Miracle-dz/UniFAR-FTUs huggingface dataset:
How to Use In-Band Network Telemetry Wisely: Network-Wise Orchestration of Sel-INT

As a promising network monitoring technique, in-band network telemetry (INT) helps to visualize networks in a fine-grained and real-time manner. Meanwhile, to address the overheads of INT, people have proposed a few selective INT (Sel-INT) approaches that only select a portion of packets in each flow to insert INT fields and distribute different types of INT fields over the selected packets. In this paper, we study how to use Sel-INT wisely in a network such that the tradeoff between monitoring accuracy/coverage and INT overheads can be balanced well. Specifically, we try to orchestrate the Sel-INT schemes of flows in both network- and flow-levels. For the network-level optimization, we model it as an INT planning problem in which the Sel-INT schemes of flows should be determined to maximize the information gain of INT as well as minimize the bandwidth overheads of INT. We formulate an integer linear programming (ILP) model to tackle the problem, prove its $\mathcal {N} \mathcal {P}$ -hardness, and leverage Lagrangian relaxation to design a polynomial-time approximation algorithm for it. The flow-level optimization considers a dynamic network environment, and we propose to combine deep learning (DL) based traffic prediction with Sel-INT, such that the Sel-INT scheme of each individual flow can be updated timely and adaptively. We implement the proposal in a small but real network testbed and experimentally demonstrate self-adaptive orchestration of Sel-INT with it.
Challenging Encounters and Within-Physician Practice Variability

We examine how physician decisions are impacted by difficult cases—encounters with newly diagnosed cancer patients. Using detailed administrative data, we compare primary care physicians' decisions in visits that occurred before and after difficult cases and matched comparison cases by the same physicians on other dates. Immediately following a difficult case, physicians increase referrals for common tests, including diagnostic tests unrelated to cancer. The effect lasts only for about an hour and is not driven by patient selection or schedule disruption. The results highlight difficult encounters as a source of variability in physician practice.
Analyzing the effect of COVID-19 in the financial behavior of consumers and investors

COVID-19's impact on GDP and consumption has already been recognized by top economic analysts. Some experts even anticipate the globe will be hit by the worst downturn since the Great Depression. Unprecedented effects on the economies of all countries are being caused by a newly discovered coronavirus. Considering that the COVID-19 pandemic isn't your typical catastrophe, several efforts have been made to try to rein in the disease's spread. Economic instability in the country has emerged as a foreshadowing of shifting market dynamics as a consequence of the deep relationship between public health initiatives and the lockout. Retail investors' financial attitudes have a direct impact on their investment decisions. The competitiveness, expansion, and economic integration of any market is driven by consumers. However, how much of the economic instability-induced change in consumer behaviour will last is a question that has to be answered. Research has recognized and explored this link, but the measures of financial attitude and behaviour still vary greatly and are often provided in the form of questions rather than declarative statements, which is problematic. As a result, nothing is known about how retail investors react to health crises, such as the present epidemic of COVID-19.
Smoothing spline regression: scalable computation and cross validation

We consider the applicability of smoothing splines via the penalized likelihood method to large data sets. Smoothing spline methods with large data suffer from expensive computations which limit the practical use. This thesis studies fast computations of the spline estimates via lower dimensional approximations in penalized likelihood regression with responses from exponential families and censored survival data in accelerated life models by choosing a random subset of basis functions. We conduct an asymptotic analysis of convergence rates followed by an algorithmic computation and simulation studies. The Bayes model associated with the approximations is discussed to provide interval estimates. We also discuss a simple modification of Generalized Cross Validation method in Gaussian regression and Generalized Approximate Cross Validation method in non Gaussian regression for smoothing parameter selection.
What is the Best Music for Neurofeedback Training?

The nucleus accumbens (NAc) is widely known for its role in reward seeking behavior which is heavily reliant on dopamine signaling. Dopamine plays an important role in reward seeking behavior and motivation and its dysregulation is shown to cause symptoms of depression such as apathy, a lack of motivation, and anhedonia, a loss of pleasure. Studying the NAc, specifically the ventral striatum in this case, is critical in understanding the underlying mechanisms of this dysregulation. Music neurofeedback is a biofeedback technique that provides feedback on brain activity through the audio quality of the music, with lower quality sounding more muffled. This study uses this technique to train participants to increase the activity of their ventral striatum in an attempt to upregulate the activity of the reward system. This paper aims to investigate what the most effective music choices are in terms of genre, key, valence (positive or negative emotions) and energy (pertaining to levels of arousal) to maximize the improvement on neurofeedback training. These musical attributes were identified by the Spotify “Organize Your Music” categorization tool. Participants underwent six EEG music neurofeedback training sessions with individually tailored pleasurable music as the source of feedback. The participants either received real feedback in the neurofeedback group (NF) or sham feedback in the control group. The results of this study showed that neurofeedback performance was negatively correlated with valence whereby songs that led to the greatest performance, measured as increase in ventral striatum activity from baseline, were those low in valence. Participants that had the greatest improvement in their neurofeedback training selected songs that were in a minor key and belonged to the pop genre. Although this study is based on a small sample, it takes the first step towards the overarching goal of using music to manage dysregulation in the reward system.
Students' Understanding of Exponential and Logarithmic Functions.

Exponential and l ogarithmic functions are pivotal mathematical concepts that play central roles in advanced mathematics. Unfortunately, these are also concepts that give students serious difficulty. In this report, we describe a theory of how students acquire an understanding of these functions by prescribing a set of mental constructions that a student can make to develop his or her understanding of these concepts. We analyze students' understanding of these concepts within the context of our theory. Our main result is that while all of the students in our study could compute exponents in simple cases, few students could reason about the process of exponentiation. Thus, according to our theory, these students' knowledge of exponential and logarithmic functions will be limited. We conclude by describing instructional activities based on our theoretical analysis designed to foster students' understanding of these concepts.
Novel Force Estimation Method for Magnetic Lead Screw-Based RotLin Actuator

This article proposes a method to precisely estimate the load force of magnetic lead screw (MLS)-based series elastic actuators (SEAs) in real time. The elastic transmissions of MLS-based SEAs generally comprise permanent magnets (PMs). Owing to the magnetic pole distortion and the characteristic nonlinear force of PMs, conventional force estimation methods are less accurate for MLS-based actuators than for SEAs with mechanical springs. In the proposed method, to precisely estimate force of MLSs, the nonlinear force characteristic of an MLS is modeled with a sinusoidal force model, and the magnetic force hysteresis phenomenon is predicted based on Bouc–Wen-type hysteresis equations. Moreover, a method called the relative displacement normalization method is proposed to detect the magnetic pole distortion and to compensate for the related force estimation error. In addition, the proposed method is applied to a rotary-linear (RotLin) actuator, which is a novel MLS-based linear SEA. The force estimation accuracy is experimentally evaluated by comparison with existing methods. The results demonstrate that the root-mean-square error of the proposed method is less than that of the existing polynomial model by up to 81.2%. Finally, a force controller and a static force control system is designed with the proposed model to prove its feasibility.
Effects of Botanical Ingredients Addition on the Bioactive Compounds and Quality of Non-Alcoholic and Craft Beer

Special beers, known as artisanal, are progressively gaining consumer preference, opening up competition, and acquiring more space in the market. Considering that, exploration for new formulations is justified and plants represent a source of novel compounds with promising antioxidant activity for this beer segment. This paper aims to evaluate the current knowledge on the role of botanical ingredients on the final yield of bioactive compounds in special beer, and how these molecules generally affect the sensory profile. Furthermore, the estimated difficulties of implementation, taking into account the new processes and the relative cost, are discussed. The addition of plants to beer could serve the interests of both the industry and consumers, on one hand, by improving the functional properties and offering a unique flavor, and on the other hand by adding variety to the craft beer landscape. This paper provides guidance and future directions for the development of new products to boost the brewing industry. Brewing processes might affect the valuable compounds, especially the phenolic content. Consequently, future studies need to identify new methods for protecting the level of bioactive compounds in special beer and increasing the bio-accessibility, along with optimization of the sensory and technological properties.
Effective Approaches in and Beyond the MSSM : applications to Higgs Physics and Dark Matter observables

In this thesis we use the Effective Field Theory approach for supersymmetric theories, applied to two experimental domains : the search for the Higgs bosons at colliders and the Dark Matter observables. The reason for introducing an effective approach in the Higgs physics is that the simple supersymmetric extension of the Standard Model (the MSSM) is known to have a tightly constrained Higgs sector, in particular with a lightest Higgs mass difficult to raise without introducing some fine-tuning. Many specific expansions (as the NMSSM for instance) allow for a richer Higgs sector. In this case however, the effective approach is handy since it encompasses many different specific ultraviolet completions. We have added to the Kahler and superpotential of the MSSM all possible terms of dimensions 5 and 6 including only Higgs superfields. The phenomenology of the resulting Higgs sector appears to be much richer, and we analyse our model in view of the latest LHC results. On the other side, supersymmetry is known for providing a Dark Matter candidate, so we have also worked to improve the accuracy of the computation of the relic density in the MSSM. Indeed the impressive accuracy on the experimental side (around 6%) calls for a precise computation including radiative corrections. Those one-loop computations being rather long in the MSSM, thus often overlooked, we have used again the effective approach by introducing new effective vertices that aim at accounting for the one-loop correction. We show on a specific process (neutralinos annihilation to muons) the agreement between the effective approach and the full one-loop computation.
The Role of Lebanon in the COVID-19 Butterfly Effect: The B.1.398 Example

In the present study, we provide a retrospective genomic surveillance of the SARS-CoV-2 pandemic in Lebanon; we newly sequence the viral genomes of 200 nasopharyngeal samples collected between July 2020 and February 2021 from patients in different regions of Lebanon and from travelers crossing the Lebanese–Syrian border, and we also analyze the Lebanese genomic dataset available at GISAID. Our results show that SARS-CoV-2 infections in Lebanon during this period were shaped by the turnovers of four dominant SARS-CoV-2 lineages, with B.1. 398 being the first to thoroughly dominate. Lebanon acted as a dispersal center of B.1. 398 to other countries, with intercontinental transmissions being more common than within-continent. Within the country, the district of Tripoli, which was the source of 43% of the total B.1. 398 sequences in our study, was identified as being an important source of dispersal in the country. In conclusion, our findings exemplify the butterfly effect, by which a lineage that emerges in a small area can be spread around the world, and highlight the potential role of developing countries in the emergence of new variants.
Evaluation of Chemical Aging on Carousel Properties Due to the COVID-19 Anti-Pandemic Measures at Airports

The study's research question in the broader context of the fight against the pandemic is the impact of disinfectants on selected surfaces of materials used in air transport. The main scientific goal of this work was to verify the assumption that disinfectant solutions cause degradation of mechanical and physical properties, i.e., chemical aging, in samples from airport conveyors, and to determine the degree of change in the properties of the samples depending on the time spent in solution and on aging. The research methodology was developed based on the ISO 1817: 2011 standard, STN EN ISO 868, and the ISO 283 standard, and applied infrared spectroscopy, specifically the Attenuated Total Reflectance method. Research suggests that constant disinfection of baggage handling systems will require the replacement of carrying parts. Otherwise, there will be significant costly breakdowns (damage to luggage, interruption of its flow, delays/inability to check in the aircraft, and the resulting legal consequences). The longer the samples were left in the disinfectant solution, the higher and more uneven the degradation. The hardness of the samples changed after immersion but was not directly proportional to the change in tensile properties. During disinfection, chemical changes occurred, which varied in length and effect depending on the disinfection used.
The architectural impact of single photon transmission measurements on full ring 3-D positron tomography

For the full potential of high resolution (2 to 4 mm) full ring 3-D positron emission tomography (PET) to be realized, reductions in both scan time and noise for measured attenuation correction must be provided. Recent advancements in 3-D PET transmission measurements have been demonstrated using a point source and collecting single photon gamma radiation. Greater than an order of magnitude increase in counting statistics for 3-D acquisitions is possible over traditional dual photon techniques such as rotating rods. Usable single photon transmission scans as short as 2 minutes are practical. Such short scans will have reduced noise due to counting rates in excess of 2.5 M events/sec. Acquiring single photon transmission data in full ring 3-D PET requires several architectural additions to the tomograph. Additions include: 1) a "place holding" mode for the coincidence processor, 2) real-time single photon line of response rebinning electronics, 3) a liquid drive for moving a 4 mCi /sup 137/Cs point source at 1 metre/sec around the field of view, and 4) a fiber-optic detector system for tracking the point source position and speed. Initial implementation of these changes supports a first-of-its-kind 82 cm diameter, 23 cm axial length high resolution full ring 3-D PET tomograph.
Unraveling the Role of Histone Variant CENP-A and Chaperone HJURP Expression in Thymic Epithelial Neoplasms

Background: Recent advances demonstrate the role of chromatin regulators, including histone variants and histone chaperones, in cancer initiation and progression. Methods: Histone H3K4me3, histone variant centromere protein (CENP-A) and histone chaperones Holliday junction recognition protein (HJURP) as well as DAXX expression were examined immunohistochemically in 95 thymic epithelial tumor (TET) specimens. Our results were compared with the expression profile of DAXX, HJURP and CENP-A in gene expression profiling interactive analysis (GEPIA2). Results: The lymphocyte-poor B3- and C-type TETs were more frequently DAXX negative (p = 0.043). B3 and C-Type TETs showed higher cytoplasmic and nuclear CENP-A (p = 0.007 and p = 0.002) and higher cytoplasmic HJURP H-score (p < 0.001). Higher nuclear CENP-A and cytoplasmic HJURP expression was associated with advanced Masaoka–Koga stage (p = 0.048 and p < 0.001). A positive correlation between HJURP and CENP-A was also observed. The presence of cytoplasmic CENP-A expression was correlated with a favorable overall survival (p = 0.03). CENP-A overexpression in survival analysis of TCGA TETs showed similar results. H3K4me3 expression was not associated with any clinicopathological parameters. Conclusions: Our results suggest a significant interaction between CENP-A and HJURP in TETs. Moreover, we confirmed the presence of a cytoplasmic CENP-A immunolocalization, suggesting also a possible favorable prognostic value of this specific immunostaining pattern.
Discrete topology and geometry algorithms for quantitative human airway trees analysis based on computed tomography images

Computed tomography is a very useful technic which allow non-invasive diagnosis in many applications for example is used with success in industry and medicine. However, manual analysis of the interesting structures can be tedious and extremely time consuming, or even impossible due its complexity. Therefore in this thesis we study and develop discrete geometry and topology algorithms suitable for use in many practical applications, especially, in the problem of automatic quantitative analysis of the human airway trees based on computed tomography images. In the first part, we define basic notions used in discrete topology and geometry then we showed that several class of discrete methods like skeletonisation algorithms, medial axes, tunnels closing algorithms and tangent estimators, are widely used in several different practical application. The second part consist of a proposition and theory of a new methods for solving particular problems. We introduced two new medial axis filtering method. The hierarchical scale medial axis which is based on previously proposed scale axis transform, however, is free of drawbacks introduced in the previously proposed method and the discrete adaptive medial axis where the filtering parameter is dynamically adapted to the local size of the object. In this part we also introduced an efficient and parameter less new tangent estimators along three-dimensional discrete curves, called 3D maximal segment tangent direction. Finally, we showed that discrete geometry and topology algorithms can be useful in the problem of quantitative analysis of the human airway trees based on computed tomography images. According to proposed in the literature design of such system we applied discrete topology and geometry algorithms to solve particular problems at each step of the quantitative analysis process. First, we propose a robust method for segmenting airway tree from CT datasets. The method is based on the tunnel closing algorithm and is used as a tool to repair, damaged by acquisition errors, CT images. We also proposed an algorithm for creation of an artificial model of the bronchial tree and we used such model to validate algorithms presented in this work. Then, we compare the quality of different algorithms using set of experiments conducted on computer phantoms and real CT dataset. We show that recently proposed methods which works in cubical complex framework, together with methods introduced in this work can overcome problems reported in the literature and can be a good basis for the further implementation of the system for automatic quantification of bronchial tree properties
Artificial Intelligence in Healthcare Competition (Teknofest-2021): Stroke Data Set

Objective: The artificial intelligence competition in healthcare was organized for the first time at the annual aviation, space, and technology festival (TEKNOFEST), Istanbul/Türkiye, in September 2021. In this article, the data set preparation and competition processes were explained in detail; the anonymized and annotated data set is also provided via official website for further research. Materials and Methods: Data set recorded over the period covering 2019 and 2020 were centrally screened from the e-Pulse and Teleradiology System of the Republic of Türkiye, Ministry of Health using various codes and filtering criteria. The data set was anonymized. The data set was prepared, pooled, curated, and annotated by 7 radiologists. The training data set was shared with the teams via a dedicated file transfer protocol server, which could be accessed using private usernames and passwords given to the teams under a non-disclosure agreement signed by the representative of each team. Results: The competition consisted of 2 stages. In the first stage, teams were given 192 digital imaging and communications in medicine images that belong to 1 of 3 possible categories namely, hemorrhage, ischemic, or non-stroke. Teams were asked to classify each image as either stroke present or absent. In the second stage of the competition, qualifying 36 teams were given 97 digital imaging and communications in medicine images that contained hemorrhage, ischemia, or both lesions. Among the employed methods, Unet and DeepLabv3 were the most frequently observed ones. Conclusion: Artificial intelligence competitions in healthcare offer good opportunities to collect data reflecting various cases and problems. Especially, annotated data set by domain experts is more valuable.