Miracle-dz/UniFAR-FTUs huggingface dataset:
An Information Theoretic Approach for Attention-Driven Face Forgery Detection

To further demonstrate the effectiveness of our proposed SIA, we conduct quantitative results on the DFDC dataset [3]. DFDC is a large-scale deepfake datasets that contains 1133 real videos and 4080 fake videos with several manipulated methods. The results in Tab.1 show that our method achieves SOTA performance compared with recently face forgery detection methods. Specifically, our method outperforms the Multi-Attentional method by around 3% in terms of ACC, which demonstrate the self-information can provide more guidance for attention mechanism and the effectiveness of the design of dual attention scheme.
Collaboration, not Confrontation: Understanding General Practitioners’ Attitudes Towards Natural Language and Text Automation in Clinical Practice

General Practitioners are among the primary users and curators of textual electronic health records, highlighting the need for technologies supporting record access and administration. Recent advancements in natural language processing facilitate the development of clinical systems, automating some time-consuming record-keeping tasks. However, it remains unclear what automation tasks would benefit clinicians most, what features such automation should exhibit, and how clinicians will interact with the automation. We conducted semi-structured interviews with General Practitioners uncovering their views and attitudes toward text automation. The main emerging theme was doctor-AI collaboration, addressing a reciprocal clinician-technology relationship that does not threaten to substitute clinicians, but rather establishes a constructive synergistic relationship. Other themes included: (i) desired features for clinical text automation; (ii) concerns around clinical text automation; and (iii) the consultation of the future. Our findings will inform the design of future natural language processing systems, to be implemented in general practice.
Developing knowledge graph based system for urban computing

Everyday our living city produces a tremendous amount of spatial-temporal data, involved with multiple sources from the individual scale to the city scale. Undoubtedly, such massive urban data can be explored for a better city and better life, as what the urban computing community has been dedicating in recent years. Nevertheless, existing studies are still facing the challenges of data fusion for the urban data as well as the knowledge distillation for specific applications. Moreover, there is a lack of full-featured and user-friendly platform for both researchers and developers in urban computing scenario. Therefore, in this paper, we present an urban knowledge graph (UrbanKG) system to incorporate knowledge graph with urban computing. Specifically, the system introduces a complete scheme to construct knowledge graph for urban data fusion from Data layer to Construction layer. The system further develops the multiple layers of Storage, Algorithm, Operation and Applications, which achieve the knowledge distillation and support various functions to the users. We perform three representative and practical use cases and demonstrate the system capability of boosting performance in various downstream applications, indicating a promising research direction of knowledge-driven urban computing.
Redefining and Evaluating Coverage Criteria Based on the Testing Scope

Test coverage information can help testers in deciding when to stop testing and in augmenting their test suites when the measured coverage is not deemed sufficient. Since the notion of a test criterion was introduced in the 70’s, research on coverage testing has been very active with much effort dedicated to the definition of new, more cost-effective, coverage criteria or to the adaptation of existing ones to a different domain. All these studies share the premise that after defining the entity to be covered (e.g., branches), one cannot consider a program to be adequately tested if some of its entities have never been exercised by any input data. However, it is not the case that all entities are of interest in every context. This is particularly true for several paradigms that emerged in the last decade (e.g., component-based development, service-oriented architecture). In such cases, traditional coverage metrics might not always provide meaningful information. In this thesis we address such situation and we redefine coverage criteria so to focus on the program parts that are relevant to the testing scope. We instantiate this general notion of scope-based coverage by introducing three coverage criteria and we demonstrate how they could be applied to different testing contexts. When applied to the context of software reuse, our approach proved to be useful for supporting test case prioritization, selection and minimization. Our studies showed that for prioritization we can improve the average rate of faults detected. For test case selection and minimization, we can considerably reduce the test suite size with small to no extra impact on fault detection effectiveness. When the source code is not available, such as in the service-oriented architecture paradigm, we propose an approach that customizes coverage, measured on invocations at service interface, based on data from similar users. We applied this approach to a real world application and, in our study, we were able to predict the entities that would be of interest for a given user with high precision. Finally, we introduce the first of its kind coverage criterion for operational profile based testing that exploits program spectra obtained from usage traces. Our study showed that it is better correlated than traditional coverage with the probability that the next test input will fail, which implies that our approach can provide a better stopping rule. Promising results were also observed for test case selection. Our redefinition of coverage criteria approaches the topic of coverage testing from a completely different angle. Such a novel perspective paves the way for new avenues of research towards improving the cost-effectiveness of testing, yet all to be explored.
The SAMI Galaxy Survey: satellite galaxies undergo little structural change during their quenching phase

At fixed stellar mass, satellite galaxies show higher passive fractions than centrals, suggesting that environment is directly quenching their star formation. Here, we investigate whether satellite quenching is accompanied by changes in stellar spin (quantified by the ratio of the rotational to dispersion velocity V/sigma for a sample of massive (M* > 10(10) M-circle dot) satellite galaxies extracted from the Sydney-AAO Multi-object Integral field spectrograph (SAMI) Galaxy Survey. These systems are carefully matched to a control sample of main sequence, high V/s central galaxies. As expected, at fixed stellar mass and ellipticity, satellites have lower star formation rate (SFR) and spin than the control centrals. However, most of the difference is in SFR, whereas the spin decreases significantly only for satellites that have already reached the red sequence. We perform a similar analysis for galaxies in the Evolution and Assembly of GaLaxies and their Environments (EAGLE) hydrodynamical simulation and recover differences in both SFR and spin similar to those observed in SAMI. However, when EAGLE satellites are matched to their true central progenitors, the change in spin is further reduced and galaxies mainly show a decrease in SFR during their satellite phase. The difference in spin observed between satellites and centrals at z similar to 0 is primarily due to the fact that satellites do not grow their angular momentum as fast as centrals after accreting into bigger haloes, not to a reduction of V/s due to environmental effects. Our findings highlight the effect of progenitor bias in our understanding of galaxy transformation and they suggest that satellites undergo little structural change before and during their quenching phase.
Learning-based Phase-aware Multi-core CPU Workload Forecasting

Predicting workload behavior during workload execution is essential for dynamic resource optimization in multi-processor systems. Recent studies have proposed advanced machine learning techniques for dynamic workload prediction. Workload prediction can be cast as a time series forecasting problem. However, traditional forecasting models struggle to predict abrupt workload changes. These changes occur because workloads are known to go through phases. Prior work has investigated machine-learning-based approaches for phase detection and prediction, but such approaches have not been studied in the context of dynamic workload forecasting. In this article, we propose phase-aware CPU workload forecasting as a novel approach that applies long-term phase prediction to improve the accuracy of short-term workload forecasting. Phase-aware forecasting requires machine learning models for phase classification, phase prediction, and phase-based forecasting that have not been explored in this combination before. Furthermore, existing prediction approaches have only been studied in single-core settings. This work explores phase-aware workload forecasting with multi-threaded workloads running on multi-core systems. We propose different multi-core settings differentiated by the number of cores they access and whether they produce specialized or global outputs per core. We study various advanced machine learning models for phase classification, phase prediction, and phase-based forecasting in isolation and different combinations for each setting. We apply our approach to forecasting of multi-threaded Parsec and SPEC workloads running on an eight-core Intel Core-i9 platform. Our results show that combining GMM clustering with LSTMs for phase prediction and phase-based forecasting yields the best phase-aware forecasting results. An approach that uses specialized models per core achieves an average error of 23% with up to 22% improvement in prediction accuracy compared to a phase-unaware setup.
Science cases for a visible interferometer

High spatial resolution is the key for the understanding various astrophysical phenomena. But even with the future E-ELT, single dish instruments are limited to a spatial resolution of about 4 mas in the visible. For the closest objects within our Galaxy most of the stellar photosphere remains smaller than 1 mas. With the success of long baseline interferometry these limitations were soom overcome. Today low and high resolution interferometric instruments on the VLTI and CHARA offer an immense range of astrophysical studies. Combining more telescopes and moving to visible wavelengths broadens the science cases even more. With the idea of developing strong science cases for a future visible interferometer, we organized a science group around the following topics: pre-main sequence and main sequence stars, fundamental parameters, asteroseismology and classical pulsating stars, evolved stars, massive stars, active galactic nuclei (AGNs) and imaging techniques. A meeting was organized on the 15th and 16th of January, 2015 in Nice with the support of the Action Specific in Haute Resolution Angulaire (ASHRA), the Programme National en Physique Stellaire (PNPS), the Lagrange Laboratory and the Observatoire de la Cote d'Azur, in order to present these cases and to discuss them further for future visible interferometers. This White Paper presents the outcome of the exchanges. This book is dedicated to the memory of our colleague Olivier Chesneau who passed away at the age of 41.
Alteration in NMDAR subunits in different brain regions of chronic unpredictable mild stress (CUMS) rat model

Abstract N-Methyl-d-aspartate receptor (NMDAR) signaling pathway has been implicated in the pathogenesis and treatment of depression. However, the role of NMDAR subunits in depression is still unclear. In this study, alteration in all seven NMDAR subunits in several brain areas of rats exposed to chronic unpredictable mild stress (CUMS), an animal model of depression, was detected. Our findings demonstrated that: (1) CUMS could induce a reduction in sucrose preference, an indicator of typical depression-like behaviors; (2) CUMS significantly reduced the NMDAR subunits of GluN2B and GluN3 in the medial prefrontal cortex (mPFC), but not altered all seven NMDAR subunits in hippocampus and corpus callosum of rats; (3) subunit composition of NMDARs in corpus callosum was different from that in mPFC, PFC and hippocampus; and (4) the mRNA expressions of GluN2B, GluN3A and GluN3B in mPFC as well as mRNA expression of GluN2C in corpus callosum were correlated to sucrose preference in rats. These findings suggested that GluN2B and GluN3 in mPFC may contribute to the pathophysiology of depression.
Multiplicity distribution and normalized moments in p–p collisions at LHC in forward rapidity using Weibull model

The measured charged particle multiplicity distribution in the forward rapidity region of the p$-$p collisions at $\sqrt{s}$ = 0.9 TeV, 7 TeV and 8 TeV at the LHC have been described using the Weibull distribution function. The higher order (up to $5^{th}$ order ) normalized moments and the factorial moments are also calculated using the extracted parameters. The multiplicity distributions in forward region are observed to be well described by the Weibull regularity and the higher order moment calculations confirm the violation of KNO scaling that has been observed at mid-rapidity and lower energies. The Weibull parameters and moments for p$-$p collisions at $\sqrt{s}$ = 13 TeV are also estimated for the forward region.
On the possibility of measuring the gravitomagnetic clock effect in an Earth space-based experiment

In this paper the effect of the post-Newtonian gravitomagnetic force on the mean longitudes l of a pair of counter-rotating Earth artificial satellites following almost identical circular equatorial orbits is investigated and the possibility of measuring it is examined. The observable is the difference of the times required for l to pass from 0 to 2π for both senses of motion. Such a gravitomagnetic time shift, which is independent of the orbital parameters of the satellites, amounts to 5×10−7 s for the Earth; it is cumulative and should be measurable after a sufficiently high number of revolutions. The major limiting factors are the unavoidable imperfect cancellation of the Keplerian periods, which yields a constraint of 10−2 cm in knowing the difference between the semimajor axes a of the satellites, and the difference I of the inclinations i of the orbital planes which, for i ∼ 0.01°, should be less than 0.006°. A pair of spacecraft endowed with a sophisticated intersatellite tracking apparatus and drag-free control down to 10-9 cm s-2 Hz-1/2 level might allow us to meet the stringent requirements posed by such a mission.
Dynamics and morphology of the Milky Way spiral arms from the metallicity distribution and radial mixing

Albeit radial migration must be a ubiquitous process in disc galaxies, its significance in the evolution of stellar discs is not always reflected through global trends. However, there are other key observables, such as the metallicity distribution function (MDF), that may shed some light in this matter. We argue that the shape of the MDF not only tells us whether the stellar disc experienced radial migration, but it also contains important clues on the structure that triggered it. Specifically, the MDF contains information about the dynamics and morphology of the spiral pattern. To constrain the spiral parameters, we have included a detailed chemical tagging in our simulations; this allows us to produce a restriction of the structural parameters of the spiral arms in the Milky Way as well as a method to constrain chemical evolution models towards the center of the Galactic disc, where no chemical model provides information. We also found that it is unlikely that the Sun was formed near its current galactocentric position, therefore it might be inaccurate to consider the Sun as representative of the chemical abundances in the solar neighborhood. We also show that a stellar disc of the Milky Way, after evolving dynamically and chemically for 5 Gyr, preserves 80% of its original global metallicity gradient despite having suffered important heating and radial migration; this means that the presence of a metallicity gradient in a given galaxy, does not guarantee that radial mixing has not played a role in its evolution.
Multidimensional Network analysis

This thesis is focused on the study of multidimensional networks. A multidimensional network is a network in which among the nodes there may be multiple different qualitative and quantitative relations. Traditionally, complex network analysis has focused on networks with only one kind of relation. Even with this constraint, monodimensional networks posed many analytic challenges, being representations of ubiquitous complex systems in nature. However, it is a matter of common experience that the constraint of considering only one single relation at a time limits the set of real world phenomena that can be represented with complex networks. When multiple different relations act at the same time, traditional complex network analysis cannot provide suitable analytic tools. To provide the suitable tools for this scenario is exactly the aim of this thesis: the creation and study of a Multidimensional Network Analysis, to extend the toolbox of complex network analysis and grasp the complexity of real world phenomena. The urgency and need for a multidimensional network analysis is here presented, along with an empirical proof of the ubiquity of this multifaceted reality in different complex networks, and some related works that in the last two years were proposed in this novel setting, yet to be systematically defined. Then, we tackle the foundations of the multidimensional setting at different levels, both by looking at the basic extensions of the known model and by developing novel algorithms and frameworks for well-understood and useful problems, such as community discovery (our main case study), temporal analysis, link prediction and more. We conclude this thesis with two real world scenarios: a monodimensional study of international trade, that may be improved with our proposed multidimensional analysis; and the analysis of literature and bibliography in the field of classical archaeology, used to show how natural and useful the choice of a multidimensional network analysis strategy is in a problem traditionally tackled with different techniques.
This paper is included in the Proceedings of the Eighteenth Symposium on Usable Privacy and Security (SOUPS 2022)

Passwords are the most popular authentication method due to their simplicity and widespread adoption. However, the prevalence of password reuse undermines its security. A promising strategy to mitigate the risks of password reuse is to use random passwords generated and stored by password managers, yet many users do not use them. Many web browsers have built-in password managers that employ nudges at the time of password creation. These nudges aim to persuade the selection of more secure random passwords; however, little is known about which designs are most effective. We study ( n = 558) the efficacy of nudges used by three popular web browsers: Chrome, Firefox, and Safari. Our results suggest Safari’s nudge implementation is significantly more effective than the others at nudging users to adopt a randomly generated password. We examine factors that may contribute to the adoption of randomly generated passwords, reasons that people adopt a randomly generated password (or not), as well as discuss elements of Safari’s nudge design that may contribute to its success. Our findings can be useful in informing both future password manager nudge designs and interventions to encourage password manager use.
Swift/XRT–NuSTAR spectra of type 1 AGN: confirmingINTEGRALresults on the high-energy cut-off

We present the 0.5 - 78 keV spectral analysis of 18 broad line AGN belonging to the INTEGRAL complete sample. Using simultaneous Swift-XRT and NuSTAR observations and employing a simple phenomenological model to fit the data, we measure with a good constraint the high energy cut-off in 13 sources, while we place lower limits on 5 objects. We found a mean high-energy cut-off of 111 keV (standard deviation = 45 keV) for the whole sample, in perfect agreement with what found in our previous work using non simultaneous observations and with what recently published using NuSTAR data. This work suggests that simultaneity of the observations in the soft and hard X-ray band is important but not always essential, especially if flux and spectral variability are properly accounted for. A lesser agreement is found when we compare our cut-off measurements with the ones obtained by Ricci et al. (2017) using Swift-BAT high energy data, finding that their values are systematically higher than ours. We have investigated whether a linear correlation exists between photon index and the cut-off and found a weak one, probably to be ascribed to the non perfect modelling of the soft part of the spectra, due to the poor statistical quality of the 2-10 keV X-ray data. No correlation is also found between the Eddington ratio and the cut-off, suggesting that only using high statistical quality broad-band spectra is it possible to verify the theoretical predictions and study the physical characteristics of the hot corona and its geometry.
Early Life Low-Calorie Sweetener Consumption Impacts Energy Balance during Adulthood

Children frequently consume beverages sweetened with either sugars (sugar-sweetened beverages; SSB) or low-calorie sweeteners (LCS). Here we evaluated the effects of habitual early life consumption of either SSB or LCS on energy balance later during adulthood. Male and female rats were provided with chow, water, and a solution containing either SSB (sucrose), LCS [acesulfame potassium (ACE-K) or stevia], or control (no solution) during the juvenile and adolescent periods (postnatal days 26-70). SSB or LCS consumption was voluntary and restricted within federal recommended daily limits. When subsequently maintained on a cafeteria-style junk food diet (CAF; various high-fat, high-sugar foods) during adulthood, ACE-K-exposed rats demonstrated reduced caloric consumption vs. controls, which contributed to lower body weights in female but not male ACE-K rats. These discrepant intake and body weight effects in male ACE-K rats are likely based on reduced gene expression of thermogenic indicators (UCP1, BMP8B) in brown adipose tissue. Female stevia-exposed rats did not differ from controls in caloric intake or body weight, yet they consumed more SSB during adult CAF exposure. No SSB-exposed rats, neither male nor female, differed from controls in adult total caloric consumption or body weight measures. Collective results reveal that early life LCS consumption alters sugar preference, body weight, and gene expression for markers of thermogenesis during adulthood, with both sex- and sweetener-dependent effects.