Miracle-dz/UniFAR-FTUs huggingface dataset:
Delayed Gradient Averaging: Tolerate the Communication Latency in Federated Learning

Federated Learning is an emerging direction in distributed machine learning that enables jointly training a model without sharing the data. Since the data is distributed across many edge devices through wireless / long-distance connections, federated learning suffers from inevitable high communication latency. However, the latency issues are undermined in the current literature [15] and existing approaches such as FedAvg [27] become less efﬁcient when the latency increases. To overcome the problem, we propose D elayed G radient A veraging (DGA), which delays the averaging step to improve efﬁciency and allows local computation in parallel to communication. We theoretically prove that DGA attains a similar convergence rate as FedAvg, and empirically show that our algorithm can tolerate high network latency without compromising accuracy. Speciﬁcally, we benchmark the training speed on various vision (CIFAR, ImageNet) and language tasks (Shakespeare), with both IID and non-IID partitions, and show DGA can bring 2.55 × to 4.07 × speedup. Moreover, we built a 16-node Raspberry Pi cluster and show that DGA can consistently speed up real-world federated learning applications.
NOTCH1 in Cutaneous Squamous Cell Carcinoma Arising in Immunosuppressed Patients: A Systematic Review and Quantitative Analysis

Immunosuppression is a strong risk factor for cutaneous squamous cell carcinoma (cSCC). Immunosuppression is also associated with unique mutagenic stressors that likely contribute to cSCC pathogenesis. However, it is unknown whether these stressors contribute to a distinct mutation profile that may drive disease progression. This review was conducted to assess the mutational landscape of cSCC arising in immunosuppressed hosts. Specifically, we sought to determine gene mutation frequencies in immunosuppressed cSCC. An electronic search was performed in PubMed, Embase, Scopus, and Cochrane databases. Studies performing DNA sequencing or genotyping of cSCC were identified. Studies were excluded if the immune status of each tumor was not available. Eighteen studies met inclusion criteria. Due to study heterogeneity a meta-analysis was unable to be performed. However, statistical analysis was performed on the most frequently reported genes. NOTCH1 was the most frequently mutated gene in immunosuppressed cSCC, and was significantly higher than immunocompetent cSCC after multiple comparison adjustment (77.7 versus 58.1%, OR 2.50, 95% CI 1.40-4.46, p=0.002). No other statistically significant differences were observed. Our results suggest that NOTCH1 mutations are more common in cSCC arising in immunosuppressed hosts. Several prior observations reviewed here further support a role for NOTCH1 in immunosuppressed cSCC, however larger studies are needed to confirm our findings. J Clin Investigat Dermatol November 2021 Volume 9, Issue 2 © All rights are reserved by Miller AD, et al. Avens Publishing Group Inviting Innovations Citation: Miller AD, Chow ML, Brian Jiang SI. NOTCH1 in Cutaneous Squamous Cell Carcinoma Arising in Immunosuppressed Patients: A Systematic Review and Quantitative Analysis. J Clin Investigat Dermatol. 2021;9(2): 5 J Clin Investigat Dermatol 9(2): 5 (2021) Page 02 ISSN: 2373-1044 abstract, 712 studies were excluded due to ineligibility or duplication. The remaining 64 studies were reviewed in full text, and 18 studies were ultimately included in our analysis [4,17-33]. 712 studies were excluded due to ineligibility or duplication. The remaining 64 studies were reviewed in full text, and 18 studies were ultimately included in our analysis [4,17-33].
No-regret Online Learning over Riemannian Manifolds

We consider online optimization over Riemannian manifolds, where a learner attempts to minimize a sequence of time-varying loss functions deﬁned on Riemannian manifolds. Though many Euclidean online convex optimization algorithms have been proven useful in a wide range of areas, less attention has been paid to their Riemannian counterparts. In this paper, we study Riemannian online gradient descent (R-OGD) on Hadamard manifolds for both geodesically convex and strongly geodesically convex loss functions, and Riemannian bandit algorithm (R-BAN) on Hadamard homogeneous manifolds for geodesically convex functions. We establish upper bounds on the regrets of the problem with respect to time horizon, manifold curvature, and manifold dimension. We also ﬁnd a universal lower bound for the achievable regret by constructing an online convex optimization problem on Hadamard manifolds. All the obtained regret bounds match the corresponding results are provided in Euclidean spaces. Finally, some numerical experiments validate our theoretical results.
IDENTIFICATION AND CLASSIFICATION OF RISKS IN A NEW MODELLING PROCESS FOR BUILD - OPERATE - TRANSFER PROJECTS

The Private – Finance –Initiative (PFI) procurement methods provide a framework for countries that lack the adequate own funds to develop major infrastructure projects. The Build-Operate-Transfer (BOT) projects represent a variation of these procurement methods. Success of these projects depends on the project agreement and, especially, the risk management during the whole life cycle. This paper contributes to the risk management of a successful BOT project by introducing a comprehensive new risk inventory and classification scheme of BOT risks. The inventory comprises 86 risks, which are classified based on two criteria, i.e. their nature and the source of their origin. Each one of these criteria comprises several categories, which are explicitly presented and discussed. This classification enables the assignment of risks to the project’s life cycle phases, where they may occur, and the mapping of their interrelations; both are the next steps of the development process for a BOT fuzzy-based risk assessment model, which is also presented and discussed in brief. The overall approach facilitates risk assessment and understanding in BOT projects and provides a clear risk framework, which is comprehensible by risk analysts as well as responsive to their demands.
Board Attributes and Financial Fraud Likelihood in Nigeria

The study examines the effect of board attributes on corporate fraud likelihood of quoted manufacturing firms in Nigeria. The scope of the study covers board attributes such as Board size, board independence and board diversity and the beneish M-score was used as the measure of fraud likelihood. This study utilized a longitudinal research design. The sample covered 15 manufacturing companies in the Nigerian Stock Exchange (NSE) as at 2017. Secondary data was utilized for this study and the data was extracted from the annual reports of corporate organizations for the period 2012-2017 financial years. The binary regression was employed as the method of data analysis in the study. The findings reveal that the odd ratio of BDIND is 0.66031 which implies that changes in managerial ownership will reduce the odds of corporate fraud. The odd ratios for BDSIZE and BDDIV though slightly higher than 1 are nevertheless not significant at 5%. The study concludes that there is the need for boards to be more effective in their monitoring roles so as to reduce the occurrence of fraud. Addition, the independence of the boards should be maintained.
Protein Controlled Release Based On Copolymerization Of HEMA-Cs and HEMA-PVA

This work describes the synthesis of new biodegradable hydrogels based on 2-hydroxyethyl methacrylate, by free radical polymerization in the presence of benzoyl peroxide as initiator and ethylene glygol di methacrylate as across linking agent. And protein was loaded in pol-ymeric matrix during polymerization. The concentration of protein release was measured by UV-Vis spectrophotometer and release. FTIR was performed to find out the total conversion of Chitosan and Poly Vinyl Alcohol into biodegradable hydrogels.
'It's Problematic but I'm not Concerned': University Perspectives on Account Sharing

Account sharing is a common, if officially unsanctioned, practice among workgroups, but so far understudied in higher education. We interview 23 workgroup members about their account sharing practices at a U.S. university. Our study is the first to explicitly compare IT and non-IT observations of account sharing as a "normal and easy" workgroup practice, as well as to compare student practices with those of full-time employees. We contrast our results with those in prior works and offer recommendations for security design and for IT messaging. Our findings that account sharing is perceived as low risk by our participants and that security is seen as secondary to other priorities offer insights into the gap between technical affordances and social needs in an academic workplace such as this.
NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs

Recent years have witnessed a surge of approaches to use neural networks to help tackle combinatorial optimization problems, including graph optimization problems. However, theoretical understanding of such approaches remains limited. In this paper, we consider the geometric setting, where graphs are induced by points in a ﬁxed dimensional Euclidean space. It turns out that several graph optimization problems can be approximated (in a bicriteria manner) by an algorithm that runs in time linear in graph size n via a framework that we call the Baker-paradigm. A key advantage of the Baker-paradigm is that it decomposes the input problem into (at most linear number of) small sub-problems of bounded sizes (independent of the size of the input). For the family of such bounded-size sub-problems, we can now design neural networks with universal approximation guarantees to solve them. This leads to a mixed algorithmic-ML framework, which we call NN-Baker that has the capacity to approximately solve a family of graph optimization problems (e.g, maximum independent set and minimum vertex cover) in time linear in the input graph size. We instantiate our NN-Baker by a CNN version and GNN version, and demonstrate the effectiveness and efﬁciency of our approach via a range of experiments.
Espadas-Aldana, Gabriela and Guaygua‐Amaguaña, Priscila and Vialle, Claire and Belaud, Jean-Pierre and Evon, Philippe and Sablayrolles, Caroline Life Cycle Assessment of Olive Pomace as a Reinforcement in Polypropylene and Polyethylene Biocomposite Materials: A New Perspective for the Valorization

The main environmental impact of olive oil production is the disposal of residues such as pomace and water vegetation. During the olive oil extraction process, the olive stone is milled and discharged within the olive pomace. However, olive stone flour can be valorized as filler for poly‐ meric composites. A life cycle assessment of the olive pomace valorization was carried out by fo‐ cusing on the manufacturing process of a biocomposite made of two different thermoplastic matri‐ ces, i.e., polyethylene and polypropylene. The functional unit is the production of 1 m2 of a lath made of an olive pomace‐based biocomposite. The analysis was carried out with the SimaPro PhD 9.1.1. 1 software, and the database used for the modeling was Ecoinvent 3.6. The obtained results reveal that the hotspot of the whole process is the twin‐screw compounding of the olive stone frac‐ tion, with the polymeric matrix and coupling agent, and that human health is the most affected damage category. It represents 89% for both scenarios studied: olive stone fraction/polypropylene (OSF/PP) and olive stone fraction/polyethylene (OSF/PE). Further research directions include the use of biosourced polymer matrices, which could reduce the impact of olive pomace‐based compo‐
Performance Analysis of MIMO-OFDM for 4G Wireless Systems under Rayleigh Fading Channel

MIMO-OFDM technology is a combination of multiple-input multiple-output (MIMO) wireless technology with orthogonal frequency division multiplexing (OFDM) that has been recognized as one of the most promising techniques to support high data rate and high performance in different channel conditions. Again, Alamouti’s space time block coding scheme for MIMO system has drawn much attention in 4G wireless technologies just because of its decoding simplicity. This paper presents the performance evaluation of Alamouti’s space-time block coded (ASTBC) MIMO-OFDM systems covering channel model, channel capacity, coding scheme and diversity gain. The mathematical model of capacity is derived for deterministic, random and correlated Rayleigh fading channels. The channel capacity per unit bandwidth is evaluated as a function of SNR. It is observed that the channel capacity increases with the number of antenna added to the system due to the more diversity gain of Alamouti’s code. Finally we investigate the correlated MIMO fading channel with different correlation matrix. At the higher SNR value, independent and identically distributed (i.i.d) channel capacity outperforms the correlated channel capacity. But At very low SNR value correlated channel capacity outperforms the i.i. d channel capacity.
Scaling byzantine replication to wide-area networks

As network environments become increasingly hostile, even well secured distributed systems are likely to suffer at least one compromised subcomponent. During the past several years, Byzantine fault-tolerant replication has emerged as a promising technique for constructing intrusion-tolerant systems that function correctly even when an attacker controls part of the system. Prior to our work, Byzantine replication systems were based on flat (nonhierarchical) protocols and offered high performance only when deployed on local-area networks. This dissertation presents the first two hierarchical Byzantine fault-tolerant replication architectures that scale to systems that span multiple wide-area sites. Our first architecture, Steward, confines the effects of a malicious replica to its local site, reduces message complexity of wide-area communication, and allows read-only queries to be performed locally within a site for the price of additional commodity hardware. Our second architecture improves upon Steward by providing customizability of the fault tolerance approach used within each site and on the wide area and by including new optimizations. Prototype implementations are evaluated in several network topologies and compared with the previous state of the art. The experimental results show an order of magnitude improvement over flat Byzantine replication protocols in typical wide-area deployments.
Enabling Ultra Large-Scale Radio Identification Systems

Radio Frequency IDentification (RFID) is growing prominence as an automated identification technology able to turn everyday objects into an ad-hoc network of mobile nodes; which can track, trigger events and perform actions. Energy scavenging and backscattering techniques are the foundation of low-cost identification solutions for RFIDs. The performance of these two techniques, being wireless, significantly depends on the underlying communication architecture and affect the overall operation of RFID systems. Current RFID systems are based on a centralized master-slave architecture hindering the overall performance, scalability and usability. Several proposals have aimed at improving performance at the physical, medium access, and application layers. Although such proposals achieve significant performance gains in terms of reading range and reading rates, they require significant changes in both software and hardware architectures while bounded by inherited performance bottlenecks, i.e., master-slave architecture. Performance constraints need to be addressed in order to further facilitate RFID adoption; especially for ultra large scale applications such as Internet of Things. A natural approach is re-thinking the distributed communication architecture of RFID systems; wherein control and data tasks are decoupled from a central authority and dispersed amongst spatially distributed low-power wireless devices. The distributed architecture, by adjusting the tag's reflectivity coefficient creates micro interrogation zones which are interrogated in parallel. We investigate this promising direction in order to significantly increase the reading rates and reading range of RFID tags, and also to enhance overall system scalability. We address the problems of energy-efficient tag singulations, optimal power control schemes and load aware reader placement algorithms for RFID systems. We modify the conventional set cover approximation algorithm to determine the minimal number of RFID readers with minimal overlapping and balanced number of tags amongst them. We show, via extensive simulation analysis, that our approach has the potential to increase the performance of RFID technology and hence, to enable RFID systems for ultra large scale applications.
Effective quality-controlled SPIHT-based ECG coding strategy under noise environments

An effective quality-controlled set partitioning in hierarchical trees (SPIHT)-based ECG coding strategy under noise environments is proposed using a wavelet-energy based weighted percentage root mean square difference (WEWPRD s ) criterion. This criterion is subjectively meaningful and leads to a better measure of rate-distortion (R-D) performance. Experiments on several noisy records from the MIT-BIH arrhythmia (mita) database show that the proposed strategy outperforms PRD and wavelet-based weighted PRD ws (WWPRD s ) measurement-criteria-based strategies.
Evaluation of some Phytochemical Contents in Spondias monbin (Hog plum) Leaves

Phytochemical analysis of Spondias mombin (hog plum) leaf extract was studied. This study identified and analyzed the concentration of various phytochemicals present in metabolites in Spondias mombin (hog plum) leaf extract using methods as described. The result revealed that Spondias mombin (Hog plum) leaf extract contains alkaloid (29.66±5.446mg/g), flavonoid(76.21±8.73mg/g), Glycoside (28.3 l±5.32mg/g),Steroid (86.89±9.32mg/l),Tanin (0.885±0.940mg/g), Terepenoid (0.914±0.956mg/g), Saponin (0.742±0.861mg/g). From the findings, Spondias mombin leaf extract contains these seven secondary metabolites with steroid at highest concentration and Saponin at the lowest concentration. The leaves were . air dried for 2 weeks and grounded in other to expose the enzymes and to reduce its surface area and was kept air tight prior to phytochemical composition of spondias mombin leaf extract and have established the plant extract as a potential source of highly medicinal and nutritious feed stuff, folk medicine and phytomedicine as a result of its aromatic asterigent and refrigerant nature. The tree exudes gum used as a glue root and bark decoctions are. used as purgative and remedies for diarrhea, dysentery and hemorrhoids iohile leaves extract have been reported to have anxiolytic, antipsychotic hypoglycemic. With increasing consumption of Spondias mombin fruit, considerable attention is required for analysis of its nutrients and natural compounds. Although considerable amount of literature exist on the importance and usefulness of the different parts of the plants. There is little information about aqueous extract of leaves. This study therefore seeks to investigate its possible phytochemical contents of aqueous leaf so as to unravel new information on the leaf.
FISH CONSUMPTION AND EFFECTS ON CARDIO VASCULAR DISEASES IN SRI LANKA

Increasing fish consumption is recommended for intake of omega-3(n=3) fatty acids and to confer benefits for the risk reduction of cardiovascular diseases (CVD). In Sri Lanka coronary heart disease (CHD) is a main cause of morbidity and mortality and reported as first rank of disease among all other diseases (11.69%). Sri Lankan studies suggest concentration of risk factors in urban areas and higher socioeconomic classes with an increasing prevalence among younger people. A diet rich in carbohydrates and saturated fats (coconut is the major supplier of fat energy) but low in protein contribute to the worsening burden of CVD in Sri Lankan community. The fishes have varying amount of Poly unsaturated fatty acids and the consumption of fish varies among people. Herein, the relationship between fish intake and CVD risk reduction as well as the other socio demographics and food habits are described. The multiple factors influencing the consumption pattern with the effects of processing and cooking methods of fish and effects of omega-3 fatty acids are also described. The purpose of this review provides the information on consumption of fish, including current dietary recommendations and intakes which positively affect the health and to meet fish intake and CVD risk reduction Sri Lanka.
Increased risk of fungal infection detection in women using using menstrual cups vs. tampons

Menstrual cups are gaining in popularity worldwide as a more environmentally sustainable and affordable alternative to disposable personal hygiene products. However, there is currently a lack of scientific literature regarding their safety and potential effects on women's health. We analysed biological, demographic, and behavioural data in a cohort of young adult women using either tampons (N = 81) or menstrual cups (N = 22). We identify an increased risk of being diagnosed with fungal infection for women using menstrual cups over tampons. We did not detect significant differences between groups in terms of vaginal microbiota composition or local cytokines expression profile. However we found that depending on the type of menstrual product they use more (cups or tampons), women fall into two different clusters in a factor analysis of mixed data, which potentially reflects differences in their local vaginal environments. These results underline the urgent need for in-depth studies to better understand the potential associations between menstrual product products and women's health.
A Generalized Formulation of Demand Response under Market Environments

Abstract This paper presents a generalized formulation of Demand Response (DR) under deregulated electricity markets. The problem is scheduling and controls the consumption of electrical loads according to the market price to minimize the energy cost over a day. Taking into account the modeling of customers’ comfort (i.e., preference), the formulation can be applied to various types of loads including what was traditionally classified as critical loads (e.g., air conditioning, lights). The proposed DR scheme is based on Dynamic Programming (DP) framework and solved by DP backward algorithm in which the stochastic optimization is used to treat the uncertainty, if any occurred in the problem. The proposed formulation is examined with the DR problem of different loads, including Heat Ventilation and Air Conditioning (HVAC), Electric Vehicles (EVs) and a newly DR on the water supply systems of commercial buildings. The result of simulation shows significant saving can be achieved in comparison with their traditional (On/Off) scheme.