Miracle-dz/UniFAR-FTUs huggingface dataset:
Neighbor Selection Strategies in the Wild for CDN/V2V WebRTC Live Streaming: Can we learn what a good neighbor is?

A hybrid CDN/Viewer-to-Viewer (V2V) architecture is an attractive solution for HTTP (HLS) and MPEG-DASH-based live streaming providers. It combines a traditional CDN with a V2V overlay for exchanging video fragments, reducing the cost of the CDN while maintaining the quality of experience. This work explores machine learning models to address the key challenge of neighbor selection. Our goal is to predict the connection quality between two arbitrary viewers using features such as locality, access providers, operating systems, past CDN, and V2V throughput. The proposed solutions are validated using an A/B testing approach on our production system, demonstrating a significant improvement in key system metrics compared to the traditional locality-based methods. We observe 17% higher V2V throughput, 26% lower delay, 37% fewer lost chunks, 39% fewer re-buffering, and 20% fewer quality switches.
Long-Term Noninvasive Ventilation in Chronic Obstructive Pulmonary Disease: Association between Clinical Phenotypes and Survival

Background: Long-term noninvasive ventilation (LTNIV) is widely used in patients with chronic hypercapnic respiratory failure (CHRF) related to COPD. Prognosis of these patients is however poor and heterogenous. Research Question: In COPD patients under LTNIV for CHRF, is it possible to identify specific phenotypes which are predictive of probability of pursuing NIV and survival? Study Design and Methods: A latent class analysis was performed in a COPD population under LTNIV included in a comprehensive database of patients in the Geneva Lake area, to determine clinically relevant phenotypes. The observation period of this subgroup of COPD was extended to allow assessment of survival and/or pursuit of NIV for at least 2 years after inclusion. A logistic regression was conducted to generate an equation accurately attributing an individual patient to a defined phenotype. The identified phenotypes were compared on a series of relevant variables, as well as for probability of pursuing NIV or survival. A competitive risk analysis allowed to distinguish death from other causes of cessation of NIV. Results: Two phenotypes were identified: a “respiratory COPD” profile with very severe airway obstruction, a low or normal body mass index, and a low prevalence of comorbidities and a “systemic COPD” profile of obese COPDs with moderate airway obstruction and a high rate of cardiovascular and metabolic comorbidities. The logistic regression correctly classified 95.7% of patients studied. Probability of pursuing NIV and survival were significantly related to these phenotypes, with a poorer prognosis for “respiratory COPD. ” Probability of death 5 years after implementing NIV was 22.3% (95% CI: 15.4–32.2) for “systemic COPD” versus 47.2% (37.4–59.6) for “respiratory COPD” (p = 0.001). Conclusion: The two distinct phenotypes of COPD under LTNIV for CHRF identified appear to be strongly related to prognosis and require further validation in other cohort studies.
Cryogeomorphic Characterization of Shadowed Regions in the Artemis Exploration Zone

The Artemis program will send crew to explore the south polar region of the Moon, preceded by and integrated with robotic missions. One of the main scientific goals of future exploration is the characterization of polar volatiles, which are concentrated in and near regions of permanent shadow. The meter‐scale cryogeomorphology of shadowed regions remains unknown, posing a potential risk to missions that plan to traverse or land in them. Here, we deploy a physics‐based, deep learning‐driven post‐processing tool to produce high‐signal and high‐resolution Lunar Reconnaissance Orbiter Narrow Angle Camera images of 44 shadowed regions larger than ∼40 m across in the Artemis exploration zone around potential landing sites 001 and 004. We use these images to map previously unknown, shadowed meter‐scale (cryo)geomorphic features, assign relative shadowed region ages, and recommend promising sites for future exploration. We freely release our data and a detailed catalog of all shadowed regions studied.
Packet-level and IEEE 802.11 MAC frame-level analysis for IoT device identification

: In cyberspace, a large number of Internet of Things (IoT) devices from different manufacturers with heterogeneous functionalities are connected together. It is challenging to identify all these devices in an IoT ecosystem. The situation becomes even more complicated when the devices come from the same manufacturer and of similar types due to their analogous network communication behaviour. In this paper, a device fingerprinting (DFP) approach based on a set of combined features from packet-level and frame-level has been proposed. A large number of features has been studied, and consequently, a suitable subset of features has been selected according to gain-ratio and device-specific features for DFP. Furthermore, experiments with different types of IoT devices in a laboratory environment to collect network traﬀic traces have been conducted and used to evaluate the performance of the proposed approach using the J48 (C4.5) algorithm. It has been shown that the proposed model is able to identify individual device types with 99.0% precision and 98.9% recall with the approach capable of classifying IoT devices coming from the same manufacturer and of similar types, with higher accuracy. These results are significant as it can be used as a security reinforcement tool towards increasing the security and resilience of IoT networks.
Underwater Detection of Small-Volume Weak Target Echo in Harbor Scene Under Multisource Interference

Owing to the strong reverberation and various obstacle echoes interference in harbor scenes, the active detection performance of tracking methods for weak targets will be seriously reduced. Moreover, the conventional dereverberation and tracking methods generally cannot effectively separate the weak target under the overlapping echoes of multiple scattering sources. Focusing on solving the above problems, the correlation residual cumulative clustering (CRCC) algorithm is proposed to extract scattering features of weak target motion. There are two innovations in this letter: First, the complex cepstrum filter is improved to suppress strong reverberation. Second, the correlation residual accumulation of adjacent frames is extracted to detect the reduction matrix containing the target trajectory. Finally, the fuzzy C-means (FCM) objective function is optimized via spatial–temporal constraint, thus the weak target can be effectively separated from the overlapping giant interference. The experimental results indicate the strong antijamming, low detection loss, and short-term accumulation of our proposed model, which are remarkably superior to the traditional posterior probability models.
Sustainable Portfolio Optimization Model Using PROMETHEE Ranking: A Case Study of Palm Oil Buyer Companies

Sustainability is one of the main concerns of decision makers, factories, and retailers. This importance increases when the organization needs to define, implement, and manage a sustainable portfolio to succeed in today’s environment of change and uncertainty. Therefore, this study examines the portfolio of palm producers from 2010 to 2020. The proposed optimization model is based on the classic mean-variance approach and companies ranked using the PROMETHEE method. Our findings indicate that the effects of this asset allocation change in favour of high-score sustainability investments significantly influence risk-adjusted returns of portfolios. According to the finding, sustainable portfolios perform better than conventional ones. The findings could be useful to private and institutional investors and fund managers in constructing and managing their portfolios. The efficient frontier of the best 50% portfolio shows a higher risk for a higher return. This could explain that a higher sustainability score in different criteria leads to more optimal portfolios in terms of risk and return. Intended contributions include considering sustainability, minimizing risk, and portfolio management simultaneously.
Ocular Inflammation Induced by Immune Checkpoint Inhibitors

Ocular immunotherapy-related adverse events (IRAEs), although rare, can be sight-threatening. Our objective was to analyze ocular IRAEs diagnosed in France from the marketing of immune checkpoint inhibitors (ICPIs) until June 2021 and to review the literature. We collected the cases of 28 patients (36 ocular IRAEs), occurring after an average of 17 weeks (±19). Forty-six percent of patients were treated for metastatic melanoma. Anti-PD1 agents were responsible for 57% of the IRAEs. Anterior uveitis was the most common (44%), followed by panuveitis (28%). Of 25 uveitis cases, 80% were bilateral and 60% were granulomatous. We found one case with complete Vogt-Koyanagi–Harada syndrome and one case of birdshot retinochoroidopathy. The other IRAEs were eight ocular surface disorders, one optic neuropathy, and one inflammatory orbitopathy. Seventy percent of the IRAEs were grade 3 according to the common terminology of AEs. ICPIs were discontinued in 60% of patients and 50% received local corticosteroids alone. The literature review included 230 uveitis cases, of which 7% were granulomatous. The distributions of ICPIs, cancer, and type of uveitis were similar to our cohort. Ocular IRAEs appeared to be easily controlled by local or systemic corticosteroids and did not require routine discontinuation of ICPIs. Further work is still warranted to define the optimal management of ocular IRAEs.
A general heuristic for two-dimensional nesting problems with limited-size containers

Cutting raw-material into smaller parts is a fundamental phase of many production processes. These operations originate raw-material waste that can be minimised. These problems have a strong economic and ecological impact and their proper solving is essential to many sectors of the economy, such as the textile, footwear, automotive and shipbuilding industries, to mention only a few. Two-dimensional (2D) nesting problems, in particular, deal with the cutting of irregularly shaped pieces from a set of larger containers, so that either the waste is minimised or the value of the pieces actually cut from the containers is maximised. Despite the real-world practical relevance of these problems, very few approaches have been proposed capable of dealing with concrete characteristics that arise in practice. In this paper, we propose a new general heuristic (H4NP) for all 2D nesting problems with limited-size containers: the Placement problem, the Knapsack problem, the Cutting Stock problem, and the Bin Packing problem. Extensive computational experiments were run on a total of 1100 instances. H4NP obtained equal or better solutions for 73% of the instances for which there were previous results against which to compare, and new benchmarks are proposed.
Structured light field three-dimensional measurement based on equivalent camera array model for highly reflective surfaces

Abstract. Light-field cameras are gaining attention for their unique light gathering and post-capture processing capabilities. In our previous work, we combined light-field imaging with structured light technology to realize three-dimensional (3D) reconstruction for highly reflective surface. However, due to the lack of effective calibration of the light-field camera and reconstruction systems, it cannot meet the requirements of precise 3D measurement. A universal sub-aperture image extraction algorithm for the light-field camera with hexagonal microlens is presented to accurately extract the multidirectional images. Then, we explore the spatial relationship in equivalent camera array (ECA) of focused light-field camera according to the perspective variation of sub-aperture images. After that, an accurate 3D measurement system based on ECA model is proposed to achieve 3D measurement for highly reflective surface. To test the system’s practical performance, a precision analysis is conducted for a standard gauge block with ground truth. By comparing the reconstruction result of traditional method and ours, we demonstrated the validity of the proposed method to perform 3D measurement for highly reflective surface.
Research on textile defects detection based on improved generative adversarial network

Fabric defects seriously affect the textile industry in China. Given that traditional manual detection methods have low efficiency and poor accuracy, using automatic textile defect detection methods is urgently needed. A fabric defect detection method based on an improved generative adversarial network is thus developed to address the shortage of fabric defect samples. This method learns to reconstruct the fabric image in an unsupervised manner and locates the defect areas based on the differences between the original image and the reconstruction. Afterward, the defect-related features are extracted from these areas to further recognize specific fabric defects. The central loss constraint is introduced to improve the recognition performance of this method, and lightweight processing is applied to guarantee its real-time operation in embedded systems. The application of this method is then evaluated on the publicly available Tianchi dataset. Both quantitative and qualitative results show that the proposed method can accurately detect fabric defects.
Can metabolic traits explain animal community assembly and functioning?

All animals on Earth compete for free energy, which is acquired, assimilated, and ultimately allocated to growth and reproduction. Competition is strongest within communities of sympatric, ecologically similar animals of roughly equal size (i.e. horizontal communities), which are often the focus of traditional community ecology. The replacement of taxonomic identities with functional traits has improved our ability to decipher the ecological dynamics that govern the assembly and functioning of animal communities. Yet, the use of low‐resolution and taxonomically idiosyncratic traits in animals may have hampered progress to date. An animal's metabolic rate (MR) determines the costs of basic organismal processes and activities, thus linking major aspects of the multifaceted constructs of ecological niches (where, when, and how energy is obtained) and ecological fitness (how much energy is accumulated and passed on to future generations). We review evidence from organismal physiology to large‐scale analyses across the tree of life to propose that MR gives rise to a group of meaningful functional traits – resting metabolic rate (RMR), maximum metabolic rate (MMR), and aerobic scope (AS) – that may permit an improved quantification of the energetic basis of species coexistence and, ultimately, the assembly and functioning of animal communities. Specifically, metabolic traits integrate across a variety of typical trait proxies for energy acquisition and allocation in animals (e.g. body size, diet, mobility, life history, habitat use), to yield a smaller suite of continuous quantities that: (1) can be precisely measured for individuals in a standardized fashion; and (2) apply to all animals regardless of their body plan, habitat, or taxonomic affiliation. While integrating metabolic traits into animal community ecology is neither a panacea to disentangling the nuanced effects of biological differences on animal community structure and functioning, nor without challenges, a small number of studies across different taxa suggest that MR may serve as a useful proxy for the energetic basis of competition in animals. Thus, the application of MR traits for animal communities can lead to a more general understanding of community assembly and functioning, enhance our ability to trace eco‐evolutionary dynamics from genotypes to phenotypes (and vice versa), and help predict the responses of animal communities to environmental change. While trait‐based ecology has improved our knowledge of animal communities to date, a more explicit energetic lens via the integration of metabolic traits may further strengthen the existing framework.
Plasmonic Effect of Ag/Au Composite Structures on the Material Transition

Noble metal nanostructures can produce the surface plasmon resonance under appropriate photoexcitation, which can be used to promote or facilitate chemical reactions, as well as photocatalytic materials, due to their strong plasmon resonance in the visible light region. In the current work, Ag/Au nanoislands (NIs) and Ag NIs/Au film composite systems were designed, and their thermocatalysis performance was investigated using luminescence of Eu3+ as a probe. Compared with Ag NIs, the catalytic efficiency and stability of surface plasmons of Ag/Au NIs and Ag NIs/Au film composite systems were greatly improved. It was found that the metal NIs can also generate strong localized heat at low temperature environment, enabling the transition of NaYF4:Eu3+ to Y2O3: Eu3+, and anti-oxidation was realized by depositing gold on the surface of silver, resulting in the relative stability of the constructed complex.
Virus Texture Classification of TEM Images Using Fusion of Chebyshev Moments and Resnet50 Features

: Classification of viruses is an essential step before suitable treatment of a disease. In this work, a novel approach for the classification of virus texture from TEM images is proposed. Chebyshev moments are used to classify virus textures with the Pre-trained convolution neural networks. Resnet50 is a pre-trained deep learning model used to classify images into object categories. Chebyshev moments and the pruned features extracted from the last global pooling layer of Resnet50 are fused to improve the classification accuracy. The fused feature vector along with corresponding labels is used to train a multiclass Error Correcting Output Code (ECOC) classifier to give the output. The proposed approach is tested on a standard benchmark virus texture dataset. Peak, mean, and median classification accuracies are calculated and compared with the state of the art approaches. In addition, various other classification metrics i.e., sensitivity, specificity, Mathews Correlation Coefficient, and kappa are given to justify the validity of the proposed method. 80% of the image dataset is chosen for training and the remaining 20% for testing. A peak classification accuracy of 90.33%, mean accuracy of 86.99% and median accuracy of 86.66% is achieved. Superiority of the proposed method is justified with simulations.
Non-Chiral S-Matrix of N=4 Super Yang-Mills

We discuss the construction of non-chiral S-matrix of four-dimensional N=4 super Yang-Mills using a non-chiral superspace. This construction utilizes the non-chiral representation of dual superconformal symmetry, which is the natural representation from the point of view of the six-dimensional parent theory. The superspace in discussion is projective superspace constructed by Hatsuda and Siegel, and is based on a half coset U(2,2|4)/U(1,1|2)^2_+. We obtain the non-chiral representation of the five-point and general n-point MHV and anti-MHV amplitude. The non-chiral formulation can be straightforwardly lifted to six dimensions, which is equivalent to massive amplitudes in four dimensions.
Deconstructing corporate activism: a consumer approach

Defending social and political positions other than those that a company's clients might support has always been an avoidable risk. However, this practice, called ‘corporate activism,’ has gradually been integrated into the strategies of organizations. The object of this work is thus to understand the antecedents of corporate activism from the consumer's point of view. To understand this, we carry out structural equation modeling (SEM) based on a sample of 1,521 consumers. The results demonstrate that: (i) institutional credibility, corporate credibility, and authenticity act as antecedents of corporate activism; (ii) corporate credibility has a positive influence on corporate activism, while institutional credibility has a negative impact. These findings represent an interesting and novel contribution that helps to understand how these types of high-risk strategies should be adopted. The application of these results could enable companies to determine the conditions that favor a positive evaluation of corporative activism by consumers and avoid the use of such strategies in less favorable situations.
Modeling and Control of Priority Queueing in Software Defined Networks via Machine Learning

Software Defined Networking (SDN) is a new architectural paradigm that enables programmable control of a network to make it more flexible and easier to manage. SDN architectures decouple control and forwarding functionalities, and enable switches and routers to be remotely configurable/programmable in run-time by a controller. Modeling and optimization of such modern heterogeneous network infrastructures are key factors to achieve better performance, e.g. in terms of traffic flow improvement while reducing bandwidth allocation. Identifying an accurate model of a network device in SDNs (e.g., a switch or a router) is crucial in order to apply advanced techniques such as Model Predictive Control (MPC). However, such a problem is very challenging due to non-linearities and unavailability of internal variables measurements in real devices. To this aim, a promising direction is given by an appropriate integration of System Identification and Machine Learning techniques to obtain predictive models using historical data collected from the network thanks to the SDN paradigm. In this paper we apply a novel data-driven methodology to learn accurate models of the dynamical input-output behavior of a network’s switch device by appropriately combining AutoRegressive eXogenous (ARX) model identification with Regression Trees (RTs) and Random Forests (RFs). The advantage of such model is that it can be directly used to apply MPC (which just requires Quadratic Programming to be solved) to optimally control the queues’ bandwidth of the switch ports within the SDN paradigm. We validate our approach on an experimental emulation setup using the Mininet network emulator environment and a real dataset obtained from measurements of an Italian Internet Service Provider (Sonicatel S.r.l.). To this aim, we first develop a model of a real network switch, then implement MPC using the RYU controller, and finally demonstrate the benefits of the proposed dynamic queueing control methodology in terms of packet losses reduction and bandwidth saving, i.e. in terms of improvement of the Quality of Service.