Miracle-dz/UniFAR-FTUs huggingface dataset:
Key performance indicators used to measure the adherence to the iterative software delivery model and policies

This paper analyzes the Agile metrics embedded in the widely adopted issue tracking tools and used by agile teams to deliver software products. Organizations face challenges when they are required to measure the performance of all the teams operating within the company to provide a holistic view of the software delivery performance at a higher level. A new set of key performance indicators (KPIs) is introduced that can help software companies standardize their delivery across the organization and provide an overview of the software delivery performance that can serve as an insight for the top management teams. The new metrics are applied to a project as part of a case study. The conclusions highlight that the metrics provided by the issue tracking systems do not provide insights into the non-functional requirements of the software products, the methodology applied during the delivery, and the quality of each increment produced. The proposed set of KPIs addresses these shortcomings.
Investigating the Martian Surface at Decametric Scale: Population, Distribution, and Dimension of Heterogeneity from Radar Statistics

Building on one decade of theory and methodology maturation, we investigate the coherent and incoherent components of the response of the Martian surface to nadir-looking orbital radar. We apply a reflectometry technique known as radar statistical reconnaissance to Mars Reconnaissance Orbiter Shallow Radar data over a test region with a large dynamic range in echo strength. This technique provides a set of statistical parameters describing the heterogeneity of the surface and near-surface structure, presumably at a scale of ∼15 m. We discuss the physical meanings of these parameters related to surface and near-surface properties. Most (but not all) investigated terrains have a dominantly coherent surface return, a characteristic that is not necessarily indicative of a smooth surface. The observed behavior of the coherent and incoherent power components of the echo matches signal growth with increasing surface roughness. This finding allows us to identify smooth and level terrains that we use as a reference to approximate the surface height and slope variations of other regions. Nearly systematic mismatches between the SHARAD and MOLA-pulse-width roughness illustrate the complementarity of these data sets from their respective sensitivity range, and advocate for the use of self-affine radar backscattering models to account for roughness variations at different scales. Our methodology provides a wealth of surface properties assessment based on radar scattering with quasi-global coverage, without a dependence on other data, and at a decametric horizontal scale relevant to subregional geology investigation and landing site reconnaissance.
Advanced Engineering Electromagnetics

Time--Varying and Time--Harmonic Electromagnetic Fields. Electrical Properties of Matter. Wave Equation and Its Solutions. Wave Propagation and Polarization. Reflection and Transmission. Auxiliary Vector Potentials, Contruction of Solutions, and Radiation and Scattering Equations. Electromagnetic Theorems and Principles. Rectangular Cross--Section Waveguides and Cavities. Circular Cross--Section Waveguides and Cavities. Spherical Transmission Lines and Cavities. Scattering. Integral Equations and the Moment Method. Geometrical Theory of Diffraction. Greena s Functions. Appendices. Index.
Comparison of Topical Oxygen with Vacuum Assisted Closure in Wound Healing in a Low Resource Setting

Background The aim of this pilot study is to obtain preliminary results comparing topical oxygen therapy (TOT) and vacuum assisted closure (VAC) in terms of its ability to accelerate wound healing. Methods This non-randomised prospective study included patients with age 16–50 years, wound size ≥ 16cm2 and present below knee joint within seven days of occurrence. Bates-Jensen wound assessment tool (BWAT) was used for evaluation at 8-day interval along with percent area reduction at final follow up. Results Mean number of cycles required in VAC and TOT group were 1.97 (range 1–3) and 2.1 (range 1–3) (each cycle of 5 days) per patient respectively. Percent area reduction was significantly higher in the VAC group (34±9.7%) than TOT (11.3±3.8%) group at final follow up (p<0.05). TOT patients had better improvement in epithelialization compared to VAC at last follow up. More extensive debridement was needed in patients of TOT than VAC. There was no significant difference between final score in both groups. Conclusions TOT appears to be comparable to well-established VAC in treatment of fresh traumatic wounds below the knee joint. Further large scale, multicentric and randomised studies comparing both these modalities of treatment should be the way forward.
Spatiotemporal variation of ringed seal blubber cortisol levels in the Canadian Arctic

Climate change in the Arctic has widespread and complex effects on the health of animals and their populations. We used radioimmunoassay to measure blubber cortisol in ringed seals (Pusa hispida) sampled in Ulukhaktok, Inuvialuit Settlement Region, Northwest Territories, Canada (spring, 2002, 2004–2005, 2007–2012) and Arviat, Nunavut, Canada (autumn, 2003–2012) to examine chronic stress relative to biology (age, sex, length), body condition (blubber depth), and diet (δ13C, δ15N, and isotopic niche size). Ulukhaktok ringed seals had higher cortisol concentrations overall (0.46 ± 0.04 ng/g) than Arviat ringed seals (0.36 ± 0.03 ng/g), and these higher concentrations were associated with higher muscle δ15N and lower blubber thickness. In contrast, blubber cortisol concentrations for Arviat ringed seals decreased with blubber depth and increased with age, though testing of age effects individually suggests that age-related patterns are weak. Annual mean cortisol concentration increased from 2003 to 2012 in Arviat ringed seals, but low sample sizes precluded analysis of annual patterns for Ulukhaktok ringed seals. The trend of increased cortisol over time in Arviat ringed seals suggests that they might be experiencing greater chronic stress over time, which could have implications for numerous population health metrics including reproduction and pup recruitment.
Current usage and challenges of master protocols—based on survey results by ASA BIOP oncology methodology working group master protocol sub-team

Background Master protocol trials, such as basket trials, umbrella trials or platform trials, have the potential of increasing efficiency in modern drug development. Meanwhile, though the concept of master protocol is getting more and more accepted, many challenges exist from design to implementation of these trials. To understand current usage and challenges of master protocol trials in action, American Statistical Association (ASA) Biopharmaceutical Section (BIOP) Oncology Methods Scientific Working Group Master Protocol Sub-team conducted a survey with the goal of providing valuable information for the community to understand the current usage of master protocol, with the goal to identify the challenges. Methods A total of 19 questions were included in an online survey that was distributed between April and May 2021. To avoid over-reporting within an organization, a pre-determined list of contacts from 37 organizations were reached out with the shared online link of the survey. Literature research and experience from the working group on challenges of master protocols are also summarized and discussed extensively. Results A total of 39 responses were received from 37 organizations. Thirty-one (79%) respondents indicated that they had trials with master protocol(s) in planning or implementation in their organization with most applications (54%) in oncology. Self-reported challenges on trial design, regulatory engagement and trial implementations were further summarized in the report. Conclusions The survey results were consistent with previous literatures and expectations of members from the Scientific Working Group Sub-team. Multiple stakeholders are called to work collaboratively to remove roadblocks for future usage of master protocol trials.
Type-supervised sequence labeling based on the heterogeneous star graph for named entity recognition

Named entity recognition is a fundamental task in natural language processing, identifying the span and category of entities in unstructured texts. The traditional sequence labeling methodology ignores the nested entities, i.e. entities included in other entity mentions. Many approaches attempt to address this scenario, most of which rely on complex structures or have high computation complexity. The representation learning of the heterogeneous star graph containing text nodes and type nodes is investigated in this paper. In addition, we revise the graph attention mechanism into a hybrid form to address its unreasonableness in specific topologies. The model performs the type-supervised sequence labeling after updating nodes in the graph. The annotation scheme is an extension of the single-layer sequence labeling and is able to cope with the vast majority of nested entities. Extensive experiments on public NER datasets reveal the effectiveness of our model in extracting both flat and nested entities. The method achieved state-of-the-art performance on both flat and nested datasets. The significant improvement in accuracy reflects the superiority of the multi-layer labeling strategy.
Spectroscopic Confirmation of Two X-Ray Diffuse and Massive Galaxy Clusters at Low Redshift

We present MMT spectroscopic observations of two massive galaxy cluster candidates at redshift z ∼ 0.07 that show extended and diffuse X-ray emission in the ROSAT All-Sky Survey (RASS) images. The targets were selected from a previous catalog of 303 newly identified cluster candidates with the similar properties using the intracluster medium emission. Using the new MMT Hectospec data and Sloan Digital Sky Survey archival spectra, we identify a number of member galaxies for the two targets and confirm that they are galaxy clusters at z = 0.079 and 0.067, respectively. The size of the two clusters, calculated from the distribution of the member galaxies, is roughly 2 Mpc in radius. We estimate cluster masses using three methods based on their galaxy number overdensities, galaxy velocity dispersions, and X-ray emission. The overdensity-based masses are (6 ∼ 8) × 1014 M ⊙, comparable to the masses of large clusters at low redshift. The masses derived from velocity dispersions are significantly lower, likely due to their diffuse and low concentration features. Our result suggests the existence of a population of large clusters with very diffuse X-ray emission that has been missed by most previous searches using the RASS images. If most of the 303 candidates in the previous catalog are confirmed to be real clusters, this may help to reduce the discrepancy of cosmological results between the cosmic microwave background and galaxy cluster measurements.
2nd Place Solution to Google Universal Image Embedding

This paper presents the 2nd place solution to the Google Universal Image Embedding Competition 2022. We use the instance-level ﬁne-grained image classiﬁcation method to complete this competition. We focus on data processing, model structure, and training strategies. To balance the weights between classes, we employ sampling and resampling strategies. For model selection, we choose the CLIP-ViT-H-14 model pretrained on LAION-2B. Besides, we re-moved the projection layer to reduce the risk of overﬁtting. In addition, dynamic margin and stratiﬁed learning rate training strategies also improve the model’s performance. Finally, the method scored 0.713 on the public leaderboard and 0.709 on the private leaderboard. Code is available at https://github.com/XL-H/GUIE-2nd-Place-Solution .
Mid-infrared imaging of Supernova 1987A

At a distance of 50 kpc, Supernova 1987A is an ideal target to study how a young supernova (SN) evolves in time. Its equatorial ring, filled with material expelled from the progenitor star about 20,000 years ago, has been engulfed with SN blast waves. Shocks heat dust grains in the ring, emitting their energy at mid-infrared (IR) wavelengths We present ground-based 10--18$\mu$m monitoring of the ring of SN 1987A from day 6067 to 12814 at a resolution of 0.5", together with SOFIA photometry at 10-30 $\mu$m. The IR images in the 2000's (day 6067-7242) showed that the shocks first began brightening the east side of the ring. Later, our mid-IR images from 2017 to 2022 (day 10952-12714) show that dust emission is now fading in the east, while it has brightened on the west side of the ring. Because dust grains are heated in the shocked plasma, which can emit X-rays, the IR and X-ray brightness ratio represent shock diagnostics. Until 2007 the IR to X-ray brightness ratio remained constant over time, and during this time shocks seemed to be largely influencing the east side of the ring. However, since then, the IR to X-ray ratio has been declining, due to increased X-ray brightness. Whether the declining IR brightness is because of dust grains being destroyed or being cooled in the post-shock regions will require more detailed modelling.
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization

Optimizing expensive-to-evaluate black-box functions of discrete (and potentially continuous) design parameters is a ubiquitous problem in scientific and engineering applications. Bayesian optimization (BO) is a popular, sample-efficient method that leverages a probabilistic surrogate model and an acquisition function (AF) to select promising designs to evaluate. However, maximizing the AF over mixed or high-cardinality discrete search spaces is challenging standard gradient-based methods cannot be used directly or evaluating the AF at every point in the search space would be computationally prohibitive. To address this issue, we propose using probabilistic reparameterization (PR). Instead of directly optimizing the AF over the search space containing discrete parameters, we instead maximize the expectation of the AF over a probability distribution defined by continuous parameters. We prove that under suitable reparameterizations, the BO policy that maximizes the probabilistic objective is the same as that which maximizes the AF, and therefore, PR enjoys the same regret bounds as the original BO policy using the underlying AF. Moreover, our approach provably converges to a stationary point of the probabilistic objective under gradient ascent using scalable, unbiased estimators of both the probabilistic objective and its gradient. Therefore, as the number of starting points and gradient steps increase, our approach will recover of a maximizer of the AF (an often-neglected requisite for commonly used BO regret bounds). We validate our approach empirically and demonstrate state-of-the-art optimization performance on a wide range of real-world applications. PR is complementary to (and benefits) recent work and naturally generalizes to settings with multiple objectives and black-box constraints.
Temporal Action Segmentation: An Analysis of Modern Techniques

Temporal action segmentation (TAS) in videos aims at densely identifying video frames in minutes-long videos with multiple action classes. As a long-range video understanding task, researchers have developed an extended collection of methods and examined their performance using various benchmarks. Despite the rapid growth of TAS techniques in recent years, no systematic survey has been conducted in these sectors. This survey analyzes and summarizes the most significant contributions and trends. In particular, we first examine the task definition, common benchmarks, types of supervision, and prevalent evaluation measures. In addition, we systematically investigate two essential techniques of this topic, i.e., frame representation and temporal modeling, which have been studied extensively in the literature. We then conduct a thorough review of existing TAS works categorized by their levels of supervision and conclude our survey by identifying and emphasizing several research gaps.
Modeling the impact of external influence on green behaviour spreading in multilayer financial networks

Growing awareness of the impact of business activity on the environment increases the pressure for governing bodies to address this issue. One possibility is to encourage or force the market into green behaviours. However, it is often hard to predict how different actions affect the market. Thus, to help with that, in this paper, we have proposed the green behaviour spreading model in the bank-company multilayer network. This model allows assessing how various elements like the duration of external influence, targeted market segment, or intensity of action affect the outcome regarding market greening level. The model evaluation results indicate that governing bodies, depending on the market "openness" to green activities, can adjust the duration and intensity of the proposed action. The strength of the impact can be changed by the public or private authority with the use of obligatory or voluntary rules and the proportion of influenced banks. This research may be helpful in the process of creating the optimal setups and increasing the performance of greening policies implementation.
On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning

Reinforcement Learning (RL) algorithms can solve challenging control problems directly from image observations, but they often require millions of environment interactions to do so. Recently, model-based RL algorithms have greatly improved sample-efficiency by concurrently learning an internal model of the world, and supplementing real environment interactions with imagined rollouts for policy improvement. However, learning an effective model of the world from scratch is challenging, and in stark contrast to humans that rely heavily on world understanding and visual cues for learning new skills. In this work, we investigate whether internal models learned by modern model-based RL algorithms can be leveraged to solve new, distinctly different tasks faster. We propose Model-Based Cross-Task Transfer (XTRA), a framework for sample-efficient online RL with scalable pretraining and finetuning of learned world models. By offline multi-task pretraining and online cross-task finetuning, we achieve substantial improvements over a baseline trained from scratch; we improve mean performance of model-based algorithm EfficientZero by 23%, and by as much as 71% in some instances.
Simplex Range Searching Revisited: How to Shave Logs in Multi-Level Data Structures

We revisit the classic problem of simplex range searching and related problems in computational geometry. We present a collection of new results which improve previous bounds by multiple logarithmic factors that were caused by the use of multi-level data structures. Highlights include the following: $\bullet$ For a set of $n$ points in a constant dimension $d$, we give data structures with $O(n^d)$ (or slightly better) space that can answer simplex range counting queries in optimal $O(\log n)$ time and simplex range reporting queries in optimal $O(\log n + k)$ time, where $k$ denotes the output size. For semigroup range searching, we obtain $O(\log n)$ query time with $O(n^d\mathop{\rm polylog}n)$ space. Previous data structures with similar space bounds by Matou\v{s}ek from nearly three decades ago had $O(\log^{d+1}n)$ or $O(\log^{d+1}n + k)$ query time. $\bullet$ For a set of $n$ simplices in a constant dimension $d$, we give data structures with $O(n)$ space that can answer stabbing counting queries (counting the number of simplices containing a query point) in $O(n^{1-1/d})$ time, and stabbing reporting queries in $O(n^{1-1/d}+k)$ time. Previous data structures had extra $\log^d n$ factors in space and query time. $\bullet$ For a set of $n$ (possibly intersecting) line segments in 2D, we give a data structure with $O(n)$ space that can answer ray shooting queries in $O(\sqrt{n})$ time. This improves Wang's recent data structure [SoCG'20] with $O(n\log n)$ space and $O(\sqrt{n}\log n)$ query time.
Safe Planning in Dynamic Environments Using Conformal Prediction

We propose a framework for planning in unknown dynamic environments with probabilistic safety guarantees using conformal prediction. Particularly, we design a model predictive controller (MPC) that uses i) trajectory predictions of the dynamic environment, and ii) prediction regions quantifying the uncertainty of the predictions. To obtain prediction regions, we use conformal prediction, a statistical tool for uncertainty quantification, that requires availability of offline trajectory data – a reasonable assumption in many applications such as autonomous driving. The prediction regions are valid, i.e., they hold with a user-defined probability, so that the MPC is provably safe. We illustrate the results in the self-driving car simulator CARLA at a pedestrian-filled intersection. The strength of our approach is compatibility with state of the art trajectory predictors, e.g., RNNs and LSTMs, while making no assumptions on the underlying trajectory-generating distribution. To the best of our knowledge, these are the first results that provide valid safety guarantees in such a setting.
Constrained estimation of a discrete distribution with probabilistic forecast control

In this paper we integrate the isotonic regression with Stone's cross-validation-based method to estimate discrete infinitely supported distribution. We prove that the estimator is strongly consistent, derive its rate of convergence for any underlying distribution, and for one-dimensional case we derive Marshal-type inequality for cumulative distribution function of the estimator. Also, we construct the asymptotically correct conservative global confidence band for the estimator. It is shown that, first, the estimator performs good even for small sized data sets, second, the estimator outperforms in the case of non-monotone underlying distribution, and, third, it performs almost as good as Grenander estimator when the true distribution is isotonic. Therefore, the new estimator provides a trade-off between goodness-of-fit, monotonicity and quality of probabilistic forecast. We apply the estimator to the time-to-onset data of visceral leishmaniasis in Brazil collected from 2007 to 2014.
Solar energetic particle time series analysis with Python

Solar Energetic Particles (SEPs) are charged particles accelerated within the solar atmosphere or the interplanetary space by explosive phenomena such as solar flares or Coronal Mass Ejections (CMEs). Once injected into the interplanetary space, they can propagate towards Earth, causing space weather related phenomena. For their analysis, interplanetary in situ measurements of charged particles are key. The recently expanded spacecraft fleet in the heliosphere not only provides much-needed additional vantage points, but also increases the variety of missions and instruments for which data loading and processing tools are needed. This manuscript introduces a series of Python functions that will enable the scientific community to download, load, and visualize charged particle measurements of the current space missions that are especially relevant to particle research as time series or dynamic spectra. In addition, further analytical functionality is provided that allows the determination of SEP onset times as well as their inferred injection times. The full workflow, which is intended to be run within Jupyter Notebooks and can also be approachable for Python laymen, will be presented with scientific examples. All functions are written in Python, with the source code publicly available at GitHub under a permissive license. Where appropriate, available Python libraries are used, and their application is described.