Miracle-dz/UniFAR-FTUs huggingface dataset:
Routing Policies & Strategies in Delay Tolerant Network

Delay Tolerant Networks (DTNs) represent a class of wireless networks that experience frequent and long lasting partitions due to sparse distribution of nodes in the topology. A traditional network assumes the definite existence of a contemporaneous end-to-end path between a source and destination. But this assumption is unrealistic in many real life applications. Therefore, DTN comes into existence to deal with the networks that assumes partitions among the network. Numerous studies have tackled the challenging problem of routing in DTNs. Routing proposals include stochastic approaches such as random, spray-and-wait and epidemic routing, or deterministic approach such as history-based, model-based, coding based and variations of these approaches. The number of routing schemes in the literature is increasing rapidly without a clear mapping of which is more suitable for any of the vast array of potential DTN application. This document surveys the main routing schemes in the DTN literature. It provides a detailed insight to the DTN approach and describes in some depth the policies and strategies proposed to manage buffers and queues in DTN nodes. Then, the predominant DTN real-life applications are presented and discussed. The aim of this document is to create a classification basis for the most prominent DTN applications and to map major DTN routing schemes to these categories according to some influential network attributes and characteristics that are related particularly to the type of
Ensemble Networks for Better Facial Recognition of Bearded Faces

Face recognition systems such as FaceNet [9] perform poorly when certain facial features are obscured [10] . We propose an ensemble network architecture which combines FaceNet with a specialized secondary network for face recognition in the presence of the facial obscurity, as well as a dispatcher network to decide which face recognition model to use. We apply this architecture to bearded faces, and demonstrate superior performance over standalone face recognition systems. Our architecture extends to an arbitrary number of facial obscurities, indicating a potential for signiﬁcant improvement to face recognition systems in general.
Ozone Amplifies Water Loss from Mature Trees in the Short Term But Decreases It in the Long Term

We measured whole-tree transpiration of mature Fagus sylvatica and Picea abies trees exposed to ambient and twice-ambient O3 regimes (1xO3 and 2xO3 free-air fumigation). After eight years, mean daily total transpiration did not vary with the O3 regime over the 31 days of our study, even though individual daily values increased with increasing daily O3 peaks in both species. Although the environmental parameters were similar at 1xO3 and 2xO3, the main factors affecting daily transpiration were vapour pressure deficit in 2xO3 spruce and O3 peaks in beech. For a mechanistic explanation, we measured O3-induced sluggish stomatal responses to variable light (sunflecks) by means of leaf-level gas exchange measurements only in the species where O3 was a significant factor for transpiration, i.e., beech. Stomata were always slower in closing than in opening. The 2xO3 stomata were slower in opening and mostly in closing than 1xO3 stomata, so that O3 uptake and water loss were amplified before a steady state was reached. Such delay in the stomatal reaction suggests caution when assessing stomatal conductance under O3 pollution, because recording gas exchange at the time photosynthesis reached an equilibrium resulted in a significant overestimation of stomatal conductance when stomata were closing (ab. 90% at 1xO3 and 250% at 2xO3). Sun and shade leaves showed similar sluggish responses, thus suggesting that sluggishness may occur within the entire crown. The fact that total transpiration was similar at 1xO3 and 2xO3, however, suggests that the higher water loss due to stomatal sluggishness was offset by lower steady-state stomatal conductance at 2xO3. In conclusion, O3 exposure amplified short-term water loss from mature beech trees by slowing stomatal dynamics, while decreased long-term water loss because of lower steady-state stomatal conductance. Over the short term of this experiment, the two responses offset each other and no effect on total transpiration was observed.
WSN simulators evaluation: an approach focusing on energy awareness

The large number of Wireless Sensor Networks (WSN) simulators available nowadays, differ in their design, goals, and characteristics. Users who have to decide which simulator is the most appropriate for their particular requirements, are today lost, faced with a panoply of disparate and diverse simulators. Hence, it is obvious the need for establishing guidelines that support users in the tasks of selecting a simulator to suit their preferences and needs. In previous works, we proposed a generic and novel approach to evaluate networks simulators, considering a methodological process and a set of qualitative and quantitative criteria. In particularly, for WSN simulators, the criteria include relevant aspects for this kind of networks, such as energy consumption modelling and scalability capacity. The aims of this work are: (i) describe deeply the criteria related to WSN aspects; (ii) extend and update the state of the art of WSN simulators elaborated in our previous works to identify the most used and cited in scientific articles; and (iii) demonstrate the suitability of our novel methodological approach by evaluating and comparing the three most cited simulators, specially in terms of energy modelling and scalability capacities. Results show that our proposed approach provides researchers with an evaluation tool that can be used to describe and compare WSN simulators in order to select the most appropriate one for a given scenario
ShipNet: A Fishing Vessel Dataset Challenges and Opportunities

Opportunities in capturing global ship movements in real time through the use of satellites, alongside publicly available images of ships, open up the possibility of creating new data sets with precise labels giving information about the type of gear that the ship uses while fishing. We detail the process of creating a data set that maps publically sourced images to precise labels about that ship, pretraining on existing data sets with ResNet convolutional architecture, training with these models on our data set, and understanding network performance through unsupervised learning techniques. Lastly, we propose future directions for research with this data set.
Warsaw School of Economics

This paper uses a restricted factor model to estimate the HICP index excluding relative prices changes. The index thus obtained, hereinafter referred to as pure inflation, demonstrates stronger relationship to the central bank instrument (short-term interest rate) than the HICP index and selected measures of core inflation. Pure inflation has also a forecasting performance for future HICP comparable or better than that of competing models. The estimated variable indicates a much weaker role of changes in relative prices in the recent period of rising inflation (2006-2008) than during previous inflation increases (1999-2000 and 2004-05). This may show that inflation was mainly driven by demand pressures in the years 2006-2008.
D-FAP: Dual-Factor Authentication Protocol for Mobile Cloud Connected Devices

Emerging Mobile Cloud Computing (MCC) technologies offer a new world of promise by leveraging the quality of mobile services. With MCC, resource-constrained mobile devices could capitalize on the computation/storage resources of cloud servers via communication networks. While MCC adoption is growing significantly, several challenges need to be addressed to make MCC-based solutions scale and meet the ever-growing demand for more resource intensive applications. Security is a critical problem hindering the adoption of MCC. One of the most important aspects of MCC security is to establish authenticated communication sessions between mobile devices and cloud servers. The huge amount of data stored on mobile devices poses information security risks and privacy concerns for individuals, enterprises, and governments. The ability to establish authenticated communication sessions between mobile devices and cloud servers can resolve many security concerns. Limited computing and energy resources on mobile devices makes authentication and encryption a challenging task. In this paper, an overview of MCC authentication protocols is presented. Then, a Dual-Factor Authentication Protocol for MCC devices (D-FAP) is proposed. D-FAP aims at increasing authentication security by using multi-factors while offloading computation to the cloud to reduce battery consumption. The security of the protocol is formally verified and informal analysis is performed for various attacks. The results prove that the D-FAP is successful in mitigating various outsider and insider attacks.
Machine Learning Techniques to Search for 2 νββ decay of 136 Xe to the excited state of 136 Ba in EXO-200 Category : Physical Sciences

Measuring the two-neutrino double-beta decay of 136Xe to the first 0+ excited state, denoted as 01 , of the daughter nucleus 136Ba is exciting because it is expected to introduce constraints to the Nuclear Matrix Elements (NMEs) related to double-beta decays. Moreover, shared uncertainties in the NMEs for the excited state and ground-state decays can be canceled by taking the ratio between their NMEs. Also, having a better understanding of these NMEs would allow us to determine the effective Majorana neutrino mass more precisely from measurements of neutrinoless double-beta decay half-lives [1]. Finally, this search would allow us to test exotic theories of alternate doublebeta decay mechanisms. Therefore, in this paper, we train a boosted decision tree (BDT), a multilayer perceptron (MLP), and a long-short term memory (LSTM) network on Monte Carlo simulations of the Enriched Xenon Observatory (EXO-200) experiment to increase the sensitivity of this search and set a lower limit on the half-life of this decay. We use specific features, described in Sections 4 and 5, of the events recorded in the detector as inputs to the aforementioned models with a binary output of whether the event was a signal or background, where signal is the excited state decay and background is all the other decays. We compare the performances of these models using the area under the specificity versus sensitivity curve and find that the LSTM model performs the best.
Communication Model of Home Area Network Devices

Smart Grid solutions, combinations of power and communication networks, are getting popular nowadays. In this paper, we focus on the Home Area Network (HAN), the part of the Smart Grid. We show a basic idea of HAN Simulation Framework and simulation process. The purpose of this framework is to prepare the environment for fast and simple models of Home Coordinator and Smart Appliances implementation.The basic functions of framework are described. Moreover, the communication protocol is specified. This protocol, based on EN 50523 specification, should be fast and simple way to transfer necessary information between devices inside HAN. When developed, the framework will serve to implement and test of HAN scheduling algorithms. Key-Words: Home Area Network, Home Coordinator, Communication protocol, Simulation, Simulation Framework.
A Comparative Analysis of Medicinal Plant belongs to Euphorbiaceae Family in Village Ardawata (Chirawa Tehsil) of Jhunjhunu District (Rajasthan)

A botanical survey was extensively carried out to get information on the uses of medicinal plants by local community who survive in village Ardawata of Chirawa tehsil (Jhunjhunu). A sum of 8 medicinal plants from Euphorbiaceae family was reported as being used to cure human being and other animal’s ailments. The result of the survey in Ardawata village is show that various diseases are treated by herbal remedies. Leaf, stem, bark, root, flower even whole plant also used to make remedy. Remedies which made from these 8 medicinal plant are cure various disease such as Asthma, Malaria, Fever, Ulcer, Dropsy, Tumors, Skin Allergy, UrineRelated Problems, Genital Problems, Blood Pressure, Tuberculosis, Diabetes, Lymphoma, Wound, Diarrhea, Rheumatism, Kidney Stones, Constipations, Scabies, Leprosy, Joint Pain, Acidity, Inflammations, Toothache, Cough, Eye Flue, Viral, venereal infection etc. KEY WORDSMedicinal Plant, Ardawata Village, Chirawa Tehsil, Local Community, Jhunjhunu, Diseases, Remedies.
Effects of Clinical Data in Skin Cancer Classification

Skin cancer is the most common cancer in the United States. It is estimated that one out of five Americans will develop skin cancer by age 70. Unlike other types of cancer, skin cancer can be visually detected easily by dermatologists and it is highly curable if detected early. Published deep learning models [1, 2] for skin cancer classification demonstrates a viable diagnostic tool by skin images alone. Inspired by this publication, our project will build and analyze a baseline SVM classifier and a modified convolutional neural network (CNN) based on Google Inception V3 [3] to diagnose skin images as benign or malignant.
Machine Learning Algorithms for Sourcing and Evaluating VC and PE investment deals

The hedge fund community is well known for using quantitative models. However, in private investment markets, many decisions are being made on a gut feel. Traditional finance calls for simple ratio analysis and cash flow valuation based on financial metrics. Current VC and PE investors network to identify potential deals. With limited operating history and tight budgets, a whole lot of features could be incorporated in the analysis to pick those deals that will unlock value for investors. Using Pitchbook data (courtesy of Professor of Finance Ilya A. Strebulaev at Stanford Graduate School Business), we applied machine learning techniques to predict the probability of a startup’s success. Given the nature of the problem at hand and the structure of the dataset, we focused on binary classification algorithms including logistic regression, regularized logistic with L1 and L2 penalty, Support Vector Machine, Random Forest and Neural Network. Our research will enable VC and PE investors to proactively know which companies to talk to, help in sourcing deals and due diligence activities that form the core of the growth investment process. Moreover, it will also serve as a self-assessment tool for start-up founders so that they can make sound decisions during incubation.
Policy Gradient in Linear Quadratic Dynamic Games Has No Convergence Guarantees

We show by counterexample that policy-gradient algorithms have no guarantees of even local convergence to Nash equilibria in continuous action and state space multi-agent settings. To do so, we analyze gradient-play inN–player general-sum linear quadratic (LQ) games. In such games, the state and action spaces are continuous and a global Nash equilibrium can be found by solving coupled Ricatti equations. Further, gradient-play in LQ games is equivalent to multi-agent policygradient. We first prove that despite the non-convexity of the players’ objectives, the only critical points of the gradient dynamics in these games are global Nash equilibria. We then give sufficient conditions under which policy-gradient will avoid the Nash equilibria, and generate a large number of general-sum LQ games with Nash equilibria that satisfy these conditions. The existence of such games indicates that one of the most popular approaches to solving reinforcement learning problems in the classic reinforcement learning setting has no guarantee of convergence in multi-agent settings. Moreover, the ease with which we can generate these counterexamples suggests that such situations are in fact quite common.
Semantic Similarity Search

At Gridspace (the company at which Josh Hedtke works), there are hundreds of billions of trascribed conversation segments holding relevant, searchable information related to customers’ quality assurance and analytics goals. Given a query sentence or small paragraph q, semantic search seeks to find the top n most semantically similar sentences/paragraphs, some of which may have no or few tokens in common with q. To accomplish this, we generated BERT (Bidirectional Encoder Representations from Transformers) [2] embeddings ∈ R to represent sentences, much like the word2vec embeddings model [10]. However, BERT takes into account both left and right context of every word in the sentence to generate every word’s embedding representation. For example, in the phrases ”in the jail cell” and ”mitochondria in the cell,” the word ”cell” would have very different BERT embedding representations in each of the sentences.
Material Synthesis Techniques from a Data-driven Perspective

Rendering realistic 3D scenes requires rich, highquality materials datasets, which are lacking. We propose to leverage recent progress in computer vision and generative models to generate materials (color maps and normal maps) at large scale from a data-driven perspective. Our pipeline consists of generative models that synthesize texture color maps and a prediction model that maps color maps to normal maps. We are able to generate large, seamless color and normal maps that look realistic,diverse, and can be directly rendered into 3D scenes.
Therapeutic Option of Adding Bevacizumab to Paclitaxel after Treatment Failure for Patients with Metastatic Breast Cancer: Case Reports and Literature Review

Introduction: Adding bevacizumab can change an impaired blood supply after resistance to weekly paclitaxel for metastatic breast cancer (MBC). We experienced effective tumor management by adding bevacizumab to paclitaxel refractory disease. Case 1: A 70-year-old female had multiple lymph-node metastases. After seven months of paclitaxel, she received bevacizumab additional to paclitaxel due to disease progression. Tumor markers were gradually reduced over five months. Case 2: A 50-year-old female developed lymph-node metastases and multiple bone metastases were also revealed later. After 16 months of paclitaxel, tumor markers were gradually reduced over the next 12 months by also using bevacizumab. Case 3: A 45-year-old woman had received neoadjuvant chemotherapy using anthracyclines and weekly paclitaxel. During post mastectomy radiation therapy, left chest wall recurrence, lung metastases, and liver metastases were found. Although the interval between paclitaxel monotherapy and paclitaxel plus bevacizumab was only three months, tumor markers were reduced over four months. Case 4: A 42-year-old woman had been referred due to left inflammatory breast cancer; she had been using anthracyclines and weekly paclitaxel. Left chest wall recurrence and lymph node metastases were found after neoadjuvant chemotherapy. Although the interval between paclitaxel monotherapy and paclitaxel plus bevacizumab was only five months, tumor markers were reduced over the next five months. Discussion: It was difficult to draw any conclusions regarding the efficacy of adding bevacizumab from this retrospective case series. However, the strategy of additional bevacizumab should be considered and may change the clinical practice for patients with MBC.
Decision Making in Limb Salvage: A Challenge for the Orthoplastic Team

Optimal decision making in reconstruction of soft tissues after massive orthopedic trauma is mission-critical to a favorable outcome. In the broadest definition this reconstruction does not just mean a large soft-tissue placed on the bony injury—but rather that the injured patient regains as full a functional recovery, is not narcotic or antibiotic dependent (as many patients with recurring osteomyelitis, plate infection are) and has as little of a disfigurement of the donor and recipient sites as possible. Many argue this is a tall order—and perhaps so. But given where we are in 2012, with advances in all trauma-related fields including imaging, injury scoring, orthopedic reconstruction, it is not too much to expect that plastic and reconstructive methods too run with the winners. An optimal result depends heavily on the following key reconstructive elements described below. (1) timing of reconstruction, (2) choice of soft tissue, (3) anticipated functional outcomes, (4) composite orthoplastic team. Critics may argue that this approach is oversimplified. But on further and more thorough examination it becomes amply clear that these 4 factors play a sentinel role: Here, we describe these aspects in detail.
An Improved Coarse-to-Fine Method for Solving Generation Tasks

The coarse-to-fine (coarse2fine) methods have recently been widely used in the generation tasks. The methods first generate a rough sketch in the coarse stage and then use the sketch to get the final result in the fine stage. However, they usually lack the correction ability when getting a wrong sketch. To solve this problem, in this paper, we propose an improved coarse2fine model with a control mechanism, with which our method can control the influence of the sketch on the final results in the fine stage. Even if the sketch is wrong, our model still has the opportunity to get a correct result. We have experimented our model on the tasks of semantic parsing and math word problem solving. The results have shown the effectiveness of our proposed model.
Vivianite formation in ferruginous sediments from Lake Towuti, Indonesia

Abstract. Ferruginous lacustrine systems, such as Lake Towuti, Indonesia, are characterized by a specific type of phosphorus cycling in which hydrous ferric iron (oxyhydr)oxides trap and precipitate phosphorus to the sediment, which reduces its bioavailability in the water column and thereby restricts primary production. The oceans were also ferruginous during the Archean, thus understanding the dynamics of phosphorus in modern-day ferruginous analogues may shed light on the marine biogeochemical cycling that dominated much of Earth's history. Here we report the presence of large crystals (>5 mm) and nodules (>5 cm) of vivianite – a ferrous iron phosphate – in sediment cores from Lake Towuti and address the processes of vivianite formation, phosphorus retention by iron and the related mineral transformations during early diagenesis in ferruginous sediments. Core scan imaging, together with analyses of bulk sediment and pore water geochemistry, document a 30 m long interval consisting of sideritic and non-sideritic clayey beds and diatomaceous oozes containing vivianites. High-resolution imaging of vivianite revealed continuous growth of crystals from tabular to rosette habits that eventually form large (up to 7 cm) vivianite nodules in the sediment. Mineral inclusions like millerite and siderite reflect diagenetic mineral formation antecedent to the one of vivianite that is related to microbial reduction of iron and sulfate. Together with the pore water profiles, these data suggest that the precipitation of millerite, siderite and vivianite in soft ferruginous sediments stems from the progressive consumption of dissolved terminal electron acceptors and the typical evolution of pore water geochemistry during diagenesis. Based on solute concentrations and modeled mineral saturation indices, we inferred vivianite formation to initiate around 20 m depth in the sediment. Negative δ56Fe values of vivianite indicated incorporation of kinetically fractionated light Fe2+ into the crystals, likely derived from active reduction and dissolution of ferric oxides and transient ferrous phases during early diagenesis. The size and growth history of the nodules indicate that, after formation, continued growth of vivianite crystals constitutes a sink for P during burial, resulting in long-term P sequestration in ferruginous sediment.