimisation strategy for the injection moulding cycle time, covering all time steps related to the injection moulding process, including a novel mathematical model to predict the ejection time. An industrial case study was considered to validate the proposed approach. The obtained results are very close to the experimental ones.
Open-Vocabulary Image Segmentation

We design an open-vocabulary image segmentation model to organize an image into meaningful regions indicated by arbitrary texts. We identify that recent open-vocabulary models [22,38] can not localize visual concepts well despite recognizing what are in an image. We argue that these models miss an important step of visual grouping, which organizes pixels into groups before learning visual-semantic alignments. We propose OpenSeg to address the above issue. First, it learns to propose segmentation masks for possible organizations. Then it learns visual-semantic alignments by aligning each word in a caption to one or a few predicted masks. We ﬁnd the mask representations are the key to support learning from captions, making it possible to scale up the dataset and vocabulary sizes. Our work is the ﬁrst to perform zero-shot transfer on holdout segmentation datasets. We set up two strong baselines by applying class activation maps or ﬁne-tuning with pixel-wise labels on a pre-trained ALIGN model [22]. OpenSeg outperforms these baselines by 3.4 mIoU on PASCAL-Context (459 classes) and 2.7 mIoU on ADE-20k (847 classes).
Model sketching by abstraction refinement for lifted model checking

In this work, we show how the use of verification and analysis techniques for model families (software product lines) with numerical features provides an interesting technique to synthesize complete models from sketches (i.e. partial models with holes). In particular, we present an approach for synthesizing Promela model sketches using variability-specific abstraction refinement for lifted (family-based) model checking.
The Impact of Information Technologies on Air Transportation

The Air Transportation System and several key subsystems including the Aircraft, Airline, and Air Trmc Management are modeled as interacting control loops. The impact of Information Technologies on each of these subsystems is evaluated through the performance of these control loops. Information technologies are seen to have a significant impact on the safety, efficiency, capability, capacity, environmental impact and financial performance of the Air Transportation System and its components. This growth in traffic has been accompanied by an increase in safety and cost efficiency. The world commercial jet aircraft fatal or hull loss accident rate decreased by a factor of 5 from 1972 to 2002 and by more than 25 from the entry of commercial jet aircraft in the early 1960's. ~ Cost efficiency has also increased. The average Cost per Available Seat Mile (CASM) for U.S. Airlines has decreased by over 40% since the late 1970's. ~ It is interesting to note that this period of strong air transportation performance correlates with both the deregulation of the U. S Airline Industry in 1978 and the growth in Information Technologies. The commercial introduction of the microprocessor was in 197 1 and the introduction of the Intel 8086 processor (which powered most personal computers) occurred in 1978 (Ref. 4) the year the US Auline industry was deregulated. This paper explores the impact of Information Technologies on Air Transportation and attempts to identify key Information Technology trends emergent issues and future opportunities for Air Transportation. In order to limit it's scope, this papa will focus primarily on Information Technologies that influence operational closed loop feedback control at various levels. It w~ll not address Information Technologies such as CFD and computational modeling which have had a significant impact on vehicle design.
A Case Study on Optimization of Warehouses

In warehouses, order picking is known to be the most labor-intensive and costly task in which the employees account for a large part of the warehouse performance. Hence, many approaches exist, that optimize the order picking process based on diverse economic criteria. However, most of these approaches focus on a single economic objective at once and disregard ergonomic criteria in their optimization. Further, the influence of the placement of the items to be picked is underestimated and accordingly, too little attention is paid to the interdependence of these two problems. In this work, we aim at optimizing the storage assignment and the order picking problem within mezzanine warehouse with regards to their reciprocal influence. We propose a customized version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing the storage assignment problem as well as an Ant Colony Optimization (ACO) algorithm for optimizing the order picking problem. Both algorithms incorporate multiple economic and ergonomic constraints simultaneously. Furthermore, the algorithms incorporate knowledge about the interdependence between both problems, aiming to improve the overall warehouse performance. Our evaluation results show that our proposed algorithms return better storage assignments and order pick routes compared to commonly used techniques for the following quality indicators for comparing Pareto fronts: Coverage, Generational Distance, Euclidian Distance, Pareto Front Size, and Inverted Generational Distance. Additionally, the evaluation regarding the interaction of both algorithms shows a better performance when combining both proposed algorithms.
DarkGates: A Hybrid Power-Gating Architecture to Mitigate the Performance Impact of Dark-Silicon in High Performance Processors

To reduce the leakage power of inactive (dark) silicon components, modern processor systems shut-off these components' power supply using low-leakage transistors, called power-gates. Unfortunately, power-gates increase the system's power-delivery impedance and voltage guardband, limiting the system's maximum attainable voltage (i.e., Vmax) and, thus, the CPU core's maximum attainable frequency (i.e., Fmax). As a result, systems that are performance constrained by the CPU frequency (i.e., Fmax -constrained), such as high-end desktops, suffer significant performance loss due to power-gates.To mitigate this performance loss, we propose DarkGates, a hybrid system architecture that increases the performance of Fmax -constrained systems while fulfilling their power efficiency requirements. DarkGates is based on three key techniques: i) bypassing on-chip power-gates using package-level resources (called bypass mode), ii) extending power management firmware to support operation either in bypass mode or normal mode, and iii) introducing deeper idle power states.We implement DarkGates on an Intel Skylake microprocessor for client devices and evaluate it using a wide variety of workloads. On a real 4-core Skylake system with integrated graphics, DarkGates improves the average performance of SPEC CPU2006 workloads across all thermal design power (TDP) levels (35W–91W) between 4.2% and 5.3%. DarkGates maintains the performance of 3DMark workloads for desktop systems with TDP greater than 45W while for a 35W-TDP (the lowest TDP) desktop it experiences only a 2% degradation. In addition, DarkGates fulfills the requirements of the ENERGY STAR and the Intel Ready Mode energy efficiency benchmarks of desktop systems.
Reconstructing public engagement from social media content volume

The consumption of news produces uneven social reactions. In most cases, people share information and discuss their opinions; public interest remains therefore bounded to the field of debate. A few cases, in contrast, fuel up the collective sensibility and give rise to social movements. To explain the dynamics that underlie the emergence of these reactive states, we set up a simple mathematical model for public interest in terms of media coverage and social interactions. We test the model on a series of events related to violence in the US during 2020. The volume of tweets and retweets is used as a proxy of public interest, and the volume of news as a proxy of media coverage. We show that the model succesfully fits the data and, crucially, allows inferring a measure of public engagement that correlates with an independent corpus of human mobility data. Our findings suggest that this low-dimensional model captures the basic ingredients that regulate social responses of very different magnitudes, which are indeed capable of ignite social mobilizations.
Evaluating categorical encoding methods on a real credit card fraud detection database

Correctly dealing with categorical data in a supervised learning context is still a major issue. Furthermore, though some machine learning methods embody builtin methods to deal with categorical features, it is unclear whether they bring some improvements and how do they compare with usual categorical encoding methods. In this paper, we describe several well-known categorical encoding methods that are based on target statistics and weight of evidence. We apply them on a large and real credit card fraud detection database. Then, we train the encoded databases using state-of-the-art gradient boosting methods and evaluate their performances. We show that categorical encoding methods generally bring substantial improvements with respect to the absence of encoding. The contribution of this work is twofold: (1) we compare many state-of-the-art"lite"categorical encoding methods on a large scale database and (2) we use a real credit card fraud detection database.
A Stochastic Bregman Primal-Dual Splitting Algorithm for Composite Optimization

We study a stochastic first order primal-dual method for solving convex-concave saddle point problems over real reflexive Banach spaces using Bregman divergences and relative smoothness assumptions, in which we allow for stochastic error in the computation of gradient terms within the algorithm. We show ergodic convergence in expectation of the Lagrangian optimality gap with a rate of O(1/k) and that every almost sure weak cluster point of the ergodic sequence is a saddle point in expectation under mild assumptions. Under slightly stricter assumptions, we show almost sure weak convergence of the pointwise iterates to a saddle point. Under a relative strong convexity assumption on the objective functions and a total convexity assumption on the entropies of the Bregman divergences, we establish almost sure strong convergence of the pointwise iterates to a saddle point. Our framework is general and does not need strong convexity of the entropies inducing the Bregman divergences in the algorithm. Numerical applications are considered including entropically regularized Wasserstein barycenter problems and regularized inverse problems on the simplex.
Open-flow mixing and transfer operators

We study finite-time mixing in time-periodic open flow systems. We describe the transport of densities in terms of a transfer operator, which is represented by the transition matrix of a finite-state Markov chain. The transport processes in the open system are organized by the chaotic saddle and its stable and unstable manifolds. We extract these structures directly from leading eigenvectors of the transition matrix. We use different measures to quantify the degree of mixing and show that they give consistent results in parameter studies of two model systems. This article is part of the theme issue ‘Mathematical problems in physical fluid dynamics (part 1)’.
Reliable Assessment of Swine Renal Fibrosis Using Quantitative Magnetization Transfer Imaging

Objectives Quantitative magnetization transfer (qMT) is useful for measurement of murine renal fibrosis at high and ultrahigh field strengths. However, its utility at clinical field strengths and in human-like kidneys remains unknown. We tested the hypothesis that qMT would successfully detect fibrosis in swine kidneys with unilateral renal artery stenosis (RAS) at 3.0 T. Methods The qMT protocol is composed of MT scans with variable flip angles and offset frequencies, and of B0, B1, and T1 mapping. Pigs were scanned 10 weeks after RAS or control. A 2-pool model was used to fit the bound pool fraction f of the renal cortex (CO) and outer medulla (OM). Then qMT-derived f in 5 normal and 10 RAS pigs was compared with histological fibrosis determined using Masson's trichrome staining and to renal perfusion assessed with computed tomography. Results The qMT 2-pool model provided accurate fittings of data collected on swine kidneys. Stenotic kidneys showed significantly elevated f in both the CO (9.8% ± 2.7% vs 6.4% ± 0.9%, P = 0.002) and OM (7.6% ± 2.2% vs 4.7% ± 1.1%, P = 0.002), as compared with normal kidneys. Histology-measured renal fibrosis and qMT-derived f correlated directly in both the cortex (Pearson correlation coefficient r = 0.93, P < 0.001) and OM (r = 0.84, P = 0.002), and inversely with stenotic kidney perfusion (r = 0.85, P = 0.002). Conclusions This study demonstrates the feasibility of qMT for measuring fibrosis in human-like swine kidneys, and the association between tissue macromolecule content and renal perfusion. Therefore, qMT may be useful as a tool for noninvasive assessment of renal fibrosis in subjects with RAS at clinical field strengths.
Lifting C semantics for dataflow optimization

C is the lingua franca of programming and almost any device can be programmed using C. However, programming modern heterogeneous architectures such as multi-core CPUs and GPUs requires explicitly expressing parallelism as well as device-specific properties such as memory hierarchies. The resulting code is often hard to understand, debug, and modify for different architectures. We propose to lift C programs to a parametric dataflow representation that lends itself to static data-centric analysis and enables automatic high-performance code generation. We separate writing code from optimizing for different hardware: simple, portable C source code is used to generate efficient specialized versions with a click of a button. Our approach can identify parallelism when no other compiler can, and outperforms a bespoke parallelized version of a scientific proxy application by up to 21%.