Miracle-dz/UniFAR-FTUs huggingface dataset:
The trap in the early Universe: impact on the interplay between gravitational waves and LHC physics in the 2HDM

We analyze the thermal history of the 2HDM and determine the parameter regions featuring a first-order electroweak phase transition (FOEWPT) and also much less studied phenomena like high-temperature electroweak (EW) symmetry non-restoration and the possibility of vacuum trapping (i.e. the Universe remains trapped in an EW-symmetric vacuum throughout the cosmological evolution, despite at T = 0 the EW breaking vacuum is deeper). We show that the presence of vacuum trapping impedes a first-order EW phase transition in 2HDM parameter-space regions previously considered suitable for the realization of electroweak baryogenesis. Focusing then on the regions that do feature such a first-order transition, we show that the 2HDM parameter space that would yield a stochastic gravitational wave signal potentially detectable by the future LISA observatory is very contrived, and will be well probed by direct searches of 2HDM Higgs bosons at the HL-LHC, and (possibly) also via measurements of the self-coupling of the Higgs boson at 125 GeV. This has an important impact on the interplay between LISA and the LHC regarding the exploration of first-order phase transition scenarios in the 2HDM: the absence of new physics indications at the HL-LHC would severely limit the prospects of a detection by LISA. Finally, we demonstrate that as a consequence of the predicted enhancement of the self-coupling of the Higgs boson at 125 GeV the ILC would be able to probe the majority of the 2HDM parameter space yielding a FOEWPT through measurements of the self-coupling, with a large improvement in precision with respect to the HL-LHC.
Aspects of optimization with increasing concave stochastic order constraints

This dissertation applies convex optimization techniques to a class of stochastic optimization problems. This class of problems is distinguished by a multivariate increasing concave stochastic order constraint on a random-variable-valued mapping. The analysis is divided into four chapters to address four different aspects of optimization with increasing concave stochastic order constraints.In Chapter 3, we describe this class of stochastic optimization problems in detail. This class of problems does not satisfy the Slater condition, and we must overcome this technical difficulty to apply convex optimization. We introduce a perturbation of the original problem for this purpose and we discuss semi-infinite programming and nonlinear programming relaxations of this perturbation. It is shown that increasing concave functions act as the Lagrange multipliers of increasing concave stochastic order constraints. We conclude this chapter with a discussion about a transformation of these problems via coupling on finite probability spaces.In Chapter 4, we consider sample average approximation for increasing concave stochastic order constrained programs. We verify consistency of sample average approximation. We also study sample average approximation for the semi-infinite programming and nonlinear programming relaxations presented in Chapter 3. Again, we verify consistency of sample average approximation for these relaxations. The solution method in this chapter is based on the coupling transformation from Chapter 3. This transformation necessarily requires a large-scale implementation. We comment on aggregation, column generation, and row generation techniques as a possible large-scale approach.In Chapter 5 we introduce a robust version of the increasing concave stochastic order. Specifically, we consider model misspecification for the underlying probability distribution. Robust versions of the semi-infinite programming and nonlinear programming relaxations from Chapter 3 are also presented and analyzed. The case of polyhedral uncertainty is emphasized for finite probability spaces. There is a large-scale implementation issue here as well, and aggregation is suggested again as a possible solution approach.In Chapter 6 we consider multi-period stochastic optimization problems with increasing concave stochastic order constraints. We put a multivariate increasing concave stochastic order constraint on a vector of performance measures across all time periods. We derive optimality conditions similar to those in Chapter 3. We also show that there is a companion auxiliary control problem for this class of multi-period problems that can be solved with dynamic programming. We comment on using duality decomposition to solve instances of this problem on finite probability spaces, such as those obtained via conditional sampling.
Reducing the Complexity of the Sensor-Target Coverage Problem Through Point and Set Classification

—The problem of covering random points in a plane with sets of a given shape has several practical applications in communications and operations research. One especially promi- nent application is the coverage of randomly-located points of interest by randomly-located sensors in a wireless sensor network. In this article we consider the situation of a large area containing randomly placed points (representing points of interest), as well a number of randomly-placed disks of equal radius in the same region (representing individual sensors’ coverage areas). The problem of ﬁnding the smallest possible set of disks that cover the given points is known to be NP-complete. We show that the computational complexity may be reduced by classifying the disks into several deﬁnite classes that can be characterized as necessary, excludable, or indeterminate. The problem may then be reduced to considering only the indeterminate sets and the points that they cover. In addition, indeterminate sets and the points that they cover may be divided into disjoint “islands” that can be solved separately. Hence the actual complexity is determined by the number of points and sets in the largest island. We run a number of simulations to show how the proportion of sets and points of various types depend on two basic scale- invariant parameters related to point and set density. We show that enormous reductions in complexity can be achieved even in situations where point and set density is relatively high.
Concept Gradient: Concept-based Interpretation Without Linear Assumption

Concept-based interpretations of black-box models are often more intuitive for humans to understand. The most widely adopted approach for concept-based interpretation is Concept Activation Vector (CAV). CAV relies on learning a linear relation between some latent representation of a given model and concepts. The linear separability is usually implicitly assumed but does not hold true in general. In this work, we started from the original intent of concept-based interpretation and proposed Concept Gradient (CG), extending concept-based interpretation beyond linear concept functions. We showed that for a general (potentially non-linear) concept, we can mathematically evaluate how a small change of concept affecting the model's prediction, which leads to an extension of gradient-based interpretation to the concept space. We demonstrated empirically that CG outperforms CAV in both toy examples and real world datasets.
Predicting Flaky Tests Categories using Few-Shot Learning

Flaky tests are tests that yield different outcomes when run on the same version of a program. This non-deterministic behaviour plagues continuous integration with false signals, wasting developers' time and reducing their trust in test suites. Studies highlighted the importance of keeping tests flakiness-free. Recently, the research community has been pushing forward the detection of flaky tests by suggesting many static and dynamic approaches. While promising, those approaches mainly focus on classifying tests as flaky or not and, even when high performances are reported, it remains challenging to understand the cause of flakiness. This part is crucial for researchers and developers that aim to fix it. To help with the comprehension of a given flaky test, we propose FlakyCat, the first approach for classifying flaky tests based on their root cause category. FlakyCat relies on CodeBERT for code representation and leverages a Siamese network-based Few-Shot learning method to train a multi-class classifier with few data. We train and evaluate FlakyCat on a set of 343 flaky tests collected from open-source Java projects. Our evaluation shows that FlakyCat categorises flaky tests accurately, with a weighted F1 score of 70%. Furthermore, we investigate the performance of our approach for each category, revealing that Async waits, Unordered collections and Time-related flaky tests are accurately classified, while Concurrency-related flaky tests are more challenging to predict. Finally, to facilitate the comprehension of FlakyCat's predictions, we present a new technique for CodeBERT-based model interpretability that highlights code statements influencing the categorization.
Optical polarization from colliding stellar stream shocks in a tidal disruption event

A tidal disruption event (TDE) occurs when a supermassive black hole rips apart a passing star. Part of the stellar material falls toward the black hole, forming an accretion disk that in some cases launches a relativistic jet. We performed optical polarimetry observations of a TDE, AT 2020mot. We find a peak linear polarization degree of 25 ± 4%, consistent with highly polarized synchrotron radiation, as is typically observed from relativistic jets. However, our radio observations, taken up to 8 months after the optical peak, do not detect the corresponding radio emission expected from a relativistic jet. We suggest that the linearly polarized optical emission instead arises from shocks that occur during accretion disk formation, as the stream of stellar material collides with itself. Description Editor’s summary If a star passes too close to a supermassive black hole, it gets ripped apart by tidal forces. As the debris falls toward the black hole, it heats up and emits light, producing a transient astronomical source known as a tidal disruption event (TDE). Liodakis et al. measured the optical polarization of a TDE, finding that it varied during the event, peaking at 25 ± 4% linear polarization. By comparing this behavior with models, the authors posit that TDE optical emission occurs when the stream of debris extends around a full orbit of the black hole, generating shocks where it collides with itself. —Keith T. Smith Polarized light from a star ripped apart by a supermassive black hole reveals the cause of its optical emission.
Time-dependent boundary conditions for multiphase flow

In this thesis a set of boundary conditions for multiphase flow is suggested.Characteristic-based boundary conditions are reviewed for single-phase flow. The problem of open-boundary conditions is investigated, and to avoid drifting values, the use of control functions is proposed.The use of control functions is also verified with a new test which assesses the quality of the boundary conditions. Particularly, P- and PI-control functions are examined. PI-controllers have the ability to specify a given variable exactly at the outlet as well as at the inlet, without causing spurious reflections which are amplified.Averaged multiphase flow equations are reviewed, and a simplified model is established. This model is used for the boundary analysis and the computations. Due to the averaging procedure, signal speeds are reduced to the order of the flow speed. This leads to numerical challenges. For a horizontal channel flow, a splitting of the interface pressure model is suggested. This bypasses the numerical problems associated with separation by gravity, and a physical realistic model is used. In this case, the inviscid model is shown to possess complex eigenvalues, and still the characteristic boundary conditions give reasonable results.The governing equations are solved with a Runge-Kutta scheme for the time integration. For the spatial discretisation, a finite-volume and a finite-difference method are used. Both implementations give equivalent results. In single-phase flow, the results improve significantly when a numerical filter is applied. For two-dimensional two-phase flow, the computations are unstable without a numerical filter.
Robots as Mental Well-being Coaches: Design and Ethical Recommendations

The last decade has shown a growing interest in robots as well-being coaches. However, insightful guidelines for the design of robots as coaches to promote mental well-being have not yet been proposed. This paper details design and ethical recommendations based on a qualitative analysis drawing on a grounded theory approach, which was conducted with a three-step iterative design process which included user-centered design studies involving robotic well-being coaches, namely: (1) a user-centred design study conducted with 11 participants consisting of both prospective users who had participated in a Brief Solution-Focused Practice study with a human coach, as well as coaches of different disciplines, (2) semi-structured individual interview data gathered from 20 participants attending a Positive Psychology intervention study with the robotic well-being coach Pepper, and (3) a user-centred design study conducted with 3 participants of the Positive Psychology study as well as 2 relevant well-being coaches. After conducting a thematic analysis and a qualitative analysis, we collated the data gathered into convergent and divergent themes, and we distilled from those results a set of design guidelines and ethical considerations. Our findings can inform researchers and roboticists on the key aspects to take into account when designing robotic mental well-being coaches.
Enhancing Early Lung Cancer Detection on Chest Radiographs with AI-assistance: A Multi-Reader Study

Objectives The present study evaluated the impact of a commercially available explainable AI algorithm in augmenting the ability of clinicians to identify lung cancer on chest X-rays (CXR). Design This retrospective study evaluated the performance of 11 clinicians for detecting lung cancer from chest radiographs, with and without assistance from a commercially available AI algorithm (red dot®, Behold.ai) that predicts suspected lung cancer from CXRs. Clinician performance was evaluated against clinically confirmed diagnoses. Setting The study analysed anonymised patient data from an NHS hospital; the dataset consisted of 400 chest radiographs from adult patients (18 years and above) who had a CXR performed in 2020, with corresponding clinical text reports. Participants A panel of readers consisting of 11 clinicians (consultant radiologists, radiologist trainees and reporting radiographers) participated in this study. Main outcome measures Overall accuracy, sensitivity, specificity and precision for detecting lung cancer on CXRs by clinicians, with and without AI input. Agreement rates between clinicians and performance standard deviation were also evaluated, with and without AI input. The use of the AI algorithm by clinicians led to an improved overall performance for lung tumour detection, achieving an overall increase of 17.4% of lung cancers being identified on CXRs which would have otherwise been missed, an overall increase in detection of smaller tumours, a 24% and 13% increased detection of stage 1 and stage 2 lung cancers respectively, and standardisation of clinician performance. Conclusions This study showed great promise in the clinical utility of AI algorithms in improving early lung cancer diagnosis and promoting health equity through overall improvement in reader performances, without impacting downstream imaging resources.
An Empirical Study and Analysis of Learning Generalizable Manipulation Skill in the SAPIEN Simulator

This paper provides a brief overview of our submission to the no interaction track of SAPIEN ManiSkill Challenge 2021. Our approach follows an end-to-end pipeline which mainly consists of two steps: we first extract the point cloud features of multiple objects; then we adopt these features to predict the action score of the robot simulators through a deep and wide transformer-based network. More specially, %to give guidance for future work, to open up avenues for exploitation of learning manipulation skill, we present an empirical study that includes a bag of tricks and abortive attempts. Finally, our method achieves a promising ranking on the leaderboard. All code of our solution is available at https://github.com/liu666666/bigfish\_codes.
2BiVQA: Double Bi-LSTM-based Video Quality Assessment of UGC Videos

Recently, with the growing popularity of mobile devices as well as video sharing platforms (e.g., YouTube, Facebook, TikTok, and Twitch), User-Generated Content (UGC) videos have become increasingly common and now account for a large portion of multimedia traffic on the internet. Unlike professionally generated videos produced by filmmakers and videographers, typically, UGC videos contain multiple authentic distortions, generally introduced during capture and processing by naive users. Quality prediction of UGC videos is of paramount importance to optimize and monitor their processing in hosting platforms, such as their coding, transcoding, and streaming. However, blind quality prediction of UGC is quite challenging, because the degradations of UGC videos are unknown and very diverse, in addition to the unavailability of pristine reference. Therefore, in this article, we propose an accurate and efficient Blind Video Quality Assessment (BVQA) model for UGC videos, which we name 2BiVQA for double Bi-LSTM Video Quality Assessment. 2BiVQA metric consists of three main blocks, including a pre-trained Convolutional Neural Network to extract discriminative features from image patches, which are then fed into two Recurrent Neural Networks for spatial and temporal pooling. Specifically, we use two Bi-directional Long Short-term Memory networks, the first is used to capture short-range dependencies between image patches, while the second allows capturing long-range dependencies between frames to account for the temporal memory effect. Experimental results on recent large-scale UGC VQA datasets show that 2BiVQA achieves high performance at lower computational cost than most state-of-the-art VQA models. The source code of our 2BiVQA metric is made publicly available at https://github.com/atelili/2BiVQA.
Effervescent waves in a binary mixture with non-reciprocal couplings

Non-reciprocal interactions between scalar fields that represent the concentrations of two active species are known to break the parity and time-reversal (PT) symmetries of the equilibrium state, as manifested in the emergence of travelling waves. We explore the notion of nonlinear non-reciprocity and consider a model in which the non-reciprocal interactions can depend on the local values of the scalar fields. For generic cases where such couplings exist, we observe the emergence of spatiotemporal chaos in the steady-state. We associate this chaotic behaviour with a local restoration of PT symmetry in fluctuating spatial domains, which leads to the coexistence of oscillating densities and phase-separated droplets that are spontaneously created and annihilated. We uncover that this phenomenon, which we denote as effervescence, can exist as a dynamical steady-state in large parts of the parameter space in two different incarnations, as characterized by the presence or absence of an accompanying travelling wave.
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning (ML) and Deep Learning (DL) has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most ML-based techniques and DL-based techniques are deployed in the “black-box” manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users’ confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security.
Inverse Propensity Score based offline estimator for deterministic ranking lists using position bias

The mission of Just Eat Takeaway is to empower users’ every food moment. A big part of fulfilling that mission is ensuring that we show the right restaurants to the right users. To do this, we design large-scale machine learning recommendation systems that can learn which types of users tend to enjoy which types of restaurants. A crucial part of this process is being able to compare the quality of different recommendation systems, in order to decide which is most effective. The gold standard approach to this evaluation problem is A/B testing allow the different systems to recommend items to randomly selected groups of users, and compare their relative performance. However, A/B testing is both slow, taking time to reach sufficient statistical power, and expensive [5, 1] if we serve poor recommendations to a subset of users. Therefore, evaluating recommender systems in offline fashion without running A/B tests is of significant importance. These offline approaches avoid serving experimental recommenders to users, and instead evaluates them using old online data, where users were served recommendations from a different system. Metrics such as Root Mean Squared Error [2] Mean Average Precision, Normalized Discounted Cumulative Gain, Mean Reciprocal Rank and Hit Rate have been used repeatedly for offline evaluation. All of these evaluation approaches are prone to selection, presentation and position biases [11, 6] and therefore the results from offline evaluation
The c-equivalence principle and its implications for physics

The c-equivalence principle, commonly accepted as true by most physicists, is the unstated assumption that 1 / √ (cid:15) o µ o equals the kinematic speed of light. Should someone prove the principle false, it would render the composition of two Lorentz transformations meaningless. It would also invalidate the second hypothesis of the Special Theory of Relativity in its strong form. This paper examine some other consequences for physics, should this principle be proven false and outline some experiments to determine light speed, which could falsify the principle and provide evidence for the ether.