Miracle-dz/UniFAR-FTUs huggingface dataset:
Occupational Well-Being and Some Contextual and Demographic Characteristics: A Multivariate Analysis

The purpose of this study was twofold: first, to determine the Turkish(cid:561) elementary(cid:561) science(cid:561) teachers(cid:514)(cid:561) occupational(cid:561) well -being profile(cid:561)and(cid:561)second(cid:496)(cid:561)to(cid:561)investigate(cid:561)the(cid:561)relation(cid:561)of(cid:561)science(cid:561)teachers(cid:514)(cid:561) occupational well-being to some contextual and demographic characteristics. The contextual variables included class size, years of teaching experience and weekly course hours, while demographic characteristics included gender, graduated faculty, marital status, and having children. Moreover, occupational well-being was examined in terms of job satisfaction and burnout (i.e. emotional exhaustion and personal accomplishment). 376 elementary science teachers that were randomly selected across the Turkey participated in the study. Descriptive statistics and canonical correlation analysis were utilized to analyze the data. Results showed that Turkish elementary science teachers experience low level of Emotional exhaustion and high level of personal accomplishment and job satisfaction. Additionally, it was found that as class size and weekly course hour increase, elementary science teachers tend to experience more emotional exhaustion but less job satisfaction. Teachers having children appeared to experience more emotional exhaustion but less job satisfaction. Moreover, a positive relationship was found between teaching experience and occupational well-being (i.e. job satisfaction and personal accomplishment). Female teachers appeared to experience more personal accomplishment and job satisfaction than male teachers. Similarly, examined the difference among Turkish citizen teachers‘ burnout levels in and by using Shirom-Melamed Burnout Measure (SMBM). They found that teachers working in have significantly higher burnout level compared to teachers in Turkey and Germany while there was no significant difference was found between Germany and Turkey in terms of burnout level. These findings show that Turkish teachers have positive attitudes toward their job and satisfy with working as a teacher as much as those teachers who are working in developed countries. Therefore, it seems quite important to find out the fundemantel context and demographic
Pulser: An open-source package for the design of pulse sequences in programmable neutral-atom arrays

Programmable arrays of hundreds of Rydberg atoms have recently enabled the exploration of remarkable phenomena in many-body quantum physics. In addition, the development of high-fidelity quantum gates are making them promising architectures for the implementation of quantum circuits.We present here Pulser, an open-source Python library for programming neutral-atom devices at the pulse level. The low-level nature of Pulser makes it a versatile framework for quantum control both in the digital and analog settings. The library also contains simulation routines for studying and exploring the outcome of pulse sequences for small systems.
A Review on Bio-Cyber Interfaces for Intrabody Molecular Communications Systems

The recent advancements in bio-engineering and wireless communications systems have motivated researchers to propose novel applications for telemedicine, therapeutics and human health monitoring. For instance, through wireless medical telemetry a healthcare worker can remotely measure biological signals and control certain processes in the organism required for the maintenance of the patient's health state. This technology can be further extended to use Bio-Nano devices to promote a real-time monitoring of the human health and storage of the gathered data in the cloud. This brings new challenges and opportunities for the development of biosensing network, which will depend on the extension of the current intrabody devices functionalities. In this paper we will cover the recent progress made on implantable micro-scale devices and introduce the perspective of improve them to foster the development of new theranostics based on data collected at the nanoscale level.
Studying the Consistency and Composability of Lottery Ticket Pruning Masks

Magnitude pruning is a common, effective technique to identify sparse subnetworks at little cost to accuracy. In this work, we ask whether a particular architecture's accuracy-sparsity tradeoff can be improved by combining pruning information across multiple runs of training. From a shared ResNet-20 initialization, we train several network copies (\emph{siblings}) to completion using different SGD data orders on CIFAR-10. While the siblings' pruning masks are naively not much more similar than chance, starting sibling training after a few epochs of shared pretraining significantly increases pruning overlap. We then choose a subnetwork by either (1) taking all weights that survive pruning in any sibling (mask union), or (2) taking only the weights that survive pruning across all siblings (mask intersection). The resulting subnetwork is retrained. Strikingly, we find that union and intersection masks perform very similarly. Both methods match the accuracy-sparsity tradeoffs of the one-shot magnitude pruning baseline, even when we combine masks from up to $k = 10$ siblings.
Driver Positioning and Incentive Budgeting with an Escrow Mechanism for Ridesharing Platforms

Drivers on the Lyft ride-share platform do not always know where the areas of supply shortage are in real time. This lack of information hurts both riders trying to find a ride and drivers trying to determine how to maximize their earnings opportunities. Lyft’s Personal Power Zone (PPZ) product helps the company to maintain high levels of service on the platform by influencing the spatial distribution of drivers in real time via monetary incentives that encourage them to reposition their vehicles. The underlying system that powers the product has two main components: (1) a novel “escrow mechanism” that tracks available incentive budgets tied to locations within a city in real time, and (2) an algorithm that solves the stochastic driver-positioning problem to maximize short-run revenue from riders’ fares. The optimization problem is a multiagent dynamic program that is too complicated to solve optimally for our large-scale application. Our approach is to decompose it into two subproblems. The first determines the set of drivers to incentivize and where to incentivize them to position themselves. The second determines how to fund each incentive using the escrow budget. By formulating it as two convex programs, we are able to use commercial solvers that find the optimal solution in a matter of seconds. Rolled out to all 320 cities in which Lyft operates in a little more than a year, the system now generates millions of bonuses that incentivize hundreds of thousands of active drivers to optimally position themselves in anticipation of ride requests every week. Together, the PPZ product and its underlying algorithms represent a paradigm shift in how Lyft drivers drive and generate earnings on the platform. Its direct business impact has been a 0.5% increase in incremental bookings, amounting to tens of millions of dollars per year. In addition, the product has brought about significant improvements to the driver and rider experience on the platform. These include statistically significant reductions in pick-up times and ride cancellations. Finally, internal surveys reveal that the vast majority of drivers prefer PPZs over the legacy system.
Test Smell Detection Tools: A Systematic Mapping Study

Test smells are defined as sub-optimal design choices developers make when implementing test cases. Hence, similar to code smells, the research community has produced numerous test smell detection tools to investigate the impact of test smells on the quality and maintenance of test suites. However, little is known about the characteristics, type of smells, target language, and availability of these published tools. In this paper, we provide a detailed catalog of all known, peer-reviewed, test smell detection tools. We start with performing a comprehensive search of peer-reviewed scientific publications to construct a catalog of 22 tools. Then, we perform a comparative analysis to identify the smell types detected by each tool and other salient features that include programming language, testing framework support, detection strategy, and adoption, among others. From our findings, we discover tools that detect test smells in Java, Scala, Smalltalk, and C++ test suites, with Java support favored by most tools. These tools are available as command-line and IDE plugins, among others. Our analysis also shows that most tools overlap in detecting specific smell types, such as General Fixture. Further, we encounter four types of techniques these tools utilize to detect smells. We envision our study as a one-stop source for researchers and practitioners in determining the tool appropriate for their needs. Our findings also empower the community with information to guide future tool development.
An augmented Lagrangian method exploiting second-order information

In this paper, we consider nonlinear optimization problems with nonlinear equality constraints and bound constraints on the variables. For the solution of such problems, many augmented Lagrangian methods have been deﬁned in the literature. Here, we propose to modify one of these algorithms, namely ALGENCAN [2, 3], in such a way to incorporate second-order information into the augmented Lagrangian framework. We show that the overall algorithm has the same convergence properties as ALGENCAN and a superlinear convergence rate under suitable assumptions. The numerical results conﬁrm that the proposed algorithm is a viable alternative to ALGENCAN with greater robustness.
The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey

Recently, various neural encoder-decoder models pioneered by Seq2Seq framework have been proposed to achieve the goal of generating more abstractive summaries by learning to map input text to output text. At a high level, such neural models can freely generate summaries without any constraint on the words or phrases used. Moreover, their format is closer to human-edited summaries and output is more readable and fluent. However, the neural model's abstraction ability is a double-edged sword. A commonly observed problem with the generated summaries is the distortion or fabrication of factual information in the article. This inconsistency between the original text and the summary has caused various concerns over its applicability, and the previous evaluation methods of text summarization are not suitable for this issue. In response to the above problems, the current research direction is predominantly divided into two categories, one is to design fact-aware evaluation metrics to select outputs without factual inconsistency errors, and the other is to develop new summarization systems towards factual consistency. In this survey, we focus on presenting a comprehensive review of these fact-specific evaluation methods and text summarization models.
HandsFormer: Keypoint Transformer for Monocular 3D Pose Estimation ofHands and Object in Interaction

We propose a robust and accurate method for estimating the 3D poses of two hands in close interaction from a single color image. This is a very challenging problem, as large occlusions and many confusions between the joints may happen. Our method starts by extracting a set of potential 2D locations for the joints of both hands as extrema of a heatmap. We do not require that all locations correctly correspond to a joint, not that all the joints are detected. We use appearance and spatial encodings of these locations as input to a transformer, and leverage the attention mechanisms to sort out the correct conﬁguration of the joints and output the 3D poses of both hands. Our approach thus al-lies the recognition power of a Transformer to the accuracy of heatmap-based methods. We also show it can be extended to estimate the 3D pose of an object manipulated by one or two hands. We evaluate our approach on the recent and challenging InterHand2.6M and HO-3D datasets. We obtain 17% improvement over the baseline. Moreover, we introduce the ﬁrst dataset made of action sequences of two hands manipulating an object fully annotated in 3D and will make it publicly available.
Certifying Emergency Landing for Safe Urban UAV

Unmanned Aerial Vehicles (UAVs) have the potential to be used for many applications in urban environments. However, allowing UAVs to fly above densely populated areas raises concerns regarding safety. One of the main safety issues is the possibility for a failure to cause the loss of navigation capabilities, which can result in the UAV falling/landing in hazardous areas such as busy roads, where it can cause fatal accidents. Current standards, such as the SORA published in 2019, do not consider applicable mitigation techniques to handle this kind of hazardous situations. Consequently, certifying UAV urban operations implies to demonstrate very high levels of integrity, which results in prohibitive development costs. To address this issue, this paper explores the concept of Emergency Landing (EL). A safety analysis is conducted on an urban UAV case study, and requirements are proposed to enable the integration of EL as an acceptable mitigation mean in the SORA. Based on these requirements, an EL implementation was developed, together with a runtime monitoring architecture to enhance confidence in the system. Preliminary qualitative results are presented and the monitor seem to be able to detect errors of the EL system effectively.
Dance Generation with Style Embedding: Learning and Transferring Latent Representations of Dance Styles

Choreography refers to creation of dance steps and motions for dances according to the latent knowledge in human mind, where the created dance motions are in general style-specific and consistent. So far, such latent style-specific knowledge about dance styles cannot be represented explicitly in human language and has not yet been learned in previous works on music-to-dance generation tasks. In this paper, we propose a novel music-to-dance synthesis framework with controllable style embeddings. These embeddings are learned representations of style-consistent kinematic abstraction of reference dance clips, which act as controllable factors to impose style constraints on dance generation in a latent manner. Thus, the dance styles can be transferred to dance motions by merely modifying the style embeddings. To support this study, we build a large music-to-dance dataset. The qualitative and quantitative evaluations demonstrate the advantage of our proposed framework, as well as the ability of synthesizing diverse styles of dances from identical music via style embeddings.
Analysis of Manual and Automated Skin Tone Assignments

The Fitzpatrick scale is a standard tool in dermatology to classify skin types for melanin and sensitivity to sun exposure. After an in-person interview, the dermatologist would classify the person’s skin type on a six-valued, light-to-dark scale. Various face image analysis researchers have recently categorized skin tone in face images on a six-valued, light-to-dark scale in order to look into questions of bias and accuracy related to skin tone. Categorization of skin tone on the basis of images rather than personal interview is not, on that basis alone, strictly speaking, on the Fitzpatrick scale. While the manual assignment of face images on a six-point, light-to-dark scale has been used by various researchers studying bias in face image analysis, to date there has been no study on the consistency and reliability of observers assigning skin type from an image. We analyze a set of manual skin type assignments from multiple observers viewing the same image set and find that there are inconsistencies between human raters. We then develop an algorithm for automated skin type assignments, which could be used in place of manual assignment by observers. Such an algorithm would allow for provision of skin tone annotations on large quantities of images beyond what could be the first work to: (a) examine the consistency of manual skin tone ratings across observers, (b) document that there is substantial variation in the rating of the same image by different observers even when exemplar images are given for guidance and all images are color-corrected, and (c) compare manual versus automated skin tone ratings. We release the automated skin tone rating implementation so that other researchers may reproduce and extend the results in this paper:
PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback

We present PSEUDo, a visual pattern retrieval tool for multivariate time series. It aims to overcome the uneconomic (re-)training problem accompanying deep learning-based methods. Very high-dimensional time series emerge on an unprecedented scale due to increasing sensor usage and data storage. Visual pattern search is one of the most frequent tasks on time series. Automatic pattern retrieval methods often suffer from inefficient training data, a lack of ground truth labels, and a discrepancy between the similarity perceived by the algorithm and required by the user or the task. Our proposal is based on the query-aware locality-sensitive hashing technique to create a representation of multivariate time series windows. It features sub-linear training and inference time with respect to data dimensions. This performance gain allows an instantaneous relevance-feedback-driven adaption to converge to users' similarity notion. We demonstrate PSEUDo's performance in terms of accuracy, speed, steerability, and usability through quantitative benchmarks with representative time series retrieval methods and a case study. We find that PSEUDo detects patterns in high-dimensional time series efficiently, improves the result with relevance feedback through feature selection, and allows an understandable as well as user-friendly retrieval process.
Apparent heating due to imperfect calorimetric measurements

Performing imperfect or noisy measurements on a quantum mechanical system both impacts the measurement outcome and the state of the system after the measurement. In this paper we are concerned with imperfect calorimetric measurements. In calorimetric measurements one typically measures the energy of a thermal environment to extract information about the system. In our setting the measurement is imperfect due to noise directly acting on the detector. Concretely, we consider an additional noise bath such that its energy is measured simultaneously with the calorimeter energy. Under weak coupling assumptions, we derive a hybrid master equation for the state of the system and the detected energy and find that the presence of the noise bath manifests itself by modifying the jump rates of the reduced system dynamics. We study an example of a driven qubit interacting with a resonant boson calorimeter and demonstrate that increasing the additional noise leads to an apparent reduction in the power flowing from qubit to calorimeter and thus to a seemingly detected heating up of the calorimeter.
AGORA: Avatars in Geography Optimized for Regression Analysis

While the accuracy of 3D human pose estimation from images has steadily improved on benchmark datasets, the best methods still fail in many real-world scenarios. This suggests that there is a domain gap between current datasets and common scenes containing people. To obtain ground-truth 3D pose, current datasets limit the complexity of clothing, environmental conditions, number of subjects, and occlusion. Moreover, current datasets evaluate sparse 3D joint locations corresponding to the major joints of the body, ignoring the hand pose and the face shape. To evaluate the current state-of-the-art methods on more challenging images, and to drive the field to address new problems, we introduce AGORA, a synthetic dataset with high realism and highly accurate ground truth. Here we use 4240 commercially-available, high-quality, textured human scans in diverse poses and natural clothing; this includes 257 scans of children. We create reference 3D poses and body shapes by fitting the SMPL-X body model (with face and hands) to the 3D scans, taking into account clothing. We create around 14K training and 3K test images by rendering between 5 and 15 people per image using either image-based lighting or rendered 3D environments, taking care to make the images physically plausible and photoreal. In total, AGORA consists of 173K individual person crops. We evaluate existing state-of-the-art methods for 3D human pose estimation on this dataset. and find that most methods perform poorly on images of children. Hence, we extend the SMPL-X model to better capture the shape of children. Additionally, we fine-tune methods on AGORA and show improved performance on both AGORA and 3DPW, confirming the realism of the dataset. We provide all the registered 3D reference training data, rendered images, and a web-based evaluation site at https://agora.is. tue.mpg. de/.
A study on vaccination control for COVID-19 in Ireland

The outbreak of a novel coronavirus causing severe acute respiratory syndrome in December 2019 has escalated into a worldwide pandemic. In this work, we propose a compartmental model to describe the dynamics of transmission of infection and use it to obtain the optimal vaccination control for the Republic of Ireland. The model accounts for the various stages of the vaccination and the optimisation is focused on minimising the infections to protect the population and relieve the healthcare system. We use data provided by Ireland’s COVID19 Data-Hub and simulate the evolution of the pandemic with and without the vaccination in place for two different scenarios, one representative of a national lockdown situation and the other indicating looser restrictions in place. One of the main findings of our work is that the optimal approach would involve a vaccination programme where the older population is vaccinated in larger numbers earlier while simultaneously part of the younger population also gets vaccinated to lower the risk of transmission between groups.
Deep Image Destruction: A Comprehensive Study on Vulnerability of Deep Image-to-Image Models against Adversarial Attacks

Recently, the vulnerability of deep image classiﬁcation models to adversarial attacks has been investigated. However, such an issue has not been thoroughly studied for image-to-image models that can have different characteristics in quantitative evaluation, consequences of attacks, and defense strategy. To tackle this, we present comprehensive investigations into the vulnerability of deep image-to-image models to adversarial attacks. For ﬁve popular image-to-image tasks, 16 deep models are analyzed from various standpoints such as output quality degradation due to attacks, transferability of adversarial examples across different tasks, and characteristics of perturbations. We show that unlike in image classiﬁcation tasks, the performance degradation on image-to-image tasks can largely differ depending on various factors, e.g., attack methods and task objectives. In addition, we analyze the effectiveness of conventional defense methods used for classiﬁcation models in improving the robustness of the image-to-image models.