Miracle-dz/UniFAR-FTUs huggingface dataset:
Delays in Cancer Diagnostic Testing at a Quick Referral Unit in Spain during COVID-19

Although COVID-19 has had dire consequences on diagnosis of cancer, little data assessing its impact on the whole range of diagnostic activity relevant to cancer are available. We examined trends in the provision of full diagnostic tests for consecutive patients with suspected cancer referred to an academic hospital-based Quick Diagnosis Unit from January 2019 to December 2020. As weekly volumes declined, waiting times for endoscopic, imaging and biopsy/cytology procedures increased steeply during the COVID-impacted period (26 February–28 April 2020). The average weekly increase compared with the same period in 2019 was substantial for invasive procedures requiring admission (200.70%), CT scans (171.20%), GI endoscopy (161.50%), PET/CT scans (152.50%), ultrasonography (148.40%), and ambulatory biopsy/cytology procedures (111.20%). Volumes and waiting times to other procedures showed similar trends. There was a remarkable downward trend in cancer diagnosis during the COVID-impacted period, with a 54.07% reduction compared with the same weeks in 2019. Despite a modest recovery in the following months, the decline in weekly activity and cancer rates persisted until 30 December. Providing insight into how COVID-19 changed the full spectrum of diagnostic activity for suspected cancer informs resilience-building interventions to guarantee access to fast and efficient diagnostics ahead of new threats.
The Longest War: The Enduring Conflict between America and Al-Qaeda

In the last decade, America's priorities have changed. National security has become the primary concern for citizens and a top priority for government officials. Billions of dollars have been spent fighting the war on terror, and millions more have been spent by private citizens around the globe trying to understand how we got here. Investigative journalist and bestselling author of Holy War, Inc. and The Osama bin Laden I Know Peter Bergen is the authority who can answer that question. One of few Westerners to ever sit down with Osama bin Laden, Bergen is renowned for his analysis of al Qaeda and its evolution over the last decade. Now, he is breaking the story open in a way that readers have never seen before. Bergen picks up where Lawrence Wright's The Looming Tower leaves off. He reveals the details of how al Qaeda has evolved since the 9/11 attacks, and tells the parallel story of how the U.S. government has changed its own course in response. Bergen has unprecedented insight that comes through many channels: internal documents from both al Qaeda and U.S. counterterrorism offices, in person interviews with al Qaeda members of the highest and lowest ranks, hundreds of interviews conducted with senior officials in the White House, Pentagon, CIA and the FBI, and his own experiences on the ground in Afghanistan. A Very Long War will be the definitive account of the war on terror--its failures and successes, the major players, and its likely future.
Slot-coupled circular microstrip antenna having a symmetric radiation pattern

The author presents the analysis and design of a circular microstrip antenna coupled through an annular slot to a coaxial line. The annular slot is concentric with the circular patch. This method of coupling yields a symmetric radiation pattern and a linear polarization that is different from that of the conventionally fed microstrip antennas. The structure is analyzed using the finite-difference time-domain (FDTD) technique in cylindrical coordinates. Numerical results are presented. <<ETX>>
Simulation of hot hole currents in ultra-thin silicon dioxides: the relationship between time to breakdown and hot hole currents

We have investigated the relationship between the currents of hot holes injected into silicon dioxides and the time to breakdown (T/sub BD/) characteristics. The hot hole currents were calculated by combining a tunnel current simulator and a silicon full-band Monte Carlo (FBMC) simulator. Our results show that the hot hole current seems to be responsible for oxide degradation and breakdown. Moreover, the additional impact ionization process where electrons are relaxed into the valence bands plays an important role in hot hole generation in the low-gate-voltage region.
Supporting Social Interactions In Human-Ai Teams: Profiling Human Teammates From Sparse Data

Artificial intelligence has been developed to perform all manner of tasks but has not gained capabilities to support social cognition. We suggest that teams comprised of both humans and artificially intelligent agents cannot achieve optimal team performance unless all teammates have the capacity to employ social-cognitive mechanisms. These form the foundation for generating inferences about their counterparts and enable execution of informed, appropriate behaviors. Social intelligence and its utilization are known to be vital components of human-human teaming processes due to their importance in guiding the recognition, interpretation, and use of the signals that humans naturally use to shape their exchanges. Although modern sensors and algorithms could allow AI to observe most social cues, signals, and other indicators, the approximation of human-to-human social interaction -based upon aggregation and modeling of such cues is currently beyond the capacity of potential AI teammates. Partially, this is because humans are notoriously variable. We describe an approach for measuring social-cognitive features to produce the raw information needed to create human agent profiles that can be operated upon by artificial intelligences.
Stable Silicon Isotopes Uncover a Mineralogical Control on the Benthic Silicon Cycle in the Arctic Barents Sea

Biogeochemical cycling of silicon (Si) in the Barents Sea is under considerable pressure from physical and chemical changes, including dramatic warming and sea ice retreat, together with a decline in dissolved silicic acid (DSi) concentrations of Atlantic inflow waters since 1990. Moreover, further expansion of the Atlantic realm (termed `Atlantification') is expected to shift phytoplankton community compositions away from diatom-dominated spring bloomsin favour of Atlantic flagellate species (coccolithophore-dominated). The changes in pelagic primary production will alter the composition of the material comprising the depositional flux, which will subsequently in influence the recycling processes at and within the seafloor. In this study we assess the predominant controls on the early diagenetic cycling of Si, a key nutrient in marine ecosystems, by combining stable isotopic analysis of pore water DSi and of operationally defined reactive pools of the solid phase. We show that low biogenic silica (BSi) contents (0.39-0.52 wt% or 92-185 μmol g dry wt-1) drive correspondingly low asymptotic concentrations of pore water DSi (~100 μM). However, while these surface sediments appear almost devoid of BSi, we present evidence for the rapid recycling of bloom derived BSi that generates striking transient peaks in sediment pore water [DSi], which is a feature that is subject to future shifts in phytoplankton community compositions. Using a simple mass balance calculation we show that the pore water DSi pool is supplemented by a lithogenic Si source (LSi), while our sediment pore water Si isotopic profiles also uncover a coupling of the iron (Fe) and Si cycles. This has previously been observed in lower latitude marine sediment systems and thus provides further support for a redox influence on oceanic pore water DSi. We suggest that a high LSi:BSi ratio and apparent Fe (oxyhydr)oxide influence could lead to a degree of stability in the annual background benthic flux of DSi despite the pressures on pelagic phytoplankton communities. Coupled with supporting isotopic evidence for the precipitation of authigenic clays in Barents Sea sediment cores, our observations have implications for the sink vs recycling terms in the regional Si budget.
Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information

In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-signal-acquisition devices. Another strategy employs facial-image-based stress-recognition methods, which do not require devices, but predominantly use handcrafted features. However, such features have low discriminating power. We propose a deep-learning-based stress-recognition method using facial images to address these challenges. Given that deep-learning methods require extensive data, we constructed a large-capacity image database for stress recognition. Furthermore, we used temporal attention, which assigns a high weight to frames that are highly related to stress, as well as spatial attention, which assigns a high weight to regions that are highly related to stress. By adding a network that inputs the facial landmark information closely related to stress, we supplemented the network that receives only facial images as the input. Experimental results on our newly constructed database indicated that the proposed method outperforms contemporary deep-learning-based recognition methods.
Association of Mean Platelet Volume (MPV) in Relation to Disease Activity in Lupus

Introduction: Among the various clinical and biochemical parameters which are employed to monitor the disease activity in Systemic Lupus Erythematosus (SLE), Mean Platelet Volume, which is a platelet activation biomarker has been recently studied. We intended at evaluating the MPV in patients with active SLE and comparing it with the same patients who were in clinical remission, and to study the correlation between MPV and SLEDAI. We also studied the correlation between MPV and ESR, complements C3 and C4. Methods: This is a prospective study conducted for a period of 12 months, in which 50 consecutive patients who were recently diagnosed with SLE according to the SLICC classification criteria were included. Complete blood count with MPV levels along with ESR, were measured at the first visit to the hospital and were repeated at every visit till they attained clinical remission. Analysis of MPV was done at first visit and at remission. Complements C3 and C4 was done at the first visit and at remission. The results were then tabulated and statistically analysed using SPSS 25. A Pearson’s correlation test was done to assess the relation between MPV, ESR and SLEDAI. Results: Patients with active disease had a decreased MPV as compared to those in clinical remission (10.91fl, 13.11fl, p=0.011). We observed a weak positive correlation between MPV and SLEDAI (r=0.034, p=0.011). We observed no correlation between MPV and ESR, C3 and C4. Meanwhile, SLEDAI showed a positive correlation with C3 and C4; there was no correlation with ESR though. Conclusion: MPV is reduced in patients with active SLE and displayed an inverse correlation with SLEDAI.
What happened when? Brain and behavioral responses to violated expectations about the structure and content of episodic memories

Episodic memories are not static but can be modified on the basis of new experiences, potentially allowing us to make valid predictions in the face of an ever-changing environment. Recent research has identified mnemonic prediction errors as a possible trigger for such modifications. In the present study, we investigated the influence of different types of mnemonic prediction errors on brain activity and subsequent memory performance using a novel paradigm for episodic modification. Participants encoded different episodes which consisted of short toy stories. During a subsequent functional magnetic resonance imaging (fMRI) session, episodic retrieval was cued by presenting videos showing the original episodes, or modified versions thereof. In modified videos either the order of two subsequent action steps was changed (violating structure expectancy) or an object was exchanged for another (violating content expectancy). While brain responses to structure expectancy violations were only subtle, content expectancy violations recruited brain areas relevant for processing of new object information. In a post-fMRI memory test, the participants’ tendency to accept modified episodes as originally encoded increased significantly when they had experienced expectancy violations during the fMRI session. Our study provides valuable initial insights into the neural processing of different types of mnemonic prediction errors and their influence on subsequent memory.
Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach

With the rapid development of vehicular crowdsensing, it becomes easier and more efficient for mobile devices to sense, compute, and measure various data. However, how to address the fair quality evaluation between the platform and participants while preserving the privacy of solutions is still a challenge. In the work, we present a fairness-aware and privacy-preserving scheme for worker quality evaluation by leveraging the blockchain, trusted execution environment (TEE), and machine learning technologies. Specifically, we build our framework atop the decentralized blockchain which can resist a single point of failure/compromise. The smart contracts paradigm in blockchain enforces correct and automatic program execution for task processing. In addition, machine learning and TEE are utilized to evaluate the quality of data collected by the sensors in a privacy-preserving and fair way, eliminating human subject judgement of the sensing solutions. Finally, a prototype of the proposed scheme is implemented to verify the feasibility and efficiency with a benchmark dataset.
MODELLING AND EVALUATION OF THE DIFFUSION OF ELECTRIC VEHICLES: EXISTING MODELS, RESULTS AND PROPOSAL FOR A NEW MODEL

In this paper we review the available methods, models and results about the diffusion of electric vehicles and the evaluation of related policy. We show that existing models often relate to contexts that are not relevant for the design of policies that could take place in Europe. We also find that many models rely on exogenous diffusion assumption and are not intended to simulate the effects of alternative policy packages, which can significantly limit their scope. Moreover we find that only a few of the studies presenting themselves as costs-benefit analysis really perform what they claim. We also draw some conclusions on the features of models that would be needed to derive recommendations relevant in the European policy framework
A Strategy For Creating Design Methods Based On Social Behaviours For Pleasurable User Experience In Human Computer Interaction

This paper reports research into the identification of a strategy for developing situation specific design methods to produce interface design guidelines that include the social and emotional nature of users to create pleasurable user experiences in human-computer interaction (HCI) in retail e-commerce. The paper conceptualises a new way to use information about user experiences in specific real world social interactions to synthesise interface design guidelines based on specific identified social behaviours (i.e. situation-specific design methods or guidelines). This focus on the development of a strategy to build design methods, rather than the design methods themselves, situates this research on a different conceptual level compared to research directed at design methods themselves.
Growth and Nutrient Uptake Characteristics of Heterosigma akashiwo (Raphidophyceae) under Nitrogen and Phosphorus Concentrations in the East China Sea

Heterosigma akashiwo is classified as a harmful algal bloom (HAB) species that frequently occurs in eutrophic coastal waters and results in the contamination and mortality of fish and shellfish. The growth of H. akashiwo in four phosphate and nitrate concentration scenarios, representing the observed nutrient concentration ranges in the East China Sea (ECS), was evaluated to further understand the effect of nutrient concentrations on H. akashiwo blooms. The specific growth rate in the exponential growth phase (µ′) and the maximum cell density were lower (17–21% and 41%, respectively) under low phosphorus concentration scenarios, compared to the rates observed under high phosphorus concentration scenarios. The cellular nitrogen-to-phosphorus ratios of H. akashiwo were influenced by the initially supplied N:P ratio and the allocation strategy employed. Phosphorus concentration had a greater influence on the total growth of H. akashiwo than nitrate did, within the natural nutrient conditions of the ECS. These results could serve as a reference for coastal water management and marine ecological management and may be useful for further studies on the simulation and prediction of H. akashiwo blooms, particularly in the ECS.
Near-field radiation patterns of electrically small thin rectangular/square loop antennas

This paper presents an alternative vector analysis of the electromagnetic (EM) fields radiated from electrically small thin square and rectangular loop antennas of arbitrary length 2a and width 2b are introduced. This method which employs the dyadic Green's function (DGF) in the derivation of the EM radiated fields makes the analysis general, compact and straightforward. Both near- and far-zones are considered so that the EM radiated fields are expressed in terms of the vector wave eigenfunctions. Not only the exact solution of the EM fields in the near and far zones outside the region (where r>/spl radic/(a/sup 2/+b/sup 2/)) is derived by the use of the spherical Hankel function of the first kind, but also the closed series form of the EM fields radiated in the near zone inside the region 0/spl les/r<b is obtained in series of spherical Bessel functions of the first kind. The true regions between the radial distances b and /spl radic/(a/sup 2/+b/sup 2/) which are defined as intermediate zones are characterized by both the spherical Bessel and Hankel functions of the first kind. Validity of the numerical results is discussed and clarified.
Robust and unbiased estimation of the background distribution for automated quantitative imaging

Quantitative analysis of high-throughput microscopy images requires robust automated algorithms. Background estimation is usually the first step and has an impact on all subsequent analysis, in particular for foreground detection and calculation of ratiometric quantities. Most methods recover only a single background value, such as the median. Those that aim to retrieve a background distribution by dividing the intensity histogram yield a biased estimation in images in non-trivial cases. In this work, we present the first method to recover an unbiased estimation of the background distribution directly from an image and without any additional input. Through a robust statistical test, our method leverages the lack of local spatial correlation in background pixels to select a subset of pixels that accurately represent the background distribution. This method is both fast and simple to implement, as it only uses standard mathematical operations and an averaging filter. Additionally, the only parameter, the size of the averaging filter, does not require fine tuning. The obtained background distribution can be used to test for foreground membership of individual pixels, or to estimate confidence intervals in derived quantities. We expect that the concepts described in this work can help to develop a novel family of robust segmentation methods.
Detecting and preventing control-flow hijacking attacks in commodity software

Control-flow hijacking attacks allow an attacker to subvert a value that is loaded into the program counter of a running program, typically redirecting execution to his own injected code. Such attacks can be used to completely subvert a remote system, and can rapidly infect all vulnerable machines when used by a fast-spreading worm. Currently, users of commodity software must rely on the vendor to develop and release a patch for a new vulnerability. Even when a patch is available, the end user must verify that it fixes the vulnerability without breaking functionality before installing it. In the time between the vulnerability becoming known to attackers, and the time that a patch is released and tested, the user is at the mercy of remote attackers if he continues to run the vulnerable program. In this work, we address the problem of how end-users or 3rd parties can automatically (1) detect and diagnose attacks against previously unknown vulnerabilities, and (2) prevent attacks against diagnosed vulnerabilities with minimal impact on performance and functionality. A workable solution must do this quickly, automatically, and without source code of the vulnerable program. Specifically, we detect attacks against unknown control-flow hijacking vulnerabilities by using dynamic taint analysis to detect when a value loaded into the program counter has been influenced by data from the network. When an attack is detected, we weed out false positives and confirm true positives by using the logged program execution to quantify how much influence the network data gained over the value loaded into the program counter. For true positives, we rewrite the vulnerable binary to detect subsequent attacks against the same vulnerability with very little performance overhead. These techniques have been used as the basis for Sting and Sweeper, which provide efficient and effective end-to-end defense against control-flow hijacking attacks in commodity software, even when used by a fast-spreading zero-day worm.
Estimating Bounds of Aerodynamic, Mass, and Auxiliary Load Impacts on Autonomous Vehicles: A Powertrain Simulation Approach

Vehicle automation requires new onboard sensors, communication equipment, and/or data processing units, and may encourage modifications to existing onboard components (such as the steering wheel). These changes impact the vehicle’s mass, auxiliary load, coefficient of drag, and frontal area, which then change vehicle performance. This paper uses the powertrain simulation model FASTSim to quantify the impact of autonomy-related design changes on a vehicle’s fuel consumption. Levels 0, 2, and 5 autonomous vehicles are modeled for two battery-electric vehicles (2017 Chevrolet Bolt and 2017 Nissan Leaf) and a gasoline powered vehicle (2017 Toyota Corolla). Additionally, a level 5 vehicle is divided into pessimistic and optimistic scenarios which assume different electronic equipment integration format. The results show that 4–8% reductions in energy economy can be achieved in a L5 optimistic scenario and an 10–15% increase in energy economy will be the result in a L5 pessimistic scenario. When looking at impacts on different power demand sources, inertial power is the major power demand in urban driving conditions and aerodynamic power demand is the major demand in highway driving conditions.
Mapping the field of misinformation correction and its effects: A review of four decades of research

Why people still rely on misinformation after clear corrections is a major concern driving relevant research. Different fields, from psychology to marketing, have been seeking answers. Yet there remains no systematic review to integrate these theoretical and empirical insights. To fill the gap, this article reviewed 135 articles on misinformation correction and its effects written before 2020 to examine the knowledge generated in the field. Our findings indicate a consistent interest on this topic over the past four decades, and a sharp increase of relevant scholarly work in the last ten years. Nevertheless, most studies have been built upon psychological inquiries and quantitative methodologies. What is lacking includes longitudinal measurements of debunking effectiveness, theoretical insights beyond cognitive sciences, methodological contributions from qualitative approaches, and empirical evidence from non-western societies. With this analysis, we propose worthwhile focuses for future exploration.