Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of high‐field domain formation in tight‐binding superlattices by capacitance–voltage measurements

A method involving capacitance–voltage (C–V) measurements has been used to investigate the process of high‐field domain formation in semiconductor structures with tight‐binding GaAs/AlGaAs superlattices grown by molecular beam epitaxy. Current–voltage (I–V) measurements carried out at 77 K showed two series of current oscillations associated with the sequential tunneling process as a result of the high‐field domain formation. Accumulation of electrons in the quantum wells due to the extension of the high‐field domains through the superlattice as a result of increasing bias is detected as a sharp increase in the capacitance which clearly corresponds to a sharp reduction in the current. Preliminary results of the frequency dependence of the C–V characteristic corresponding to the sequential tunneling process as an effect of the high‐field domain formation are discussed.
Homopolymers and copolymers of Nε‐4‐phenylbenzamido‐L‐lysine and Nε‐trifluoroacetyl‐L‐lysine: Synthesis and liquid‐crystalline properties

In order to obtain comb-like poly-L-lysines bearing a biphenyl mesogen at the end of every repeating unit we have synthesised N e- 4-phenylbenzamido-L-lysine (Kφφ) and the corresponding N-carboxy anydride (Kφφ-NCA). We have homopolymerised the Kφφ-NCA and block-copolymerised it with the NCA of N e -trifluoroacetyl-L-lysine (Kt). The structural study of homopolymers pKt and pKφφ and block copolymers pKt/pKφφ was performed by infrared spectroscopy and X-ray diffraction. Two main types of structures were established: a bidimensional hexagonal one (Hα) where the polypeptide chains adopt an α-helix conformation and a lamellar smectic one (S A ) deriving from the β-pleated- sheet structure of polypeptides. Homopolymers pKt and block copolymers pKt/pKφφ rich in pKt exhibit the Hα structure, whereas the homopolymers pKφφ exhibit an S A2 structure, and block copolymers rich in pKφφ exhibit an S Ad structure. All the liquid-crystalline structures were stable until the beginning of decomposition of the polymers, Hydrolysis of the pKt block of copolymers pKt/pKφφ provided amphiphilic block copolymers pK/pKφφ without modification of the structure of the mesophases.
The pharmacology and safety profile of ferric carboxymaltose (Ferinject ® ): structure/reactivity relationships of iron preparations

 ABSTRACT Ferric carboxymaltose (FCM, marketed as Ferinject ® ) is a new intravenous iron preparation. It combines the positive characteristics of iron dextran and iron sucrose but is not associated with dextraninduced hypersensitivity reactions and can be given in much higher doses than iron sucrose or iron gluconate. The chemical characteristic of the ironcarbohydrate complex means that iron is released slowly, avoiding toxicity and oxidative stress. Up to 1000 mg iron can be given in a single administration of FCM, and this can be given by rapid infusion over 15 minutes. The reduced administration time, coupled with the fact that a test dose is not required (in contrast to iron dextran and iron sucrose) and the fact that a large amount of iron can be given in one dosage, offer convenience for patients and potential cost savings for healthcare providers.
Blocking the Spread of Misinformation in a Network under Distinct Cost Models

Given a network $N$ and a set of nodes that are the starting point for the spread of misinformation across $N$ and an integer k, in the influence blocking maximization problem the goal is to find $k$ nodes in $N$ as the starting point for a competing information (say, a correct information) across $N$ such that the reach of the misinformation is minimized. In this paper we deal with a more realistic scenario for this problem where different nodes have different costs and the counter strategy has a “budget” for picking nodes for a solution. Our experimental results show that the success of a given strategy varies substantially depending on the cost function in the model. In particular, we investigate the cost function where all nodes have cost 1 and a cost function that assigns higher costs to higher degree nodes. We show that, even though strategies that perform well in these two diverse cases are very different from each other, both correlate well with simple (but different) strategies: greedily choose high degree nodes and choose nodes uniformly at random.
MANAGEMENT STRATEGIES OF CORAL REEFS FISHERIES IN BANGGAI LAUT ARCHIPELAGO, CENTRAL SULAWESI, INDONESIA

Banggai Laut archipelagoes hold a huge potential of coral reefs fisheries, yet sustainable fisheries management is not applied optimally prompted by several characteristics. Here we present the coral reefs and fisheries characteristics, diversity of fish and also the key aspect of reefs fisheries which are coral reefs. We also address the main issues and try to reformulate management strategies based on multidimensional perspectives. The study was conducted on several islands including Labobo Island, Bokan Islands, Banggai Island and Bangkurung Island of Banggai Laut archipelagoes. It was shown that not-environmentally friendly fishing methods are indeed affecting the coral reefs fisheries indicated by the coral reefs condition, fishing gear, trend production, and also the fish diversity and percent coverage of coral reefs. The overexploitation of coral reefs fisheries has directly pressured the conditions of the coral reefs and biotas. Strategic issues and general problems in actualizing the sustainable fisheries management in Banggai Laut including 1) fisheries management; 2) law enforcement, and 3) fisheries business actors. Hence, it is essential to reformulate management strategies based on ecological, social, institutional, and economic perspectives.
TPINN: An improved architecture for distributed physics informed neural networks

Signiﬁcant progress has been made to obtain approximate solutions to PDEs using neural networks as a basis. One of these approaches (and the most popular and well-developed one) is the Physics Informed Neural Network (PINN). PINN has proved to provide promising results in various forward and inverse problems with great accuracy. However, PINN cannot be employed in its native form for solving problems where the PDE changes its form or when there is a discontinuity in the parameters of PDE across different sub-domains. Using separate PINNs for each sub-domain and connecting the corresponding solutions by interface conditions is a possible solution for this. However, this approach demands a high computational burden and memory usage. Here, we present a new method, Transfer Physics Informed Neural Network (TPINN), where one or more layer of PINN across different non overlapping sub-domains are changed keeping the other layers same for all the sub-domains. Solutions from different sub-domains are connected via problem speciﬁc interface conditions which are incorporated in to the loss function. We demonstrate the efﬁcacy of TPINN through three heat transfer problems.
Application of Big Data Analytic in Cybersecurity

—The technological and social changes in the current information age pose new challenges for security analysts. To overcome these challenges, new strategies and security solutions are sought to improve security operations concerning the detection and analysis of threats and attacks of security. Security analysts address security challenges by analyzing large amounts of data from server logs, communication equipment, security solutions and blogs related to information security in different types of structured and unstructured formats. In this paper, we analyze the application of Big Data in order to support some security activities and conceptual models to generate knowledge that can be used for the decision making or automation of security response action.
Hypolipidemic & Antioxidant Effect of Garcinia gummi-gutta barkson Atherogenic Diet-Induced Hyperlipidemic Rats

To evaluate the Hypolipidemic& Antioxidant effect of Garcinia gummi-gutta barks on Atherogenic Diet-Induced Hyperlipidemic Rats. Bark extract was prepared using a Soxhlet apparatus. 30 Wistar albino rats of either sex was divided into 5 groups (n=6). Atherogenic diet (AD) was administered to all the rats except group 1 for 4 weeks. Following induction, Group 3 & 4 were treated with bark extracts at 100 & 200mg/kg respectively and group 5 with atorvastatin at 10mg/kg for the next 4 weeks along with AD. Weekly estimation of lipid profile was done and, in the end, all the animals were sacrificed and the liver and heart were sent for histopathological examination. MDA levels were estimated by measuring the TBARS activity. Results were expressed in the Mean ± standard error of the mean. Statistical analysis was done using one-way ANOVA followed by post hocTukey test. p<0.05 is considered statistically significant. There was a significant decrease in the mean cholesterol, triglyceride, low-density lipoproteins&very-low-density lipoproteins and an increase in High-density lipoproteins when compared to the AD group. Barks extracts at both doses (100mg/kg & 200mg/kg) showed a significant reduction in lipid profile wherein the extract at higher dose was found to be better and there was no significant difference when compared to atorvastatin. The study showed that bark extract of Garcinia gummi-guttapossess significant hypolipidemic & antioxidant activity. Further studies are warranted for its clinical use in humans.
The Interior of a Network

In his study of social network systems, Granovetter [9], made the distinction between strong and weak ties between individuals. We show that these weak ties constitute a well-defined “interior”, $\mathcal{I}$) that is part of every network, $\mathcal{N}$. From $\mathcal{I}$, which is an algebraic matroid, one can rigorously define network properties such as “similarity” or “betweenness” whose implementation is scalable.
Variation of cytochrome oxidase-I gene and venom proteins of Rhopalurus junceus populations in the Moanicum sector: Pharmacological implications

Context: In Cuba, the venom of the endemic scorpion Rhopalurus junceus has been used in traditional medicine for cancer treatment. The genetic variability in populations from distinct origin and differences in the composition of venom –a little explored topic- could influence the pharmacological effects of products used in medical practice. Aims: To characterize the populations of R. junceus from six different districts of the Moanicum sector in northeastern Cuba, taking into account the variations in the sequences of cytochrome oxidase subunit I gene (COI) and peptide masses of venom. Methods: For the genetic characterization, a 658 bp gene fragment of COI was amplified by PCR. The genetic structure of populations was analyzed hierarchically, using an analysis of molecular variance (AMOVA). In proteomic analysis, molecular masses of venom constituents were arranged in increasing order, and different components were considered when masses differed by 2 Da. Results: R. junceus species in the six districts studied in the Moanicum sector belong to different populations. The sequences of COI gene showed high levels of genetic variability, recovering 47 haplotypes (87% unique). The composition of the venom was also significantly different between the districts. The most abundant components presented masses of 3-6 kDa (K+-channel specific peptides) and 6,1-10 kDa (Na+-channel specific peptides). Conclusions: Populations of R. junceus showed high genetic differentiation and a broad intraspecific variation in the venom peptides (3-10 kDa) in six districts of Moanicum sector of Cuba. This richness in scorpion varieties with different toxins profile should be considered for pharmacological applications.
Helium atom differential cross sections for scattering from single adsorbed CO molecules on a Pt(111) surface

Differential cross sections have been measured for the scattering of helium atoms from isolated CO molecules on a Pt(111) surface. The cross sections reveal an oscillatory structure as a function of scattering angle extending to large momentum transfer on both sides of the specular peak. Using a hard hemisphere model to approximate the interaction potential, the data can be well reproduced by a hard core of radius about 2.5 A. This result is compared to the charge density profile of the adsorbed molecule and also to the gas phase interaction potential, and in both cases good agreement with the experimentally predicted classical turning points is found.
A Battery-Powered Friend: Towards Embedded Emotion Recognition (CS231N Project

In this project, we set out to build a smiling robot: an embedded, battery-powered device that ”smiles back” when a human subject in front of it smiles. We deﬁne a problem of building a neural system that would power the smiling robot. We prepare the dataset, apply augmentations, and use MTurk to label data for this project speciﬁcally . We set out to ﬁnd the neural architecture that, once deployed, consumes the least power, but achieves the desired quality deﬁned as 80% of the Area Under ROC Curve compared to state-of-the-art models. We use the number of operations during the evaluation pass as an estimate of the power consumption. The AUC performance targets are set by a CNN similar to a 6-layer AlexNet that achieves state-of-the-art performance on our dataset. Based on the validation set performance, we have found a 4-layer convolutional network that achieves the desired outcome, and theoretically runs on SparkFun Appollo 3 in 3 seconds; based on the test set evaluation, only architectures that required 6 seconds perform well. Due to time constraints, we leave the deployment of the resultant CNN onto an actual embedded device to future work. We additionally ﬁnd, by inspecting saliency maps, that our model tends to identify the smile by locating muscle contraction in the cheeks rather than by the shape of the lips.
Brick Detection for MBZIRC Competition- Acronym

The Mohamed Bin Zayed International Robotic Challenge (MBZIRC) is focused on using autonomous multi-robot systems for tasks that are motivated by real-world problems. One of the tasks was to exploit the group of unmanned aerial and ground vehicles to build a wall from bricks. Because all robots have to operate autonomously, it is crucial to have a system that can reliably detect the bricks. The set of methods that were used to detect these bricks is described in the thesis. Most of the methods are based on the lidar data measured by the ground robot. The lidar scans are processed by the split and merge algorithm to find the bricks in a very sparse pointcloud. Further, the RANSAC algorithm is applied from close range to verify the spatial distribution of different types of bricks. Moreover, the maximum likelihood estimate of the brick pile model, for the novel application of the EM algorithm, is derived. With a proper model, it is possible to poll previously saved partial measurements with different levels of confidence from a symbolic map, and obtain a position of any large spatially distributed object.
Hybrid Features for Churn Prediction in Mobile Telecom Networks with Data Constraints

In a competitive Mobile telecommunications market, the customers want competitive pricing and high quality of service. A customer won't hesitate to change their telecom service provider if he/she does not find what they are looking for. This phenomenon is called churning. The telecom service providers often find that the cost of acquiring a new customer is much more that the cost of retaining one. Hence telecom operators are focusing their marketing strategies toward targeted customer retention campaigns and this is known as Churn management. One of the primary tasks of Churn management is to build an effective churn prediction models that can predict customers who are most likely to churn. The primary idea is to create profile of a customer using various data sources including call patterns, contractual information, billing, payment, customer service calls, demographic profiles and then predict the probability that he/she will churn based on his/her features. The apparent drawback of these approaches is that they require access to numerous other sources of information apart from Call Data Records (CDRs). More importantly, these models do not take into account any social influence. In our present work, we recognize the importance of the role played by social ties understanding the causal behavior of customers, and incorporate a novel feature of the social aspects of customers' social group along with the traditional individual customer profiles with potential practical implications. We propose hybrid feature sets that are based not only on the features extracted from CDRs but also on the changes in these feature sets combined with the changes in the social group patterns that would give improved performance over existing models with similar data constraints. Despite the data constraints, we demonstrate through our experiments that our model achieves improved prediction performance using these hybrid feature sets extracted from the CDRs as well as mobile social graphs even with our data constraints.
Business Cycles and Earnings Persistence: Evidence from the ASEAN-5 Countries

A business cycle is a type of volatility found in aggregate economic activity representing the presence of macro-financial risks. In the literature, it is held that the business cycle and firm performance are unequivocally interconnected, but they do not always have shared connections. For the most part, these cycles comprise two periods: an expansionary stage and a recessionary, or contractionary, stage. In this study, we analyse the effect of business cycles on firms’ earnings persistence. In contrast to existing studies, in terms of identifying cycles (i.e. periods of expansion and contraction), our approach relies on results from the Markovswitching model, with the application of ASEAN-5 data (from Indonesia, Malaysia, Singapore, Thailand and the Philippines). The results show that in an expansionary regime, earnings persistence is higher than during a period of contraction. They also support the notion that business cycles have a significant effect on earnings persistence, as explained by the relevant theory. JEL Classification: E32, M40, M41
On the Impact of the Music Encoding for Optical Music Recognition

Optical music recognition (OMR) is the field of research that studies how to make computers read musical notation (Bainbridge and Bell 2001). Since music typesetting is a tedious and expensive process, OMR represents a key element to efficiently convert the large number of existing written musical sources into a codified format that allows for its computational process. Recent advances in Machine Learning — namely Deep Learning (DL) — which have achieved great results in similar tasks such as text recognition, allow us to be optimistic about developing more accurate OMR systems. One of the current trends in these fields is the use of holistic systems. That is, systems that face the process in a single stage (end-to-end approaches), without taking into account the constituent parts of the documents. To develop these approaches, only training pairs are needed, consisting of problem images, together with their corresponding transcript solutions (Wel and Ullrich 2017; Calvo-Zaragoza and Rizo 2018; Baró, Riba, and Fornés 2018). For design reasons, these models, based on recurrent neural networks, can only formulate the output of the system as one-dimensional sequences. This is straightforward for text, since it is mainly composed of character sequences. However, its application for musical notation is not so straightforward due to the presence of different elements sharing horizontal position. The vertical distribution of these elements disrupts the linear flow of the time line, that is not trivial to codify and can cause important difficulties in the performance of the classification systems that make use of the temporal relationships of the recognized elements. XML-based music formats, such as MEI, are focused on how the score has to be coded. This makes the code plenty of irrelevant marks for the system to generate when it is recognizing the score content (mainly which symbols and where are they in the score). For that, we have designed a language to represent an appropriate OMR output, based on serializing the music symbols found in a staff. The sequential nature of music reading must be compatible with the representation of the vertical alignments of some symbols. In addition, this representation has to be easy to generate by the system, which analyzes the input sequentially and produces a linear series of symbols.
The Economics of Data Externalities

I study a model of a firm that buys data from consumers. There are data externalities, whereby data of some consumers reveal information about others. I characterize data externalities that maximize or minimize consumer surplus and the firm’s profit. I use the results to solve an information design problem in which the firm chooses what information to collect from consumers, taking into account the impact of data externalities on the cost of buying data. The firm collects no less information than the efficient amount. In some cases, we can solve the firm’s problem using a two-step concavification method.
Overview of a route-planning tool for capacitated field processes in arable farming

This paper presents an overview of a planning tool being developed for the generation of routes in the spatio-temporal domain for vehicles participating in capacitated arable farming operations. The planner supports processes where the field is divided into a surrounding headland and an inner-field (main) region. Two scenarios for output material flow operations are currently supported: 1) one machine equipped with a container works the field; and 2) a machine with no container works the field while being serviced on-the-run by one or more capacitated service units.
Microbial-Community Fingerprints as Indicators for Buried Mineralization, Southern British Columbia

As global population grows and modernizes, demand for mineral resources is expanding (Kesler, 2007). At the same time, existing orebodies are being exhausted, while the frequency of new discoveries of exposed or partially exposed deposits diminishes. Demand for mineral resources must therefore be met through the discovery and development of buried or concealed mineral deposits. Although mineral resource extraction supported the core of the Canadian economy for more than a century—currently contributing $56 billion to Canada’s GDP and providing 19% of its goods exports (The Mining Association of Canada, 2018)—its ability to do so relies on continued discovery of mineral deposits that may be concealed by overburden. Finding these mineral deposits beneath exotic overburden consisting of glacial and preglacial sediments remains a fundamental and widespread challenge to mineral exploration in Canada (Anderson et al., 2012; Ferbey et al., 2014).