Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Inverse Reinforcement Learning

This paper presents a simple yet general framework for employing deep architectures to solve the inverse reinforcement learning (IRL) problem. In particular, we propose to exploit the representational capacity and favourable computational complexity of deep networks to approximate complex, nonlinear reward functions in scenarios with large state spaces. This leads to a framework with the ability to make reward predictions in constant time rather than scaling cubically in the number of state rewards observed. Furthermore, we show that the maximum entropy paradigm for IRL lends itself naturally to the efficient training of deep architectures. The approach presented outperforms the state-of-the-art on a new benchmark with a complex underlying reward structure representing strong interactions between features while exhibiting performance commensurate to state-of-the-art methods on a number of established benchmarks of comparatively low complexity.
Renal hemodynamics, function, and oxygenation during cardiac surgery performed on cardiopulmonary bypass: a modeling study

Acute kidney injury, a prevalent complication of cardiac surgery performed on cardiopulmonary bypass (CPB), is thought to be driven partly by hypoxic damage in the renal medulla. To determine the causes of medullary hypoxia during CPB, we modeled its impact on renal hemodynamics and function, and thus oxygen delivery and consumption in the renal medulla. The model incorporates autoregulation of renal blood flow and glomerular filtration rate and the utilization of oxygen for tubular transport. The model predicts that renal medullary oxygen delivery and consumption are reduced by a similar magnitude during the hypothermic (down to 28°C) phase of CPB. Thus, the fractional extraction of oxygen in the medulla, an index of hypoxia, is increased only by 58% from baseline. However, during the rewarming phase (up to 37°C), oxygen consumption by the medullary thick ascending limb increases 2.3‐fold but medullary oxygen delivery increases only by 33%. Consequently, the fractional extraction of oxygen in the medulla is increased 2.7‐fold from baseline. Thus, the renal medulla is particularly susceptible to hypoxia during the rewarming phase of CPB. Furthermore, autoregulation of both renal blood flow and glomerular filtration rate is blunted during CPB by the combined effects of hemodilution and nonpulsatile blood flow. Thus, renal hypoxia can be markedly exacerbated if arterial pressure falls below its target level of 50 mmHg. Our findings suggest that tight control of arterial pressure, and thus renal oxygen delivery, may be critical in the prevention of acute kidney injury associated with cardiac surgery performed on CPB.
Point-cloud-to-point-cloud technique on tool calibration for dental implant surgical path tracking

Dental implant is one of the most popular methods of tooth root replacement used in prosthetic dentistry. Computerize navigation system on a pre-surgical plan is offered to minimize potential risk of damage to critical anatomic structures of patients. Dental tool tip calibrating is basically an important procedure of intraoperative surgery to determine the relation between the hand-piece tool tip and hand-piece's markers. With the transferring coordinates from preoperative CT data to reality, this parameter is a part of components in typical registration problem. It is a part of navigation system which will be developed for further integration. A high accuracy is required, and this relation is arranged by point-cloud-to-point-cloud rigid transformations and singular value decomposition (SVD) for minimizing rigid registration errors. In earlier studies, commercial surgical navigation systems from, such as, BrainLAB and Materialize, have flexibility problem on tool tip calibration. Their systems either require a special tool tip calibration device or are unable to change the different tool. The proposed procedure is to use the pointing device or hand-piece to touch on the pivot and the transformation matrix. This matrix is calculated every time when it moves to the new position while the tool tip stays at the same point. The experiment acquired on the information of tracking device, image acquisition and image processing algorithms. The key success is that point-to-point-cloud requires only 3 post images of tool to be able to converge to the minimum errors 0.77%, and the obtained result is correct in using the tool holder to track the path simulation line displayed in graphic animation.
Research paper on E-Learning application design features: Using cloud computing & software engineering approach

For several years, the importance of Cloud computing influences in many areas including E-Learning. Education is seen as important for every individual and country's growth. Basics objective is to design an Application Model to support eLearning Services. The current e-learning systems lack the appropriate infrastructures & efficacy integrated Application Model. A cloud technology gives platform to run our e-learning applications on services basis to any end users using the internet from cloud infrastructure. It will provide optimum affordable price package to educational organizations in particular for trainer and learners. We need to combine various technologies to achieve this particular objective. Further explains about importance of the E-Learning Design features and analyses the need of cloud computing. So, we need to figure out cloud based application model implementation's importance for e-learning system, and which made an active research on following manner: its working method, architecture design, Development tools and external interface with the application model, Software Engineering approaches. This paper describes about importance using of cloud environments for any institutes and learners usage, to underscore its possible benefits and offerings in term design.
Embedded wireless sensors for aircraft/automobile tire structural health monitoring

Structural Health Monitoring (SHM) of automobile tires is an active area of research. Within this area, the monitoring of strain on tires using wireless devices and networks is gaining prominence because these techniques do not require any wired connection to a base station. In this paper we discuss a wireless sensor network based strain monitoring architecture for aircraft/automobile tires present some initial results, and outline future plans.
NanoPower CMOS Voltage Reference with Temperature and Process Compensation

A low-power CMOS voltage reference independent of temperature and process is presented. The PTAT current source based on subthreshold MOSFET biases the active load to generate a voltage reference of 660mV. Process insensitive performance is achieved by controlling the bulk of a high L/W ratio PMOS active load. The temperature coefficient is 36ppm/ °C in the range of [-20, 80] °C, and, the PSRR at 1 KHz and 10 KHz is -43.5 and -40 dB, respectively. And power consumption is 420nW at normal supply voltage, 1.2V. The circuit was designed with 0. 13μm standard CMOS process.
The politics of technology in Africa: communication, development, and nation-building in Ethiopia

The politics of technology in Africa: communication, development, and nation-building in Ethiopia by Iginio Gagliardone, New York, Cambridge University Press, 2016, $99.99, 190 pp., ISBN 9781107177857 Mercy Muendo To cite this article: Mercy Muendo (2019) The politics of technology in Africa: communication, development, and nation-building in Ethiopia, Information, Communication & Society, 22:12, 1837-1840, DOI: 10.1080/1369118X.2019. 1590440 To link to this article: https://doi.org/10.1080/1369118X.2019. 1590440
Recognition by Rule Extraction from Support Vector Machines

Affective computation allows machines to express and recognize emotions, a core component of computer games. A natural way to express emotion is language, through text and speech; computational methods that accurately recognize emotion in text and speech are therefore important. Machine learning techniques such as support vector machines (SVMs) have been used successfully for topic detection in documents and speech as well as for the identification of authors/speakers. SVMs have also been used for emotion detection in written and spoken communication, although with mixed success. An impediment to emotion extraction by use of support vector machines is that, after learning, it is not quite clear what has been learned. For instance, a gamer may acoustically respond to a character with fear and the SVMs that observe user behaviour confuse the sentiment (fear) with the character (e.g. an in-game persona). Successful emotion identification by support vector machines requires methods that ensure the recognition of sentiments without any confusion with certain topics or characters. This paper provides an introduction to affective computation and rule extraction from support vector machines, a set of techniques used for emotion recognition in text.
Organ-dependent in vivo priming of naive CD4+,but not CD8+,T cells by plasmacytoid dendritic cells

Plasmacytoid dendritic cells (PDCs) play a pivotal role as cytokine-secreting accessory cells in the antimicrobial immune defense. In contrast, the capacity of PDCs to act as antigen-presenting cells in naive T cell priming remains unclear. By studying T cell responses in mice that lack conventional DCs (cDCs), and by the use of a PDC-specific antigen-targeting strategy, we show that PDCs can initiate productive naive CD4+ T cell responses in lymph nodes, but not in the spleen. PDC-triggered CD4+ T cell responses differed from cDC-driven responses in that they were not associated with concomitant CD8+ T cell priming. Our results establish PDCs as a bona fide DC subset that initiates unique CD4+ Th cell–dominated primary immune responses.
Video shot boundary detection using midrange local binary pattern

The fundamental step in content based video retrieval is shot boundary detection. This step is essential for characterizing videos in any video processing system. Reliable detection of shots in videos is still a challenging issue. In this work, we address automatic detection of abrupt shots in video sequences. We have proposed a method termed as Midrange LBP (MRLBP), which enhances the discriminative capability of basic LBP. Each frame of a video is processed to extract LBP histogram values based on midrange statistics for its description. A dissimilarity measure is applied on the feature vectors of adjacent frames and the distance values obtained are used for shot detection process using adaptive threshold approach. To check the efficacy of the proposed method, we carried out experiments on a subset of standard video data set TRECVID 2001. The results obtained by the proposed approach outperform the existing shot boundary detection algorithms in terms of different performance measures.
Perceived Social Support and Suicidal Ideation: The Moderating Role of thwarted Belongingness and Burdensomeness

Suicide is a global problem whose causes have not been fully understood yet. Clearly, suicide is the result of complex interactions of various factors, yet Asian studies have reported social stresses and interpersonal problems as more important contributors. The purpose of the present study is to develop a model to predict suicidal ideations based on perceived social support, thwarted belongingness, and perceived burdensomeness as the variables. The research method is correlational and the statistical population consists of single 20-30year-old girls in Isfahan City chosen by multistage clustering sampling. The sample size included 239 participants and the research tools were the multidimensional scale of perceived social support (Zimet, Dahlem, Zimet & Farely, 1988), and the suicidal ideations (Reynolds, 1987-1988 quoted by Beaumont, 1994) questionnaires. Studying the general fitting of the recommended model was performed by AMOS-22 statistical software. The research results indicated that the social support of family predicts suicidal ideations through thwarted belongingness and perceived burdensomeness directly and indirectly, and friends' support predicts it indirectly through thwarted belongingness. However, the relationship between support of significant others and suicidal ideations was not verified. The results also suggested the relatively different role of family and friends in development of suicidal ideations, but generally increase in the social support of family and friends can inhibit suicidal ideations.
Designing energy-efficient NoC for real-time embedded systems through slack optimization

Hard real-time embedded systems impose a strict latency requirement on interconnection subsystems. In the case of network-on-chip (NoC), this means each packet of a traffic stream has to be delivered within a time interval. In addition, with the increasing complexity of NoC, it consumes a significant portion of total chip power, which boosts the power footprint of such chips. In this work, we propose a methodology to minimize the energy consumption of NoC without violating the prespecified latency deadlines of real-time applications. First, we develop a formal approach based on network calculus to obtain the worst-case delay bound of all packets, from which we derive a safe estimate of the number of cycles that a packet can be further delayed in the network without violating its deadline- the worst-case slack. With this information, we then develop an optimization algorithm that trades the slacks for lower NoC energy. Our algorithm recognizes the distribution of slacks for different traffic streams, and assigns different voltages and frequencies to different routers to achieve NoC energy-efficiency, while meeting the deadlines for all packets.
Towards adaptive intrusion detection in mobile ad hoc networks

One of the main challenges in budding intrusion detection systems (IDSs) for mobile ad hoc networks (MANETs) is to integrate the mobility impact and adjust the behavior of IDSs dynamically. In this paper, focusing on the protection of MANET routing protocols, we first demonstrate that nodes' moving speed, a commonly used parameter in tuning IDS performance, is not an effective metric for the performance measurement of IDSs for MANETs. We then propose a new feature - the link change rate, which can not only act as a unified metric in measuring MANET IDS performance, but also be used to facilitate local MANET IDSs to select normal profiles adaptively. We utilize different mobility models to study the performance of our proposed adaptive mechanisms at different mobility levels. Simulation results show that our proposed adaptive mechanisms are effective and less dependent on mobility models. Detailed analysis of simulation results is also provided.
Essays in Spatial Economics

Essays in Spatial Economics Jonathan I. Dingel A central concern in international economics and urban economics is explaining the distributions of economic assets and activity across space. This dissertation contains three essays examining the pattern of specialization across US cities. Chapter 1 investigates the determinants of quality specialization within products. A growing literature suggests that high-income countries export high-quality goods. Two hypotheses may explain such specialization, with different implications for welfare, inequality, and trade policy. Fajgelbaum, Grossman, and Helpman (2011) formalize the Linder (1961) conjecture that home demand determines the pattern of specialization and therefore predict that high-income locations export high-quality products. The factor-proportions model also predicts that skill-abundant, highincome locations export skill-intensive, high-quality products (Schott, 2004). Prior empirical evidence does not separate these explanations. I develop a model that nests both hypotheses and employ microdata on US manufacturing plants’ shipments and factor inputs to quantify the two mechanisms’ roles in quality specialization across US cities. Home-market demand explains at least as much of the relationship between income and quality as differences in factor usage. In Chapter 2, Donald R. Davis and I develop a theory to jointly explain the distributions of skills, occupations, and industries across cities. Our model incorporates a system of cities, their internal urban structures, and a high-dimensional theory of factor-driven comparative advantage. It predicts that larger cities will be skill-abundant and specialize in skill-intensive activities according to the monotone likelihood ratio property. We test the model using data on 270 US metropolitan areas, 3 to 9 educational categories, 22 occupations, and 21 manufacturing industries. The results provide support for our theory’s predictions. Chapter 3 examines whether larger cities are attractive to consumers. Popular and academic discussions celebrate the virtues of large cities for consumption and leisure. But the standard spatial-equilibrium account says that the consumer attractions of larger cities cannot account for their higher nominal wages and more skilled populations. This chapter revisits that conclusion and shows that the consumption motive can play a first-order role in spatial variation in wage distributions when individuals are heterogeneous. I present a general-equilibrium model in which larger cities offer a greater variety of local goods and services, attracting higher-income individuals who value such variety relatively more. Despite the absence of production-related agglomeration economies, the equilibrium outcomes match a series of facts about spatial variation in wage distributions. I present evidence on the spatial choices of retirees, who consume but do not produce, that is consistent with consumption-driven agglomeration.
Nestly - a Framework for Running Software with Nested Parameter Choices and Aggregating Results

UNLABELLED The execution of a software application or pipeline using various combinations of parameters and inputs is a common task in bioinformatics. In the absence of a specialized tool to organize, streamline and formalize this process, scientists must write frequently complex scripts to perform these tasks. We present nestly, a Python package to facilitate running tools with nested combinations of parameters and inputs. nestly provides three components. First, a module to build nested directory structures corresponding to choices of parameters. Second, the nestrun script to run a given command using each set of parameter choices. Third, the nestagg script to aggregate results of the individual runs into a CSV file, as well as support for more complex aggregation. We also include a module for easily specifying nested dependencies for the SCons build tool, enabling incremental builds. AVAILABILITY Source, documentation and tutorial examples are available at http://github.com/fhcrc/nestly. nestly can be installed from the Python Package Index via pip; it is open source (MIT license).
Improving the Precision of Synset Links Between Cornetto and Princeton WordNet

Knowledge-based multilingual language processing benefits from having access to correctly established relations between semantic lexicons, such as the links between different WordNets. WordNet linking is a process that can be sped up by the use of computational techniques. Manual evaluations of the partly automatically established synonym set (synset) relations between Dutch and English in Cornetto, a Dutch lexical-semantic database associated with the EuroWordNet grid, have confronted us with a worrisome amount of erroneous links. By extracting translations from various bilingual resources and automatically assigning a confidence score to every pre-established link, we reduce the error rate of the existing equivalence relations between both languages’ synsets (section 2). We will apply this technique to reuse the connection of Sclera and Beta pictograph sets and Cornetto synsets to Princeton WordNet and other WordNets, allowing us to further extend an existing Dutch text-to-pictograph translation tool to other languages (section 3).
Investment Distortion by Collateral Requirement: Evidence from Japanese SMEs

We examine the significance of the distortionary effect of the collateral requirement to investments in assets pledgeable for collateral by small and medium-sized enterprises (SMEs). The theory predicts that the binding collateral constraint causes over-investment if the price of pledgeable assets is expected to go up steeply while it causes under-investment otherwise. Our structural estimation of the Euler equation under a collateral constraint using the dataset on Japanese SMEs in the 1980s and 1990s shows that the collateral constraint is binding when the price of a pledgeable asset is declining, whereas it is not when the price is increasing. This finding indicates that the binding collateral constraint causes mainly the problem of under-investment for many SMEs in a recession and casts doubt on the welfare effect of the loan-to-value (LTV) ratio cap as a macroprudence policy.
Constructing Optimal Prediction Intervals by Using Neural Networks and Bootstrap Method

This brief proposes an efficient technique for the construction of optimized prediction intervals (PIs) by using the bootstrap technique. The method employs an innovative PI-based cost function in the training of neural networks (NNs) used for estimation of the target variance in the bootstrap method. An optimization algorithm is developed for minimization of the cost function and adjustment of NN parameters. The performance of the optimized bootstrap method is examined for seven synthetic and real-world case studies. It is shown that application of the proposed method improves the quality of constructed PIs by more than 28% over the existing technique, leading to narrower PIs with a coverage probability greater than the nominal confidence level.
Watermarking Algorithm for Print-Scan Based on HVS and Multiscale Error Diffusion

In this paper, we introduce a watermarking algorithm for halftone image. The arithmetic is based on HVS and multiscale error diffusion. We insert special binary image in the halftone image and study on it after printing and scanning. Using the proposed methods, several hundred information bits can be embedded into images with perfect recovery against the print-scan operation. Different printing resolution, different scanning resolution and printing paper type are experimented. The experiments show that the watermarking algorithm is robust to print-scan process.
Fast, Private and Verifiable: Server-aided Approximate Similarity Computation over Large-Scale Datasets

Computing similarity, especially Jaccard Similarity, between two datasets is a fundamental building block in big data analytics, and extensive applications including genome matching, plagiarism detection, social networking, etc. The increasing user privacy concerns over the release of has sensitive data have made it desirable and necessary for two users to evaluate Jaccard Similarity over their datasets in a privacy-preserving manner. In this paper, we propose two efficient and secure protocols to compute the Jaccard Similarity of two users' private sets with the help of an unfully-trusted server. Specifically, in order to boost the efficiency, we leverage Minhashing algorithm on encrypted data, where the output of our protocols is guaranteed to be a close approximation of the exact value. In both protocols, only an approximate similarity result is leaked to the server and users. The first protocol is secure against a semi-honest server, while the second protocol, with a novel consistency-check mechanism, further achieves result verifiability against a malicious server who cheats in the executions. Experimental results show that our first protocol computes an approximate Jaccard Similarity of two billion-element sets within only 6 minutes (under 256-bit security in parallel mode). To the best of our knowledge, our consistency-check mechanism represents the very first work to realize an efficient verification particularly on approximate similarity computation.