Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of operational value-at-risk in the presence of minimum collection threshold: An empirical study

The recently finalized Basel II Capital Accord requires banks to adopt a procedure to estimate the operational risk capital charge. Under the Advanced Measurement Approaches, that are currently mandated for all large internationally active US banks, require the use of historic operational loss data. Operational loss databases are typically subject to a minimum recording threshold of roughly $10,000. We demonstrate that ignoring such thresholds leads to biases in corresponding parameter estimates when the threshold is ignored. Using publicly available operational loss data, we analyze the effects of model misspecification on resulting expected loss, Value-at-Risk, and Conditional Value-at-Risk figures and show that underestimation of the regulatory capital is a consequence of such model error. The choice of an adequate loss distribution is conducted via in-sample goodness-of-fit procedures and backtesting, using both classical and robust methodologies.
Behavioural Red Flags of Fraud— A Qualitative Assessment

Fraud literature suggests that the presence or absence of fraudulent intentions can be assessed by a close scrutiny of human behaviour. This can help identify prospective fraud perpetrators. Given this consideration, the present study qualitatively explores the observations and views of people who have personally investigated or closely observed a fraud/fraudster. Twenty-six interviews help condense a checklist of behavioural red flags of fraud. The themes of strong ambition, social aloofness, extended working hours, dissatisfaction with current job, justifying unethical behaviour, personal problems and living standard disproportionate to current means are central to the articulations of the experiences of the respondents. Findings of the study have been integrated with the theory of the fraud triangle. This research displays the complementarity between a primary data based ex-post analysis of the behaviour of fraud perpetrators and existing literature on the subject. The results are likely to help improve professional standards related to frauds, both from an organizational and regulatory perspective.
Evolution of Species Assemblages: Effects of Energetic Constraints and Species Dynamics on the Diversification of the North American Avifauna

Characteristics of the terrestrial avifauna of North America can be viewed as adaptations by a taxonomically, geographically, and ecologically defined assemblage of many species to the constraints imposed by its own biology and by the environment. We have identified distinctive patterns in the variation among species in population density, body size, area of geographical range, and trophic status. The patterns observed in bivariate plots of log-transformed variables can be characterized provisionally in terms of polygons that enclose combinations of the variables exhibited by species. The sides of these polygons may be either abrupt or indistinct. We suggest that sharp, clear-cut boundaries separating combinations of characteristics that species possess from those combinations that are not observed in any species are the result of absolute constraints. As a trivial example, the maximum size of the geographical range is determined by the size of the continent. A more interesting example of an apparently absolute constraint is an energetic trade-off between maximum population density and body size. Boundaries separating combinations of characteristics that species possess from those not possessed can also be diffuse and relatively poorly defined. We suggest that such boundaries result from the probabilistic processes of origination and extinction, such that the number of species declines gradually across the boundary. An example is the increase in minimum area of geographical range with increasing body size, which is hypothesized to reflect the probability of extinction. We summarize our hypotheses to account for the observed patterns in a model for the adaptive evolution of the North American terrestrial avifauna. With appropriate modifications, a similar model could be developed for any biota. Our analyses provide neontological evidence for the kinds of patterns observed in the fossil record and used by paleontologists to argue that a process analogous to natural selection at the level of individuals within populations operates at the level of species within biotas. It is useful to view certain attributes of contemporary species as the result of some combination of absolute constraints and the dynamics of speciation, colonization, and extinction processes.
Project Plan : Deep Learning on Mobile Devices

In recent years, we have witnessed the great success of General Purpose Graphics Processor Unit (GPGPU) in massively computing task. This achievement encourages processor manufactures to improve general computing capability of Desktop GPUs. Nowadays, programmable GPUs are also available on mobile devices, such as smartphones, autopilot cars, and IoT devices, which lead to significant performance boost and substantial energy reduction for massively mobile computation tasks [1].
A Bootstrapping Approach to classification of Deep web Query Interfaces

Classification of Deep web sources is a very important process for the data extraction process while accessing the deep web content since it deals with domain specific data only. The existing methods cannot effectively classify these web databases. Hence, to solve this problem, we propose a new framework that uses the bootstrapping approach for automatic and accurate classification of the query interfaces of web databases. Our system was able to classify the deep web sources with a better average precision when compared to the existing systems. Also, the classified results used for surfacing the deep web content gave good results equivalent to the results obtained when manually classified query interfaces were used.
Dopamine D1 Activation Potentiates Striatal NMDA Receptors by Tyrosine Phosphorylation-Dependent Subunit Trafficking

Interactions between dopaminergic and glutamatergic afferents in the striatum are essential for motor learning and the regulation of movement. An important mechanism for these interactions is the ability of dopamine, through D1 receptors, to potentiate NMDA glutamate receptor function. Here we show that, in striatal neurons, D1 receptor activation leads to rapid trafficking of NMDA receptor subunits, with increased NR1 and NR2B subunits in dendrites, enhanced coclustering of these subunits with the postsynaptic density scaffolding molecule postsynaptic density-95, and increased surface expression. The dopamine D1 receptor-mediated NMDA receptor trafficking is blocked by an inhibitor of tyrosine kinases. Blockers of tyrosine phosphatases also induce NMDA subunit trafficking, but this effect is nonselective and alters both NR2A- and NR2B-containing receptors. Furthermore, tyrosine phosphatase inhibition leads to the clustering of tyrosine-phosphorylated NR2B subunit along dendritic shafts. Our findings reveal that D1 receptor activation can potentiate striatal NMDA subunit function by directly promoting the surface insertion of the receptor complexes. This effect is regulated by the reciprocal actions of protein tyrosine phosphatases and tyrosine kinases. Modification of these pathways may be a useful therapeutic target for Parkinson’s disease and other basal ganglia disorders in which abnormal function of striatal NMDA receptors contributes to the symptoms of the diseases.
On the Invariance of Dictionary Learning and Sparse Representation to Projecting Data to a Discriminative Space

In this paper, it is proved that dictionary learning and sparse representation is invariant to a linear transformation. It subsumes the special case of transforming/projecting the data into a discriminative space. This is important because recently, supervised dictionary learning algorithms have been proposed, which suggest to include the category information into the learning of dictionary to improve its discriminative power. Among them, there are some approaches that propose to learn the dictionary in a discriminative projected space. To this end, two approaches have been proposed: first, assigning the discriminative basis as the dictionary and second, perform dictionary learning in the projected space. Based on the invariance of dictionary learning to any transformation in general, and to a discriminative space in particular, we advocate the first approach.
Mobile Anchor-based Localization over Android

While localization is essential to many applications, there are quantities of wireless nodes whose accurate locations remain unknown or hard to tell. As a remedy, this paper is leveraging a GPS-capable Android handset that features portability and computing capability to collect data and display localization results thereof. Our objective is to locate the unknown nodes in consideration of radio-signal irregularity. To this end, we devise a hybrid means to incorporate received signal strengths used in range-based schemes with the notion of estimative regions adopted in range-free methods to refine the area of interest for localization. Our scheme operates without involved computations such as to accommodate many usable reference coordinates. Our treatment mitigates the errors of estimation resulting from different radio signal propagation scenarios, by taking an additional parameter to narrow region size for ameliorating localization accuracy. Experiments are conducted to demonstrate our performance results overlying Google Maps on the handset and through quantitative comparisons with trilateration under different settings. Performance results show the effectiveness of our development.
A Two-Dimensional Polyrotaxane with Large Cavities and Channels: A Novel Approach to Metal-Organic Open-Frameworks by Using Supramolecular Building Blocks.

A seven-membered molecular necklace composed of six copper ions and six pseudorotaxane units behaves as a secondary building block in the formation of a two-dimensional polyrotaxane network with large voids. This novel metal-organic framework allows size-selective anion exchange as well as the exchange of coordinated ligands. Thus a new synthetic strategy has been identified for modular porous solids which utilizes large, rigid, interlocked supermolecules as primary or secondary building blocks.
Self‐Assembly of Transition Metal Oxide Nanostructures on MXene Nanosheets for Fast and Stable Lithium Storage

Recently, a new class of 2D materials, i.e., transition metal carbides, nitrides, and carbonitrides known as MXenes, is unveiled with more than 20 types reported one after another. Since they are flexible and conductive, MXenes are expected to compete with graphene and other 2D materials in many applications. Here, a general route is reported to simple self‐assembly of transition metal oxide (TMO) nanostructures, including TiO2 nanorods and SnO2 nanowires, on MXene (Ti3C2) nanosheets through van der Waals interactions. The MXene nanosheets, acting as the underlying substrate, not only enable reversible electron and ion transport at the interface but also prevent the TMO nanostructures from aggregation during lithiation/delithiation. The TMO nanostructures, in turn, serve as the spacer to prevent the MXene nanosheets from restacking, thus preserving the active areas from being lost. More importantly, they can contribute extraordinary electrochemical properties, offering short lithium diffusion pathways and additional active sites. The resulting TiO2/MXene and SnO2/MXene heterostructures exhibit superior high‐rate performance, making them promising high‐power and high‐energy anode materials for lithium‐ion batteries.
Finding potential influences of a specific financial market in Twitter

This paper proposes a new framework to identify and rank Twitter accounts of which short messages or tweets may influence a specific financial stock price. In this paper, we start by mainly focusing on the first step of our framework, selecting potential influencers based on their association with a particular stock market. With numerous limitations of Twitter service to access and acquire Twitters data, a new methodology is also proposed to use a List feature to select potential financial influencers. It requires only Lists of accounts that contain an official Twitter account of the specific financial market, provided by Twitter's users, through Twitter API. Our methodology is designed to use only a small amount of data, by which it can be practically used under the limitation of Twitter API's crawling rate. Experimental results show that most of the potential influencers returned by our methodology are similar and related to the specific financial markets, which are companies listed on S&P 500 in this experiment. Most of the returned influencers are the official accounts of company or organization from the same sector and news media with a special emphasis on the same industry. Comparing to Twitter's user recommendation service (Who-To-Follow) and a crowdsourcing search for topic experts (Cognos), our methodology returns more related accounts in both percentage and the number.
Gene Expression Profiling in Entamoeba histolytica Identifies Key Components in Iron Uptake and Metabolism

Entamoeba histolytica is an ameboid parasite that causes colonic dysentery and liver abscesses in humans. The parasite encounters dramatic changes in iron concentration during its invasion of the host, with relatively low levels in the intestinal lumen and then relatively high levels in the blood and liver. The liver notably contains sources of iron; therefore, the parasite's ability to use these sources might be relevant to its survival in the liver and thus the pathogenesis of liver abscesses. The objective of the present study was to identify factors involved in iron uptake, use and storage in E. histolytica. We compared the respective transcriptomes of E. histolytica trophozoites grown in normal medium (containing around 169 µM iron), low-iron medium (around 123 µM iron), iron-deficient medium (around 91 µM iron), and iron-deficient medium replenished with hemoglobin. The differentially expressed genes included those coding for the ATP-binding cassette transporters and major facilitator transporters (which share homology with bacterial siderophores and heme transporters) and genes involved in heme biosynthesis and degradation. Iron deficiency was associated with increased transcription of genes encoding a subset of cell signaling molecules, some of which have previously been linked to adaptation to the intestinal environment and virulence. The present study is the first to have assessed the transcriptome of E. histolytica grown under various iron concentrations. Our results provide insights into the pathways involved in iron uptake and metabolism in this parasite.
Participatory Assessment of Development Interventions

This article presents the principles and findings of developing a new participatory assessment of development (PADev) evaluation approach that was codesigned with Dutch nongovernmental organizations (NGOs) and northern and southern research institutes over a period of 4 years in the context of rural development in Ghana and Burkina Faso. Although participatory approaches in development evaluations have become widely accepted since the 1990s, the PADev approach is different by taking the principles of holism and local knowledge as starting points for its methodological elaboration. The PADev approach is found to have an added value for assessing the differentiated effects of development interventions across different subgroups in a community through intersubjectivity. Moreover, if PADev is taken up by a multitude of stakeholders, including the intended beneficiaries of development interventions and development stakeholders, it can contribute to a process of local history writing, knowledge sharing, capacity development, and providing input into community action plans and the strategies of community-based organizations and NGOs.
Depression Awareness and Self-Management Through the Internet: Protocol for an Internationally Standardized Approach

Background Depression incurs significant morbidity and confers increased risk of suicide. Many individuals experiencing depression remain untreated due to systemic and personal barriers to care. Guided Internet-based psychotherapeutic programs represent a promising means of overcoming such barriers and increasing the capacity for self-management of depression. However, existing programs tend to be available only in English and can be expensive to access. Furthermore, despite evidence of the effectiveness of a number of Internet-based programs, there is limited evidence regarding both the acceptability of such programs and feasibility of their use, for users and health care professionals. Objective This paper will present the protocol for the development, implementation, and evaluation of the iFightDepression tool, an Internet-based self-management tool. This is a cost-free, multilingual, guided, self-management program for mild to moderate depression cases. Methods The Preventing Depression and Improving Awareness through Networking in the European Union consortium undertook a comprehensive systematic review of the available evidence regarding computerized cognitive behavior therapy in addition to a consensus process involving mental health experts and service users to inform the development of the iFightDepression tool. The tool was implemented and evaluated for acceptability and feasibility of its use in a pilot phase in 5 European regions, with recruitment of users occurring through general practitioners and health care professionals who participated in a standardized training program. Results Targeting mild to moderate depression, the iFightDepression tool is based on cognitive behavioral therapy and addresses behavioral activation (monitoring and planning daily activities), cognitive restructuring (identifying and challenging unhelpful thoughts), sleep regulation, mood monitoring, and healthy lifestyle habits. There is also a tailored version of the tool for young people, incorporating less formal language and additional age-appropriate modules on relationships and social anxiety. The tool is accompanied by a 3-hour training intervention for health care professionals. Conclusions It is intended that the iFightDepression tool and associated training for health care professionals will represent a valuable resource for the management of depression that will complement existing resources for health care professionals. It is also intended that the iFightDepression tool and training will represent an additional resource within a multifaceted approach to improving the care of depression and preventing suicidal behavior in Europe.
Incremental Domain Adaptation of Deformable Part-based Models

Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers by adapting them from the previous training environment (source domain) to the new testing one (target domain) is an approach with increasing acceptance in the computer vision community. In this paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data.
Natural User Interface for Robot Task Assignment

—We present a user interface that utilizes hand-tracking and gesture detection to provide a natural and intuitive interface to a semi-autonomous robotic manipulation system. The developed interface leverages humans and robots for what they each do best. The robotic system performs object detection, and grasp and motion planning. The human operator oversees high-level task assignment and manages unexpected failures. We discuss how this low-cost, task-level interface can reduce operator training time, decrease an operator’s cognitive load, and increase their situational awareness. There are many potential applications for this system such as explosive ordnance disposal, logistics package handling, and assembly tasks.
Loop corrections in the spectrum of two-dimensional Hawking radiation

We determine the one- and two-loop backreaction corrections in the spectrum of the Hawking radiation for the Callan - Giddings - Harvey - Strominger (CGHS) model of two-dimensional dilaton gravity by evaluating the Bogoliubov coefficients for a massless scalar field propagating on the corresponding backgrounds. Since the backreaction can induce a small shift in the position of the classical horizon, we find that a positive shift leads to a non-Planckian late-time spectrum, while a null or a negative shift leads to a Planckian late-time spectrum in the leading-order stationary-point approximation. In the one-loop case there are no corrections to the classical Hawking temperature, while in the two-loop case the temperature is three times greater than the classical value. We argue that these results are consistent with the behaviour of the Hawking flux obtained from the operator quantization only for times which are not too late, in accordance with the limits of validity of the semiclassical approximation.