Worst Case Research Articles

On-the-Fly Static Analysis via Dynamic Bidirected Dyck Reachability

Dyck reachability is a principled, graph-based formulation of a plethora of static analyses. Bidirected graphs are used for capturing dataflow through mutable heap data, and are usual formalisms of demand-driven points-to and alias analyses. The best (offline) algorithm runs in O ( m + n · α( n )) time, where n is the number of nodes and m is the number of edges in the flow graph, which becomes O ( n 2 ) in the worst case. In the everyday practice of program analysis, the analyzed code is subject to continuous change, with source code being added and removed. On-the-fly static analysis under such continuous updates gives rise to dynamic Dyck reachability, where reachability queries run on a dynamically changing graph, following program updates. Naturally, executing the offline algorithm in this online setting is inadequate, as the time required to process a single update is prohibitively large. In this work we develop a novel dynamic algorithm for bidirected Dyck reachability that has O ( n · α( n )) worst-case performance per update, thus beating the O ( n 2 ) bound, and is also optimal in certain settings. We also implement our algorithm and evaluate its performance on on-the-fly data-dependence and alias analyses, and compare it with two best known alternatives, namely (i) the optimal offline algorithm, and (ii) a fully dynamic Datalog solver. Our experiments show that our dynamic algorithm is consistently, and by far, the top performing algorithm, exhibiting speedups in the order of 1000X. The running time of each update is almost always unnoticeable to the human eye, making it ideal for the on-the-fly analysis setting.

Disregarding Uncertainty, Marginalizing Patients

What does it mean to “trust the science”? The phrase is often used to suggest that scientific knowledge is static and that the public can treat current recommendations as eternal verities. The medical community is not omniscient, however. Indeed, some illnesses are characterized by uncertainty: it is not clear with what frequency the associated symptoms occur or whether they are a manifestation of a psychological disorder or a physiological disease. The medical field is no stranger to such challenges, labeling such illnesses as “contested.” The communication of uncertainty surrounding these illnesses, however, presents a troubling dynamic. Rather than engaging in constructive dialogue that moves scientific understanding forward, viewpoints become entrenched, points of disagreement are obscured, and professionals retaliate against one another. In the best case scenario, patients are merely left to sort through dense medical concepts on their own; in the worst case, they are targeted by predatory providers. Patients suffer, trust in science declines.
 Drawing on insights from the economics literature, this Article presents a theoretical framework for approaching the uncertainty inherent in contested illnesses. The framework discusses the pattern of evidence accumulation that accompanies an uncertain illness, distinct from that of a disease where uncertainty is substantially resolved. Applying these insights to the communication of uncertainty surrounding contested illnesses, the Article notes that null results in the presence of well-designed studies should be weighed differently than null results in the presence of case studies or small sample studies. If an illness is truly uncertain, the framework highlights the potential benefits of additional caution in approaching irreversible actions, such as prematurely communicating the resolution of uncertainty (which can lead to the development of patient mistrust). This careful communication of uncertainty is vital to prevent patient marginalization and to clarify the often-inscrutable information landscape in these contexts. Current regulations for medical providers and public figures are insufficient, however, to incentivize such careful communication. This Article proposes a government-coordinated informational digest that weighs existing evidence based on the rigor of study design and imputes null results to missing results of completed studies. The agency coordinating the digest will provide incentives for studies that fill gaps in the literature and which incorporate input from patient advocate groups into the design. In doing so, it will enable the medical community to both better handle existing uncertainty and to take important steps toward resolving it. More importantly, however, this transparent process educates the public about how scientific beliefs should evolve and prevents the irreversible harm of patient marginalization.

Climate Change Affects Choice and Management of Training Systems in the Grapevine

Although vertical shoot positioned (VSP) training systems, either cane- or spur-pruned, are adopted in the great majority of the vineyards worldwide, the lianas nature of the grapevine and the presence of long and flexible canes confer high plasticity and render structural and pruning changes quite easy. The focus of this review is if, in light of the most consistent features triggered by global warming (e.g., longer growing season, earlier phenology, faster ripening, higher incidence of overheating stress and sunburn, higher frequency of extreme weather events), the type and management of training systems should also be reconsidered. We surveyed the main methods to assess training system efficiency and the current attempts and outlook toward exploiting the training system as an adaptation tool to climate change. For the latter, we considered 12 main trellis types and scored them based on climate-related features and general traits such as vigor, yield control, susceptibility to fungal diseases, and suitability according to wine types (still or sparkling). The resulting balance of positive and negative recommendations leads to a re-evaluation of either old, nonmechanizable trellis types (e.g., Raggi-Bellussi and pergola types), divided canopy systems (e.g., GDC and Scott Henry) or, among the single canopy types, of the single high wire (SHW) trellis. However, historical systems traditionally used by best regions and producers (e.g., goblet and VSP either cane- or spur-pruned) overall show less adherence to the chosen evaluation criteria. To direct future evolution of training systems, regardless of the broadly shared need for suitability to partial or full mechanization, the scenario looks different depending on cool and temperate (warm) areas. The former experiences an outburst of interest as warming is broadening growing areas and affordable genotypes. Under such circumstances, training systems should help accelerate or favor the ripening process through vigor control and lower yield, better cluster exposure, and nonlimiting leaf area-to-fruit ratio. Whereas, in warm areas that are now becoming sub-tropical areas in the worst cases, the SHW gains credit as compared to goblet and traditional VSP. The latter requires an increasing number of canopy manipulations and a rethinking of some planting choices to accommodate the needs of slower and more delayed ripening, more cluster shading, and higher cordons, the latter reducing the probability of incurring significant frost damage.

Electromagnetic analysis of HCCB TBM-set during plasma major disruptions

Electromagnetic (EM) loads (i.e., Lorentz and Maxwell loads) on the Chinese (CN) Helium-Cooled Ceramic Breeder (HCCB) Test Blanket Module set (TBM-set) as well as on the installation Frame are numerically estimated using the ANSYS software. ITER Global Model (IGM) based on the edge-flux formulation is utilized in EM analysis. The current Preliminary Design (PD) model of HCCB TBM-set is taken into account. Four typical plasma Major Disruptions (MDs) are considered in accordance with the system load specifications. It is discovered that both kinds of EM loads are dominated by the radial component. The MD_DW_III is the worst case in terms of Lorentz loads, while the considered MDs have almost the same maximum Maxwell loads which appear under the condition of plasma start of burn. Regarding the EM loads on two TBMs, the Maxwell Force (MF) is much larger compared with the Lorentz Force (LF), however the Maxwell Torque (MT) is far smaller than the maximum Lorentz Torque (LT). The obtained results will be applied in corresponding structural analysis to ensure the integrity of HCCB TBM-set in the further work.

Automatic testing of runtime enforcers with Test4Enforcers

Users regularly use apps to access services in a range of domains, such as health, productivity, entertainment, and business. The safety and correctness of the runtime behavior of these apps is thus a key concern for users. Indeed, unreliable apps may generate dissatisfaction, frustration and issues to users.Runtime enforcement techniques can be used to implement software enforcers that monitor executions and apply corrective actions when needed, potentially preventing misbehaviors and failures. However, enforcers might be faulty themselves, applying the wrong actions or missing to apply the right actions.To address this problem, this paper presents Test4Enforcers, an approach to automatically test software enforces. Test4Enforcers relies on an enforcement model describing the strategy that shall be applied at runtime to correct misbehaviors. Test4Enforcers first uses the enforcement model to derive a specification of the test cases that shall be executed to validate any software enforcer implemented from the given model. Then, it automatically turns the test specification into a set of concrete test cases that can be executed against apps augmented with the enforcers.We evaluated Test4Enforces with a set of 3,135 faults injected in the enforcers derived from 13 enforcement models. Results show that Test4Enforcers can automatically reveal 64% of the faults, while existing approaches relying on crash detection can only reveal 6% of the faults. Test4Enforcers is also practical since testing an enforcer required 9 min, in the worst case.

Reduced-Order Neural Network Synthesis With Robustness Guarantees.

In the wake of the explosive growth in smartphones and cyber-physical systems, there has been an accelerating shift in how data are generated away from centralized data toward on-device-generated data. In response, machine learning algorithms are being adapted to run locally on board, potentially hardware-limited, devices to improve user privacy, reduce latency, and be more energy efficient. However, our understanding of how these device-orientated algorithms behave and should be trained is still fairly limited. To address this issue, a method to automatically synthesize reduced-order neural networks (having fewer neurons) approximating the input-output mapping of a larger one is introduced. The reduced-order neural network's weights and biases are generated from a convex semidefinite program that minimizes the worst case approximation error with respect to the larger network. Worst case bounds for this approximation error are obtained and the approach can be applied to a wide variety of neural networks architectures. What differentiates the proposed approach to existing methods for generating small neural networks, e.g., pruning, is the inclusion of the worst case approximation error directly within the training cost function, which should add robustness to out-of-sample data points. Numerical examples highlight the potential of the proposed approach. The overriding goal of this article is to generalize recent results in the robustness analysis of neural networks to a robust synthesis problem for their weights and biases.

NEMESIS - EXAMPLE OF OPEN ACCESS JOURNAL PROMOTING NEGATIVE RESULTS IN SCHOLARLY PUBLICATIONS

Nemesis (Negative Effects in MEdical ScIenceS) is peer-reviewed, free, open-access scientific University-founded journal (UCLouvain, Belgium in 2018). Currently most of the scientific journals accept to publish only ”positive effects”of experimental and clinical research. Only successful studies have the right to be published and cited. However, positive effects in research are frequently achieved after years of negative results. The negative results should also be accessible for scientific community as they represent the main source of progress, of inspiration, and of hope. In the open access journal all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. There are no charges for authors neither for readers. Nemesis is using creative common license CC-BY-SA. The peer-review is performed by the Editorial Board. Nemesis accepts for publication: original contribution in the field of oral and craniomaxillofacial surgery, three-dimensional cone beam computed tomography (CBCT) dentomaxillofacial imaging, and physical anthropology and archaeology around the head. The first scope of Nemesis is to accept studies providing with negative results obtained after applying a correct methodology. Nemesis journal accepts also studies describing complications and their management, the worst cases, diagnostic and treatment failures, devices and implants failures, orphan diseases, neglected areas of research related to our journal scope, drugs adverse effects, technical problems, relapse and failures in oral and maxillofacial surgery. Nemesis is opened to multilingualism and provides translations of original articles in English and French into Polish and Ukrainian.

Tensor Recovery With Weighted Tensor Average Rank.

In this article, a curious phenomenon in the tensor recovery algorithm is considered: can the same recovered results be obtained when the observation tensors in the algorithm are transposed in different ways? If not, it is reasonable to imagine that some information within the data will be lost for the case of observation tensors under certain transpose operators. To solve this problem, a new tensor rank called weighted tensor average rank (WTAR) is proposed to learn the relationship between different resulting tensors by performing a series of transpose operators on an observation tensor. WTAR is applied to three-order tensor robust principal component analysis (TRPCA) to investigate its effectiveness. Meanwhile, to balance the effectiveness and solvability of the resulting model, a generalized model that involves the convex surrogate and a series of nonconvex surrogates are studied, and the corresponding worst case error bounds of the recovered tensor is given. Besides, a generalized tensor singular value thresholding (GTSVT) method and a generalized optimization algorithm based on GTSVT are proposed to solve the generalized model effectively. The experimental results indicate that the proposed method is effective.

Fault-tolerant Safe Control for Water Networks: An Interconnected Switched System Approach

This paper investigates the fault-tolerant safe control problem in water network systems in the presence of process and actuator faults. Firstly, based on the established watersafe benchmark, the studied water network is modeled as an interconnected switched system, and a fault-tolerant regulation problem is proposed while ensuring the states of the system remain within safe regions. Secondly, based on the stability theories for interconnected and switched systems, considering the worst cases of leakage and pump faults, a proper state-dependent safety-guaranteed switching law and distributed control policies are designed. Thirdly, the stability and safety of the proposed approach are proved in theory. Finally, simulations are conducted to validate the effectiveness of the proposed approach on a benchmark water transport system.

Optimization of open optical communication with multiple UAV over non homogenous turbulent paths

The article is devoted to optimizing communication through open atmospheric channels with many UAV by using inhomogeneous turbulent paths. A probabilistic assessment of the total signal loss in the communication system between the central communication node and a group of UAV located at the same distance from this node is analyzed. The case is considered when the scintillation indices in the subzones of optical communication with the UAV are different, which leads to unequal signal losses in the corresponding optical channels. The target functional is compiled by using a certain integral of the lognormal probability density function, which includes the newly introduced function of the losses dependence from the scintillation index. An analytical dependence of these indicators, characterizing the worst case when the total losses in the network reach a maximum, is presented. Keywords optical communications, turbulence, UAV, optimization, atmosphere

QoE-Aware Decentralized Task Offloading and Resource Allocation for End-Edge-Cloud Systems: A Game-Theoretical Approach

Due to the limited computing resource and battery capability at the mobile devices, the computation-intensive tasks generated by mobile devices can be offloaded to edge servers or cloud for processing. In this paper, we study the multi-user task offloading problem in an end-edge-cloud system, in which all user devices compete for the limited communication and computing resources. Particularly, we first formulate the offloading problem with the goal of maximizing the Quality of Experience (QoE) of the users subject to resource constraints. Since each user focuses on maximizing its own QoE, we reformulate the problem as a Multi-User Task Offloading Game (MUTO-Game). We then identify an important property that for any device, both the communication interference and the degree of computing resource competition can be upper bounded. Based on the property, we further theoretically prove that there exists at least one Nash Equilibrium offloading strategy in the MUTO-Game. We propose the Game-based Decentralized Task Offloading (GDTO) approach to obtain the Nash Equilibrium offloading strategy. Finally, we analyze the upper bound for the convergence time and characterize the performance guarantee of the obtained offloading strategy for the worst case. A series of experimental results are presented, in comparison with both the centralized optimal approach and the approximate approaches.

Low-Complexity Uncertainty-Set-Based Robust Adaptive Beamforming for Passive Sonar

Recent work has highlighted the potential benefits of exploiting ellipsoidal uncertainty-set-based robust Capon beamformer (RCB) techniques in passive sonar. Regrettably, the computational complexity required to form RCB weights is cubic in the number of adaptive degrees of freedom, which is often prohibitive in practice. For this reason, several low-complexity techniques for computing RCB weights, or equivalent worst case robust adaptive beamformer weights, have recently been developed. These techniques, whose complexities are only quadratic in the number of adaptive degrees of freedom, use gradient-based, reduced-dimension Krylov-subspace or Kalman-filtering methods. In this work, we review these techniques for passive sonar, analyzing their complexities and evaluating them initially on simulated data. The best performing methods are then evaluated on two in-water recorded passive sonar data sets. One set, containing a strong controlled acoustic source, demonstrates the ability of the algorithms to protect against signal cancellation when pointing at the source, and their ability to reject the source when pointing away from it. The other data set, recorded during a period when the boat was accelerating, demonstrates the ability of the algorithms to operate in the presence of speed-induced noises.

Online and Robust Intermittent Motion Planning in Dynamic and Changing Environments.

In this article, we propose RRT-Q X∞ , an online and intermittent kinodynamic motion planning framework for dynamic environments with unknown robot dynamics and unknown disturbances. We leverage RRT X for global path planning and rapid replanning to produce waypoints as a sequence of boundary-value problems (BVPs). For each BVP, we formulate a finite-horizon, continuous-time zero-sum game, where the control input is the minimizer, and the worst case disturbance is the maximizer. We propose a robust intermittent Q-learning controller for waypoint navigation with completely unknown system dynamics, external disturbances, and intermittent control updates. We execute a relaxed persistence of excitation technique to guarantee that the Q-learning controller converges to the optimal controller. We provide rigorous Lyapunov-based proofs to guarantee the closed-loop stability of the equilibrium point. The effectiveness of the proposed RRT-Q X∞ is illustrated with Monte Carlo numerical experiments in numerous dynamic and changing environments.

Verification of an open-source Python library for the simulation of district heating networks with complex topologies

District Heating Networks are seen as a popular solution for the decarbonation of space and domestic hot water heating by the use of renewables or waste heat. Their design and operation can be enhanced both environmentally and economically through the use of dedicated simulation tools.This work presents a new multicomponent Python-based software library including an efficient steady-state model for the hydraulic simulation and a Lagrange-based dynamic model for the thermal part. The model is verified on real data from a meshed DHN with a granularity of 15 min. For the considered period of 6 days, the model estimated the return temperatures of two heat plants with a mean error of 0.08 K and 0.43 K and standard deviations of 0.56 K and 0.71 K respectively. On the hydraulic part, after applying a fixed correction factor, the error on the pressure differences was estimated in 3 peripheral locations of the network. In the worst case, the relative error distribution had a mean of 0.38% and a standard deviation of 8.74%. Finally, the implemented hydraulic solver achieved a speed improvement over the standard loop method of 17.5%.

The catastrophic effects of groundwater intensive exploitation and Megadrought on aquifers in Central Chile: Global change impact projections in water resources based on groundwater balance modeling

Central Chile is undergoing its most severe drought since 2010, affecting ecosystems, water supply, agriculture, and industrial uses. The government's short-term measures, such as increasing groundwater extraction (by 383 % from 1997 to 2022), are exacerbating the situation, leading to long-term hydrological deterioration. The objective of this research is to establish the main processes driving the water table depth evolution within Central Chile over the period 1979–2023. This is done by conducting groundwater balances on five major hydrological basins of Central Chile. For the Megadrought (MD) period (2010−2022), the groundwater level depths reflect not only the recharge variability but, especially, the forcing trend of groundwater withdrawals: they represent 35 % and 65 %, respectively, of the total phreatic level drawdown. This result underlines the dominant role played by groundwater withdrawals in the current delicate state of Central Chile's groundwater resources, while revealing that drought is a new complex phenomenon to deal with, in the midterm, to revert the current water resource trend in Central Chile. Our study moreover presents the impact of climate change in the basin in the framework of six different groundwater withdrawal scenarios. In the worst case (i.e., RCP8.5), the aquifer recharge decreases 18 % with respect to 1979–1997, which is the period assumed to be unaffected by the impact of MD and withdrawals. Such a reduction may be irrelevant in the dynamics of the aquifer system if the current extraction rate does not change. The estimated recovery time needed to reach aquifer conditions equal to those of the unaffected period is approximately 50 years.

A 3D-printed fence-surface plasma source for skin treatment and its potential for personalized medical application

A 3D-printed fence-surface electrode that has the potential for personalized medical application is fabricated in this study. The framework of the 3D-printed fence electrode could be any shape to fit the outline of the target. Here, the fence electrode with an area of 50 × 50 mm is made as an example to study the discharge characteristic and the affecting factors (number of bars and the curvature of the electrode). The results show that more bars of the fence electrode with the same area will have a larger discharge current and a more uniform glowing area, which is consistent with the numerical results. When introducing the human load, it will not affect the original discharge between the fence and the ground electrodes but will add discharging channels between the fence electrode and the human load when the load contacts the fence electrode. In the worst case, the maximum root-mean-square of the discharge current flowing through the human model is 5.9 mA, which is still lower than the safety thresholds. The highest temperature rise on the surface of the fence electrode is 35.226.55 °C at the condition of 15 bars for 7 kV, 3 min running. It needs a 60 s treatment for Escherichia coli and Staphylococcus aureus to get a sterilization rate of 99.99% while it needs about 180 s treatment for Pseudomonas aeruginosa to get the same rate. Finally, the procedure for designing a personalized fence-surface plasma source is illustrated and the electrodes used to fit the calf and heel are made. The discharge of the personalized fence-electrode is stable and could be used in personalized plasma medicine in the future.

Complexity for a class of elliptic ordinary integro-differential equations

Consider the variational form of the ordinary integro-differential equation (OIDE)−u″+u+∫01q(⋅,y)u(y)dy=f on the unit interval I, subject to homogeneous Neumann boundary conditions. Here, f and q respectively belong to the unit ball of Hr(I) and the ball of radius M1 of Hs(I2), where M1∈[0,1). For ε>0, we want to compute ε-approximations for this problem, measuring error in the H1(I) sense in the worst case setting. Assuming that standard information is admissible, we find that the nth minimal error is Θ(n−min⁡{r,s/2}), so that the information ε-complexity is Θ(ε−1/min⁡{r,s/2}); moreover, finite element methods of degree max⁡{r,s} are minimal-error algorithms. We use a Picard method to approximate the solution of the resulting linear systems, since Gaussian elimination will be too expensive. We find that the total ε-complexity of the problem is at least Ω(ε−1/min⁡{r,s/2}) and at most O(ε−1/min⁡{r,s/2}ln⁡ε−1), the upper bound being attained by using O(ln⁡ε−1) Picard iterations.

In the search for all zeros of smooth functions

We consider the multi-valued problem of nding all solutions of the equation f(x) = 0 in the space of functions f : [0, 1] → R such that the derivative f(r) with r ∈ {0, 1, 2, . . .} exists and is Hölder continuous with exponent ϱ ∈ (0, 1]. Available algorithms use information about values of f and/or its derivatives at adaptively selected n points, and the error between the true solution Z(f) and approximate solution Zn(f) is measured by the Hausdor distance dH(Z(f),Zn(f)). We show that, despite the fact that the worst case error of any algorithm is innite, it is possible to construct nonadaptive approximations Z∗n such that the error dH (Z(f),Z∗n(f)) converges to zero as n → +∞. However, the convergence can be arbitrarily slow. Specically, for arbitrary sequence of approximations {Zn}n≥1 that use n adaptively chosen function values and/or its derivatives, and for arbitrary positive sequence {τn}n≥1 converging to zero there are functions f in our space such that supn≥1 τ−1n dH(Z(f),Zn(f))= +∞. We conjecture that the same lower bound holds if we allow information about values of n arbitrary and adaptively selected linear functionals at f.

Application of a composite, multi-scale COVID-19 mitigation framework: US border use-case

ABSTRACT Airborne pathogen transmission within crowded facilities can be modelled by combining several interrelated mechanisms of spread: movement of people, airflow dynamics, and aerosol dispersion. This paper describes a composite model framework combining analytical models to demonstrate the spread of an airborne pathogen in a crowded, confined space at an immigrant processing centre on the southern US border during the border crisis of March 2021. Recommendations that could reduce current COVID-19 infection rate from 11% to 6.16% at relatively low additional cost to the government are given. These recommendations could also lower the infection rate by approximately five times from 31.14% worst case from long indoor exposures down to 6.35% when immigrant processing times surge to 10 days. This work highlights the challenges of managing COVID-19 in crowded facilities, and provides quantitative decision options with potential both to slow and prevent disease spread, while lessening the economic burden on communities.

Creating conditions for sustainability transformation through transformative governance – The case of plastic food packaging in Finland

Transformative governance is an emerging approach to addressing major sustainability challenges. Despite the mounting research on this topic, there is still limited understanding on how to create conditions for transformation in practice. Through an analytical frame of transformative governance, in this qualitative case study we investigate how policies and governance in Finland have created conditions for sustainability transformation in food packaging, a key source of plastics pollution, and how the joint implementation of different transformative approaches affects their power to induce systemic change. Our results, based on policy mapping and stakeholder interviews, show that applying principles of transformative governance is necessary but not yet effective enough for creating conditions that encourage and motivate key actors to advance in sustainability transformation. Joint implementation of different transformative approaches and modes of governance can be beneficial, but it is also challenging and may in the worst case even weaken the transformative power and slow down progress. The current governance has fostered packaging waste recycling but does not effectively solve the root cause of unsustainability: mounting consumption of single-use packaging, despite current policy targets and active use of transformative approaches. To reduce packaging consumption and create favourable conditions for packaging reuse, more emphasis is needed on transformative innovation policies, as well as setting more ambitious and proactive measures such as implementing tighter legislation and carrying out ex-ante impact evaluations when designing new policies and regulation. Integrative governance should also be strengthened to improve directionality and policy coherence.