Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Droop Control in Low-Inertia Power Systems

A widely embraced approach to mitigate the dynamic degradation in low-inertia power systems is to mimic generation response using grid-connected inverters to restore the stiffness of the grid. In this article, we seek to challenge this approach and advocate for a principled design based on a systematic analysis of the performance trade-offs of inverter-based frequency control. With this aim, we perform a qualitative and quantitative study comparing the effect of conventional control strategies—droop control (DC) and virtual inertia (VI)—on several performance metrics induced by $\mathcal L_2$ and $\mathcal L_\infty$ signal norms. By extending a recently proposed modal decomposition method, we capture the effect of step and stochastic power disturbances, and frequency measurement noise, on the overall transient and steady-state behavior of the system. Our analysis unveils several limitations of these solutions, such as the inability of DC to improve dynamic frequency response without increasing steady-state control effort, or the large frequency variance that VI introduces in the presence of measurement noise. We further propose a novel dynam-i-c droop controller (iDroop) that overcomes the limitations of DC and VI. More precisely, we show that iDroop can be tuned to achieve high noise rejection, fast system-wide synchronization, or frequency overshoot (Nadir) elimination without affecting the steady-state control effort share, and propose a tuning recommendation that strikes a balance among these objectives. Extensive numerical experimentation shows that the proposed tuning is effective even when our proportionality assumption is not valid, and that the particular tuning used for Nadir elimination strikes a good trade-off among various performance metrics.
A new and simple test for diagnosis and prognosis in children with otitis media with effusion: cVEMP

Abstract Aim/objectives: A typical vestibular stimulated myogenic potential (VEMP) response depends on suitable sound transfer from the middle ear to inner ear. This transfer is degraded at various degrees in otitis media with effusion (OME). In cases where the effusion is reduced by treating the OME, response to VEMP test can be obtained Backgrounds: In this study we aimed to compare preoperative and postoperative VEMP values of children who were applied ventilation tube for OME and to compare these values and VEMP values of healthy children. Materials and methods: Forty healthy and 40 patients with OME aged between 4 and 16 years were used for the study. VEMP test was performed before and after the surgery. Results: Significant decrease was seen in p13 and n23 values of patient group (p < .05). Statistically significant increase was seen in postoperative amplitudes of patient group (p < .05). Conclusions: Increased VEMP responses in children with OME have shown that dysfunction of middle ear caused vestibular dysfunction. We think that VEMP test can be used to follow up children with OME as a test for showing improvement. Significance: The VEMP test can be used a diagnostic and prognostic test in the diagnosis and follow-up of children with OME.
Faster feedback through lexical test prioritization

Immediacy and continuity of feedback are desirable properties during programming. Automated tests are a widely used practice to gain feedback on whether test authors' expectations are consistent with an implementation. With growing test suites, feedback becomes less immediate and is obtained less frequently because of that. The objective of test prioritization is to choose an order of tests that catches errors as early as possible, ideally within a time frame that we can consider live. Research in test prioritization often relies on dynamic analysis, which is expensive to obtain. Newer approaches focus on most recently edited source code locations and propose IR (information retrieval) approaches that regard a change to the software as query against a collection of tests. We study the capability of the IR approach to reduce testing time in the presence of faults using the example of open-source Python projects, identify trade-offs in classical TF-IDF-based IR frameworks, and propose different approaches that consider lexical and semantic context of a change, including topic modeling. We conclude that even simple IR strategies achieve immediate error detection, especially when tests themselves were edited alongside program code. We further discuss applications of this approach in live programming environments, where change granularity does not leave sufficient time to run a test suite entirely.
Live software development: tightening the feedback loops

Live Programming is an idea pioneered by programming environments from the earliest days of computing, such as those for Lisp and Smalltalk. One thing they had in common is liveness: an always accessible evaluation and nearly instantaneous feedback, usually focused on coding activities. In this paper, we argue for Live Software Development (LiveSD), bringing liveness to software development activities beyond coding, to make software easier to visualize, simpler to understand, and faster to evolve. Multiple challenges may vary with the activity and application domain. Research on this topic needs to consider the more important liveness gaps in software development, which representations and abstractions better support developers, and which tools are needed to support it.
Let them fail: towards VM built-in behavior that falls back to the program

An important purpose of managed runtime environments like virtual machines is to provide built-in behavior to their programs, that is, behavior that cannot or may not be expressed in the program's language and is implemented inside the VM. For performance, stability, and security, such built-in behavior is typically rigorous, and if it deems its invocation to be erroneous, ill-suited, or just not quite right, it communicates this failure via exceptions at best. This implies that a vm's sphere of influence extends rather far into the realm of the program executed: at best, exceptions can be reacted to when built-in behavior fails, even though a program might be able to provide alternatives, improvements, or other ways of handling a failure. By reflecting on how built-in behavior works in a Smalltalk system, we argue that the sphere of influence of programs on VMS is extensible with regard to built-in behavior. For that, we suggest to let built-in behavior fail and let programs decide whether to raise exceptions, try again, or simply do nothing at all.
Climate Benefits of Intact Amazon Forests and the Biophysical Consequences of Disturbance

Tropical forests have an important regulating influence on local and regional climate, through modulating the exchange of moisture and energy between the land and the atmosphere. Deforestation disrupts this exchange, though the climatic consequences of progressive, patch-scale deforestation of formerly intact forested landscapes have not previously been assessed. Remote sensing datasets of land surface and atmospheric variables were used to compare the climate responses of Amazon evergreen broadleaf forests that lost their intact status between 2000 and 2013. Clear gradients in environmental change with increasing disturbance were observed. Leaf area index (LAI) showed progressively stronger reductions as forest loss increased, with evapotranspiration (ET) showing a comparative decline. These changes in LAI and ET were related to changes in temperature (T), with increased warming as deforestation increased. Severe deforestation of intact Amazon forest, defined as areas where canopy cover was reduced below 70 %, was shown to have increased daytime land surface T by 0.44 °C over the study period. Differences between intact and disturbed forests were most pronounced during the dry season, with severely deforested areas warming as much as 1.5 °C. Maintenance of canopy cover was identified as an important factor in minimising the impacts of disturbance. Overall, the results highlight the climate benefits provided by intact tropical forests, providing further evidence that protecting intact forests is of utmost importance.
Theoretical Issues in Deep Networks : Approximation , Optimization and Generalization

While deep learning is successful in a number of applications, it is not yet well understood theoretically. A satisfactory theoretical characterization of deep learning however, is beginning to emerge. It covers the following questions: 1) representation power of deep networks 2) optimization of the empirical risk 3) generalization properties of gradient descent techniques — why the expected error does not suffer, despite the absence of explicit regularization, when the networks are overparametrized? In this review we discuss recent advances in the three areas. In approximation theory both shallow and deep networks have been shown to approximate any continuous functions on a bounded domain at the expense of an exponential number of parameters (exponential in the dimensionality of the function). However, for a subset of compositional functions, deep networks of the convolutional type (even without weight sharing) can have a linear dependence on dimensionality, unlike shallow networks. In optimization we discuss the loss landscape for the exponential loss function. It turns out that global minima at infinity are completely degenerate. The other critical points of the gradient are less degenerate, with at least one – and typically more – nonzero eigenvalues. This suggests that stochastic gradient descent will find with high probability the global minima. To address the question of generalization for classification tasks, we use classical uniform convergence results to justify minimizing a surrogate exponential-type loss function under a unit norm constraint on the weight matrix at each layer – since the interesting variables for classification are the weight directions rather than the weights. As a side remark, such minimization for (homogeneous) ReLU deep networks implies maximization of the margin. The resulting constrained gradient system turns out to be identical to the well-known weight normalization technique, originally motivated from a rather different way. We also show that standard gradient descent contains an implicit L2 unit norm constraint in the sense that it solves the same constrained minimization problem with the same critical points (but a different dynamics). Our approach, which is supported by several independent new results (1– 4), offers a solution to the puzzle about generalization performance of deep overparametrized ReLU networks, uncovering the origin of the underlying hidden complexity control in the case of deep networks.
Unsupervised Recalibration

Unsupervised recalibration (URC) is a general way to improve the accuracy of an already trained probabilistic classification or regression model upon encountering new data while deployed in the field. URC does not require any ground truth associated with the new field data. URC merely observes the model's predictions and recognizes when the training set is not representative of field data, and then corrects to remove any introduced bias. URC can be particularly useful when applied separately to different subpopulations observed in the field that were not considered as features when training the machine learning model. This makes it possible to exploit subpopulation information without retraining the model or even having ground truth for some or all subpopulations available. Additionally, if these subpopulations are the object of study, URC serves to determine the correct ground truth distributions for them, where naive aggregation methods, like averaging the model's predictions, systematically underestimate their differences.
Prototyping on site Virtual Museums: the case study of the co-design approach to the Palatine hill in Rome (Barberini Vineyard) exhibition

In 2016, during the preparation of a rock musical in Rome about the life of the Roman emperor Nero, CNR ITABC became involved in the proposal of a Virtual Museum dedicated to an area of the Palatine hill (the so-called Barberini Vineyard), facing the Colosseum, where the stage of the show should have been temporarily built. The paper presents a methodology that has been tested for the creation of the Virtual Museum of the Palatine, and identifies useful tools that could support the creation of virtual museums and how such museums could be located in a physical space and within a real collection. While planning and testing, we have demonstrated the usefulness of immersive reality as a way to prototype "on site virtual museums" and to solve common issues that emerge in the design and production of such installations.
Great Ape Detection in Challenging Jungle Camera Trap Footage via Attention-Based Spatial and Temporal Feature Blending

We propose the first multi-frame video object detection framework trained to detect great apes. It is applicable to challenging camera trap footage in complex jungle environments and extends a traditional feature pyramid architecture by adding self-attention driven feature blending in both the spatial as well as the temporal domain. We demonstrate that this extension can detect distinctive species appearance and motion signatures despite significant partial occlusion. We evaluate the framework using 500 camera trap videos of great apes from the Pan African Programme containing 180K frames, which we manually annotated with accurate per-frame animal bounding boxes. These clips contain significant partial occlusions, challenging lighting, dynamic backgrounds, and natural camouflage effects. We show that our approach performs highly robustly and significantly outperforms frame-based detectors. We also perform detailed ablation studies and a validation on the full ILSVRC 2015 VID data corpus to demonstrate wider applicability at adequate performance levels. We conclude that the framework is ready to assist human camera trap inspection efforts. We publish code, weights, and ground truth annotations with this paper.
Microscopic dynamics in rare events: generalized L\'evy processes and the big jump principle

The prediction and estimate of rare events is an important task in disciplines that range from physics and biology, to economics and social science. A peculiar aspect of the mechanism that drives rare events is described by the so called Big Jump Principle. According to the principle, in heavy-tailed processes a rare huge fluctuation is caused by a single event and not by the usual coherent accumulation of small deviations. We consider generalized Levy walks, a class of stochastic models with wide applications, from complex transport to search processes, that accounts for complex microscopic dynamics in the single stretch. We derive the bulk of the probability distribution and using the big jump principle, the exact form of the tails that describes rare events. We show that the tails of the distribution feature non-universal and non-analytic behaviors, that crucially depend on the dynamics of the single step. The big jump estimate also provides a physical explanation of the processes driving the rare events, opening new possibilities for their correct prediction.
A real time optical neural networks using programmable LCTV spatial light modulator

A new memory modle of optical neural networks called the reflexive associative memory is implemented in real-time using programmable liquid crystal television (LCTV) in our experiment. In this model the vectors are stored in a memory matrix in the form of vector pairs. Not only a full vector prestored in neural networks can be recalled from a partial information of the vector, but the other vector of the prestored vector pair can also be recalled.
Federal Reserve Bank of Philadelphia Working Paper No. 02-1 an Overall Perspective on Banking Regulation an Overall Perspective on Banking Regulation 1 Forthcoming in Financial Regulation, Edited

1 The views expressed are those of the authors and do not necessarily represent those of the Federal Reserve Bank of Philadelphia or the Federal Reserve System. 1 Introduction Recently, the theory of banking regulation has undergone important changes. This has been the consequence of a number of compounding effects that have been occurring in the financial sector. First among these is ongoing financial innovation, which has caused a virtual revolution in both financial instruments and markets. As a result, the markets and institutions that must be regulated have changed substantially over time. At the same time, regulation has evolved, as the regulators have learned the lessons from the recent spat of banking crises. As a consequence of these experiences, regulation has become more sophisticated, with the introduction of capital requirements and more complex restrictions on operating procedures.
Abstract. The present study sought to determine the survival outcomes for women diagnosed with breast and endometrial cancer. Using SEER data, a population-based cohort study of women diagnosed with breast and endometrial cancer

The present study sought to determine the survival outcomes for women diagnosed with breast and endometrial cancer. Using SEER data, a population-based cohort study of women diagnosed with breast and endometrial cancer was conducted. Kaplan-Meier survival curves were created for disease‐specific survival rates. A total of 2,027 women diagnosed with breast and endometrial cancer were identified. Of these, 1,296 (63.9%) developed breast cancer first and 731 (36.1%) developed endometrial cancer first. Regional lymph node involvement was significantly more common with a breast cancer diagnosis [522 (25.8%) women] compared with an endometrial cancer diagnosis [87 (4.3%) women] (P<0.05). Factors associated with decreased survival included a high tumor grade in endometrial cancer, nodal positivity and estrogen receptor-negative breast cancer (P<0.05 for each). There were 83 (4.1%) mortalities due to breast cancer, 63 (3.1%) mortalities due to endometrial cancer and 178 (8.8%) mortalities due to other causes (P<0.05). In conclusion, for women diagnosed with breast and endometrial cancer, the cumulative risk of mortality at five years following the second cancer diagnosis is nearly four times more likely to be due to breast cancer than endometrial cancer.
A Common Approach for Consumer and Provider Fairness in Recommendations

We present a common approach for handling consumer and provider fairness in recommendations. Our solution requires defining two key components, a classification of items and a target distribution, which together define the case of perfect fairness. This formulation allows distinct fairness concepts to be specified in a common framework. We further propose a novel reranking algorithm that optimizes for a desired trade-off between utility and fairness of a recommendation list.
Behaviour: perception, action and intelligence — the view from situated robotics

We relate the problems afflicting implementations of classical knowledge-based symbolic systems to theoretical criticisms of the paradigm, and explain why many of those pursuing research programmes designed to avoid these problems and underpinned by models of mind variously described as ‘behaviour-based’, ‘reactive’, ‘enactive’, ‘situated’, ‘embedded’ are using robot rather than computer systems as their experimental domain. We argue that mentalistic terms are only applicable to contingent historical agents embedded in the local world with which they interact, and therefore (for example) attempting to implement intelligence, semantics, etc., in a computer system is a doomed enterprise.
Negative-Aware Collaborative Filtering

Most traditional recommender systems regard unseen user-item associations as negative user preferences and optimize recommendation models mainly based on observed associations and some negative instances sampled from unseen associations. However, such unseen user-item associations may contain potential positive user preferences on items and are not uniformly distributed in terms of the possibility of being negative (or positive) user preference; therefore, it is essential to quantify such associations for model training. Along this line, in this paper, in contrast to existing recommendation models, which equally treat all unseen associations as negative samples, we present a negative-aware recommendation approach that explicitly models the likelihood of each unseen association being a potentially positive preference. Empirical results on real-world datasets in different fields show that our approach consistently improves recommendation performance.
The need for compatible and more human-relevant animal models

OPEN ACCESS This open-access article is distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC) (http:// creativecommons.org/licenses/by-nc/4.0/), which permits reuse, distribution and reproduction of the article, provided that the original work is properly cited and the reuse is restricted to noncommercial purposes. For commercial reuse, contact reprints@pulsus.com The need for compatible and more human-relevant animal models
ADJACENT QUERY SET EXPLORE IN DIFFERENT VARIANCE DATASETS

Unlike tree-like ratios adopted in actual whole shebang, our indicator is less active to the rise of reach and scales well with multi-dimensional data. Undesirable candidates are pruned harmonious the distances enclosed by MBRs of points or keyword and also admirable raise width. NKS queries are important for a lot of applications, for instance photo-discussing in civil systems, visual representation sequence ransack, relocation inspect in GIS systems, thus. We present a perfect also a like form of capacity. Within this essay, we feel objects whichever are tagged with paternoster and so are stewed into a bearing distance. Keyword-based ransack in text-wealthy multi-dimensional datasets facilitates many peculiar applications and tools. Of the above-mentioned datasets, we inspect queries that solicit the tightest categories of point’s pleasurable entrenched gather of opener. Our preliminary results on real and insincere datasets report that Promos has morally 60 occasions of speedup over condition-of-the-art tree-based techniques. We notify an extraordinary structure common as ProMiSH that utilizes arbitrary outthrust and hash-based ratio structures, and achieves high scalability and speedup. We oversee considerable experiential studies to show the appearance from the advised techniques.
Prevention 0f Unwanted Free-Declaration of Static Obstacles in Probability Occupancy Grids

Obstacle detection and avoidance are major research fields in unmanned aviation. Map based obstacle detection approaches often use discrete world representations such as probabilistic grid maps to fuse incremental environment data from different views or sensors to build a comprehensive representation. The integration of continuous measurements into a discrete representation can result in rounding errors which, in turn, leads to differences between the artificial model and real environment. The cause of these deviations is a low spatial resolution of the world representation comparison to the used sensor data. Differences between artificial representations which are used for path planning or obstacle avoidance and the real world can lead to unexpected behavior up to collisions with unmapped obstacles. This paper presents three approaches to the treatment of errors that can occur during the integration of continuous laser measurement in the discrete probabilistic grid. Further, the quality of the error prevention and the processing performance are compared with real sensor data.