Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient and precise interactive hand tracking through joint, continuous optimization of pose and correspondences

Fully articulated hand tracking promises to enable fundamentally new interactions with virtual and augmented worlds, but the limited accuracy and efficiency of current systems has prevented widespread adoption. Today's dominant paradigm uses machine learning for initialization and recovery followed by iterative model-fitting optimization to achieve a detailed pose fit. We follow this paradigm, but make several changes to the model-fitting, namely using: (1) a more discriminative objective function; (2) a smooth-surface model that provides gradients for non-linear optimization; and (3) joint optimization over both the model pose and the correspondences between observed data points and the model surface. While each of these changes may actually increase the cost per fitting iteration, we find a compensating decrease in the number of iterations. Further, the wide basin of convergence means that fewer starting points are needed for successful model fitting. Our system runs in real-time on CPU only, which frees up the commonly over-burdened GPU for experience designers. The hand tracker is efficient enough to run on low-power devices such as tablets. We can track up to several meters from the camera to provide a large working volume for interaction, even using the noisy data from current-generation depth cameras. Quantitative assessments on standard datasets show that the new approach exceeds the state of the art in accuracy. Qualitative results take the form of live recordings of a range of interactive experiences enabled by this new approach.
An Automated Image Analysis System to Quantify Endosomal Tubulation

Recycling of cargos from early endosomes requires regulation of endosomal tubule formation and fission. This regulation is disrupted in cells depleted of the microtubule severing enzyme spastin, causing elongation of endosomal tubules and mis-trafficking of recycling endosomal cargos such as the transferrin receptor. Spastin is encoded by SPAST, mutations in which are the most frequent cause of autosomal dominant hereditary spastic paraplegia, a condition characterised by a progressive loss of lower limb function resulting from upper motor neuron axonopathy. Investigation of molecular factors involved in endosomal tubule regulation is hindered by the need for manual counting of endosomal tubules. We report here the development of an open source automated system for the quantification of endosomal tubules, using ImageJ and R. We validate the method in cells depleted of spastin and its binding partner IST1. The additional speed and reproducibility of this system compared with manual counting makes feasible screens of candidates to further understand the mechanisms of endosomal tubule formation and fission.
Optimal Alphabets and Binary Labelings for BICM at Low SNR

Optimal binary labelings, input distributions, and input alphabets are analyzed for the so-called bit-interleaved coded modulation (BICM) capacity, paying special attention to the low signal-to-noise ratio (SNR) regime. For 8-ary pulse amplitude modulation (PAM) and for 0.75 bit/symbol, the folded binary code results in a higher capacity than the binary reflected Gray code (BRGC) and the natural binary code (NBC). The 1 dB gap between the additive white Gaussian noise (AWGN) capacity and the BICM capacity with the BRGC can be almost completely removed if the input symbol distribution is properly selected. First-order asymptotics of the BICM capacity for arbitrary input alphabets and distributions, dimensions, mean, variance, and binary labeling are developed. These asymptotics are used to define first-order optimal (FOO) constellations for BICM, i.e., constellations that make BICM achieve the Shannon limit -1.59 dB. It is shown that the Eb/N0 required for reliable transmission at asymptotically low rates in BICM can be as high as infinity, that for uniform input distributions and 8-PAM there are only 72 classes of binary labelings with a different first-order asymptotic behavior, and that this number is reduced to only 26 for 8-ary phase shift keying (PSK). A general answer to the question of FOO constellations for BICM is also given: using the Hadamard transform, it is found that for uniform input distributions, a constellation for BICM is FOO if and only if it is a linear projection of a hypercube. A constellation based on PAM or quadrature amplitude modulation input alphabets is FOO if and only if they are labeled by the NBC; if the constellation is based on PSK input alphabets instead, it can never be FOO if the input alphabet has more than four points, regardless of the labeling.
A Framework of Process Mining for RFID Event Analysis

As information systems and telecommunication devices are spread, many organizations accumulate a lot of events which are generated in performing business activities. The analysis of real-time data and events can play a critical role in implementing real-time enterprises and business intelligence. Recently, supply chain and manufacturing sectors have adopted ubiquitous environment that generate RFID and sensor events. However events analysis to discover important information is desirable. In this paper, we propose a framework of business process mining for RFID events analysis. We explain how raw RFID events can make business process data accessible for process mining. The results of this research can be widely used for RFID events analysis in various areas such as logistics and manufacturing systems.
A Configuration Space View of View Planning

For sensor-based robot motion planning, view planning problem refers to planning the next sensing action to further facilitate the motion planning task. In Y. Yu and K. Gupta (2004), C-space entropy was introduced as a measure of knowledge of robot configuration space, or C-space. The robot plans the next sensing action to maximally reduce the expected C-space entropy, also called the maximal expected entropy reduction, or MER criterion. It was shown that MER criterion resulted in much more efficient C-space exploration performance than physical space based view planning criteria, such as to maximize unknown physical volume in each view. From a C-space perspective, MER criterion consists of two important aspects: sensing actions are evaluated in C-space (geometric aspect); these effects are evaluated in an information theoretical sense (stochastic aspect). In this paper, we investigate how much of this better performance is attributable to the paradigmatic shift to evaluating the sensor action in C-space, i.e., the pure geometric component of MER, and how much is attributable to the stochastic aspect of MER. We propose C-space based pure geometric criteria (which are essentially geometric aspect of MER) for view planning and compare them with the MER criterion. We empirically show that a great deal of efficiency is attributable to the pure geometric aspect; however, we also show that the stochastic aspect, despite being based on simple assumptions, result in moderately more efficient C-space exploration over the pure geometric component of MER. We outline explanations for our findings
VSD2014: A dataset for violent scenes detection in hollywood movies and web videos

In this paper, we introduce a violent scenes and violence-related concept detection dataset named VSD2014. It contains annotations as well as auditory and visual features of Hollywood movies and user-generated footage shared on the web. The dataset is the result of a joint annotation endeavor of different research institutions and responds to the real-world use case of parental guidance in selecting appropriate content for children. The dataset has been validated during the Violent Scenes Detection (VSD) task at the MediaEval benchmarking initiative for multimedia evaluation.
Learning to be Capitalists: Entrepreneurs in Vietnam's Transition Economy

Given the overwhelming number of ineffective economic reform policies and programs, a central question for international development concerns how significant economic change happens. In the midst of this quandary, a puzzle has been growing quietly the last two decades. Vietnam has transitioned from a poor, centrally planned economy to one of the fastest growing, market economies in the world, despite ignoring conventional reform strategies. This book focuses on solving a specific puzzle of Vietnam's transition. Its fastest growing city, Ho Chi Minh City, has a real estate industry that ranks as the worst place in the world for private capital to invest . Nevertheless, entrepreneurs have emerged to form private firms within the first decade of transition. Where did these people come from? How could they conduct business in such an inhospitable economic environment? The book finds that the transition to capitalism is neither the natural propensity of individuals nor the decision of an all-powerful state nor necessarily requires a long, evolutionary process. The major, rapid, and discontinuous economic change that occurred in Vietnam was fundamentally enabled by a social reconstruction of cognitive paradigms. The new social cognition framework accounts for why some firms were more successful than others as well as why Vietnam's capitalism has surprising characteristics. Available in OSO: http://www.oxfordscholarship. com/oso/public/content/economicsfinance/9780195369397/toc.html
A neurofeedback system to promote learner engagement

This report describes a series of experiments that track novice programmer's engagement during two attention based tasks. The tasks required participants to watch a tutorial video on introductory programming and to attend to a simple maze game whilst wearing an electroencephalogram (EEG)device called the Emotiv EPOC. The EPOC's proprietary software includes a system which tracks emotional state (specifically: engagement, excitement, meditation, frustration, valence and long-term excitement). Using this data, a software application written in the Processing language was developed to track user's engagement levels and implement a neurofeedback based intervention when engagement fell below an acceptable level. The aim of the intervention was to prompt learners who disengaged with the task to re-engage. The intervention used during the video tutorial was to pause the video if a participant disengaged significantly. However other interventions such as slowing the video down, playing a noise or darkening/brightening the screen could also be used. For the maze game, the caterpillar moving through the maze slowed in line with disengagement and moved more quickly once the learner re-engaged. The approach worked very well and successfully re-engaged participants, although a number of improvements could be made. A number of interesting findings on the comparative engagement levels of different groups e.g. by gender and by age etc. were identified and provide useful pointers for future research studies.
Efficient and robust query processing in dynamic environments using random walk techniques

Many existing systems for sensor networks rely on state information stored in the nodes for proper operation (e.g., pointers to parent in a spanning tree, routing information, etc). In dynamic environments, such systems must adopt failure recovery mechanisms, which significantly increase the complexity and impact the overall performance. We investigate alternative schemes for query processing based on random walk techniques. The robustness of this approach under dynamics follows from the simplicity of the process, which only requires the connectivity of the neighborhood to keep moving. In addition we show that visiting a constant fraction of sensor network, say 80%, using a random walk is efficient in number of messages and sufficient for answering many interesting queries with high quality. Finally, the natural behavior of a random walk, also provide the important properties of load-balancing and scalability.
TrafficKarma: Estimating Effective Traffic Indicators using Public Data

TrafficKarma optimizes the use of publicly available information to monitor and estimate traffic data in order to map and visualize it for its effective use. This is achieved by using state of the art optimization and machine learning techniques coupled with insightful visualization of the data. Traffic Karma can be used by authorities in transportation, traffic police and various agencies that needs current(online) or past(statistical) information about traffic. This paper will concisely explain the features of the application proposed, strategy, data sources and methodology, system architecture and the data analysis techniques used.
Sliding Viewport for Interactive Virtual Environments

The ability to manipulate objects is fundamental to most virtual reality (VR) applications. A multitude of metaphors have been developed to facilitate object selection and manipulation [1], including virtual hand [2], which remains by far the most popular technique in this category. The utility of the virtual hand depends on the user’s ability to see it. Unfortunately, ensuring this is not always easy, especially in immersive systems which employ head mounted displays (HMD) with a limited field of view. Building on [3], we show how the virtual hand metaphor may be used effectively both to manipulate virtual objects, and to guide the user’s view in order to ensure continuous visibility of the hand. We provide implementation detail and report experimental results from the user studies. This paper is intended for an audience interested in building HMD-based VR systems, where users are required to actively interact with the environment by using their virtual hand.
FPGA based exhaust gas analysis for automotive vehicles

This paper presents the exhaust gas emission analysis for automotive vehicles which is accurate way to determine the running conditions of an engine. Verilog code is developed for FPGA with its RTL schematic simulated in Xilinx along with waveform of gas emission results are presented. The main aim of the paper is intended to read the data from Sensors such as Oxygen & Carbon monoxide interfaced to FPGA board to get emissions gas information and to carry out its analysis by comparing with ideal conditions. The basic principles regarding why and how exhaust analysis carried out is briefly discussed for better understanding along with different methods of gas analysis. The advantage of this automotive system design is that it offers accurate detection of gases and faster response time compared to manual methods.
A Post Keynesian Environmental Macroeconomic Model for Agricultural Water Sustainability under Climate Change in the Murray-Darling Basin, Australia

Climate change has profound consequences for the agriculture of south-eastern Australia and its climate-induced water shortage in the Murray-Darling Basin. Post Keynesian Economics (PKE) macro-dynamics, along with Kaleckian investment and growth theory, are used to develop an ecological-economic system dynamics model of this complex nonlinear river basin system. The MurrayDarling Basin Simulation Model (MDB-SM) uses the principles of PKE to incorporate the fundamental uncertainty of economic behaviors of farmers regarding the investments they make and the climate change they face, particularly as regards water ecosystem services. MDB-SM provides a framework for macroeconomic policies, especially for long-term fiscal policy and for policy directed at the sustainability of agricultural water, as measured by socio-economic well-being considerations, which include sustainable consumption and investment in the river basin. The model can also reproduce other ecological and economic aspects and, for certain parameters and initial values, exhibit endogenous business cycles and ecological sustainability with realistic characteristics. Most importantly, MDBSM provides a platform for the analysis of alternative economic policy scenarios. These results reveal the importance of understanding water ecosystem adaptation under climate change by integrating a PKE macroeconomic analytical framework with the system dynamics modelling approach. Once parameterised and supplied with historical initial values, MDB-SM should prove to be a practical tool to provide alternative long-term policy simulations of agricultural water and socio-economic well-being. Keywords—Agricultural water, Macroeconomic dynamics, Modeling, Investment dynamics, Sustainability, Unemployment, Economics, Keynesian, Kaleckian.
A New Scale for Measuring Insomnia: The Bergen Insomnia Scale

A new scale for the measurement of insomnia, the Bergen Insomnia Scale, was constructed on the basis of current formal and clinical diagnostic criteria for insomnia. There are six items, of which the first three pertain to sleep onset, maintenance, and early morning wakening insomnia, respectively. The last three items refer to not feeling adequately rested, experiencing daytime impairment, and being dissatisfied with current sleep. This scale was validated in three samples, 320 students, 2,645 community persons, and 225 patients. Cronbach alphas in the three samples were . 79, . 87, and . 80, respectively. The 2-wk. test-retest reliability for students was . 77. In the student and the patient samples, a two-factor solution was found, nocturnal symptoms and daytime symptoms, but in the community sample, a one-factor solution was found. The Bergen Insomnia Scale discriminated well between the patient sample and the other two. In all three, values of convergent and discriminative validity in relation to other self-report measures were good, as well as in relation to polysomnographic data for patients. It is concluded that the Bergen Insomnia Scale has good psychometric properties. It is one of very few insomnia scales which provide normative data for comparisons and which has been validated against subjective as well as polysomnographic data.
Autonomous Navigation in Crowded Campus Environments

This paper considers autonomous navigation in crowded city environments. An autonomous vehicle testbed is presented. We address two challenges of pedestrian detection and GPS-based localization in the presence of high-level buildings. First, we augment the localization using local laser maps and show improved results. A pedestrian detection algorithm using a complementary vision and laser system is proposed. We implement this algorithm in our testbed and evaluate its performance. We also show how utilizing existing infrastructural sensors can improve the performance of the system. Potential applications of this work include fully automated vehicle systems in urban environments typical in megacities in Asia.
Modeling Ecohydrological Processes and Spatial Patterns in the Upper Heihe Basin in China

The Heihe River is the second largest inland basin in China; runoff in the upper reach greatly affects the socio-economic development in the downstream area. The relationship between spatial vegetation patterns and catchment hydrological processes in the upper Heihe basin has remained unclear to date. In this study, a distributed ecohydrological model is developed to simulate the hydrological processes with vegetation dynamics in the upper Heihe basin. The model is validated by hydrological observations at three locations and soil moisture observations at a watershed scale. Based on the simulated results, the basin water balance characteristics and their relationship with the vegetation patterns are analyzed. The mean annual precipitation and runoff increase with the elevation in a similar pattern. Spatial patterns of the actual evapotranspiration is mainly controlled by the precipitation and air temperature. At the same time, vegetation distribution enhances the spatial variability of the actual evapotranspiration. The highest actual evapotranspiration is around elevations of 3000–3600 m, where shrub and alpine meadow are the two dominant vegetation types. The results show the mutual interaction between vegetation dynamics and hydrological processes. Alpine sparse vegetation and alpine meadow dominate the high-altitude regions, which contribute most to the river runoff, and forests and shrub contribute relatively small amounts of water yield.
Identifying Controlling Features of Engineering Design Iteration

Engineering design often involves a very complex set of relationships among a large number of coupled problems. It is this complex coupling that leads to iteration among the various engineering tasks in a large project. The design structure matrix DSM is useful in identifying where iteration is necessary. The work transformation matrix model developed in this paper is a powerful extension of the DSM method which can predict slow and rapid convergence of iteration within a project, and predict those coupled features of the design problem which will require many iterations to reach a technical solution. This model is applied to an automotive brake-system development process in order to illustrate the model's utility in describing the main features of an actual design process.
Weak value measurement with an incoherent measuring device

In the Aharonov–Albert–Vaidman (AAV) weak measurement, it is assumed that the measuring device or the pointer is in a quantum mechanical pure state. In reality, however, it is often not the case. In this paper, we generalize the AAV weak measurement scheme to include more generalized situations in which the measuring device is in a mixed state. We also report an optical implementation of the weak value measurement in which the incoherent pointer is realized with the pseudo-thermal light. The theoretical and experimental results show that the measuring device under the influence of partial decoherence could still be used for amplified detection of minute physical changes and is applicable for implementing the weak value measurement for massive particles.
Bankster's Paradise: The Importance Of Discourse In Creating A Haven For Criminogenic Banks In The United States

This work examines the ways in which language can contribute to a cultural climate in which white-collar crime is no longer considered “deviant,” but rather is considered part of a normally functioning political economy. The 2012 money laundering case involving HSBC is examined in conjunction with the rhetoric of popular financial counselor Dave Ramsey. This research seeks to define how language involving the accumulation of capital is equated with virtuousness, thus constructing a myth about the criminality inherent to “legitimate” capital enterprises.
The Economic Returns to Schooling in Israel

This paper examines the causal effect of education on earnings in Israel. The Ordinary Least Squares coefficient of schooling on earnings (the Mincer coefficient) is likely to be biased upwards because it ignores the unobserved 'ability' which is correlated with schooling and has a positive effect on earnings. We use two methods to estimate the causal effect of education on earnings: The first is to use proxy variables - variables that are correlated with the unobserved ability of the individual, such as mother’s education and siblings' wage. The second method is to use the amendment to the Compulsory Education Law, which extended free education to all high-school grades, and also raised the minimal age limit for compulsory education by one year, as an instrumental variable. We found that this amendment increased the years of schooling of students whose fathers were born in Asia or Africa. We conclude that the causal effect estimated by the instrumental variable was not different from that obtained using Ordinary Least Squares regressions.