Miracle-dz/UniFAR-FTUs huggingface dataset:
Total Static Balancing and Kinetostatics of the 3R Base Cinematic Chain

It has been presented in other works and studied matrix spatially, or more simply in a plan, but in this case, it is necessary to move from the working plane to the real space, or vice versa, passage that we will present in this study. In the basic plan module already presented in other geometric and cinematic works, we want to highlight some dynamic features such as static balancing, total balancing and determination of the strength of the module after balancing. The forces that appear in the unbalanced base module were also presented in other papers, so we want to fill in only the aspects of the already balanced module forces in this study. The mechanism in Fig. 1 (planar cinematic chain) must be balanced to have a normal operation. Through a total static balancing, balancing the gravitational forces and moments generated by the forces of gravity is achieved, balancing the forces of inertia and the moments (couples) generated by the presence of inertial forces (not to be confused with the inertial moments of the mechanism, which appear separately from the other forces, being part of the inertial torsion of a mechanism and depending on both the inertial masses of the mechanism and its angular accelerations. Balancing the mechanism can be done through various methods. Partial balancing is achieved almost in all cases where the actuators (electric drive motors) are fitted with a mechanical reduction, a mechanical transmission, a sprocket, spiral gear, spool screw type. Such a reducer called the unisens (the movement allowed by it is a two-way rotation, but the transmission of the force and the motor moment can only be done in one direction, from the spindle to the worm gear, vice versa from the worm gear to the screw the force cannot be transmitted and the movement is not possible by blocking the mechanism, which makes it apt to transmit the movement from the wheel of a vehicle to its wheels in the steering mechanism, not allowing the wheel forces due to the unevenness of the ground, to be transmitted to the steering wheel and implicitly to the driver, or this mechanism is suitable for mechanical meters so that they do not twist and vice versa etc.) can balance the transmission by letting the forces and motor moments unfold, but not allowing the kinematic elements to influence the movement through their forces of weight and inertia. This results in a "forced" drive balancing from the transmission, which makes the operation of the assembly to be correct but rigid and with mechanical shocks. Such balancing is not possible when the actuators directly actuate the elements of the kinematic chain without using mechanical reducers.
Revisiting Bellman Errors for Offline Model Selection

There has been a recent surge of reinforcement learning (RL) algorithms that can train an agent offline with varying 2 success [11, 15]. Most such algorithms rely on online environment interactions to tune their own hyperparameters. 3 However, applying offline RL algorithms in real-world settings necessitates tuning their hyperparameters fully offline 4 (i.e. offline model selection). Prevalent offline model selection approaches are typically based on offline policy 5 evaluation (OPE) algorithms that estimate the expected returns under a given policy [28, 34, 38]. Unfortunately, 6 accurately estimating the expected returns under one policy using data generated by a completely different behavioral 7 policy can be very difficult. Other approaches that have been explored have been based on empirical Bellman errors 8 as an approximation for the true Bellman errors [13, 21, 28]. These works have found empirical Bellman errors to be 9 poor predictors of value model accuracy, leading to a current belief that Bellman errors are not useful in offline model 10 selection [10, 14, 28]. 11
Space-Time Domain Tensor Neural Networks: An Application on Human Pose Recognition

Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this work, we propose a spatially and temporally aware tensor-based neural network for human pose recognition using three-dimensional skeleton data. Our model employs three novel components. First, an input layer capable of constructing highly discriminative spatiotemporal features. Second, a tensor fusion operation that produces compact yet rich representations of the data, and third, a tensor-based neural network that processes data representations in their original tensor form. Our model is end-to-end trainable and characterized by a small number of trainable parameters making it suitable for problems where the annotated data is limited. Experimental validation of the proposed model indicates that it can achieve state-of-the-art performance. Although in this study, we consider the problem of human pose recognition, our methodology is general enough to be applied to any pattern recognition problem spatiotemporal data from sensor networks.
The Information Structure and Argument Mapping of Mandarin Chinese Resultative Verb Construction

This thesis will investigate the information structure and its interaction with argument mappings of Chinese resultative verb construction (RVC). One RVC may have several different mappings. This thesis proposes that these mappings have a strong relation with information structure. If a mapping is not in accord with the information structure of the sentence, this mapping will not be chosen by the speaker. This thesis proposes that there are four types of Topics in Chinese: primary Topic, secondary Topic, continuing Topic and contrastive Topic. The primary Topic and contrastive Topic motivate a Patient-type argument to be the Subject, and an Agent-type argument being a secondary Topic may not be the Subject of the sentence. Secondary Topics in BA and BEI construction bear different information update, and this blocks some mappings that are possible in the canonical structure to appear in BA and BEI construction.
Event Horizon Detection for Five Dimensional Stationary Black Holes

In this thesis we show how to locate the event horizons for five dimensional (5D) stationary black holes. We present the Cartan algorithm in an arbitrary number of dimensions and apply it in 4D and 5D. To facilitate the algorithm in 5D, we classifiy the Weyl tensor using its boost weight decomposition. We also consider the Lorentz frame transformations in 5D. We present the algorithm explicitly for the 4D Kerr metric. For 5D, computations by hand are not feasible. Thus we show how to perform the algorithm on Maple 2016 and illustrate it with four 5D examples: the singly rotating Myers-Perry metric, the Kerr-NUTT-(Anti)-de Sitter metric, the Reissner-Nordstrom-(Anti)-de Sitter metric, and the singly rotating static black ring.
Discriminator Delay line SPI SPI AND Single-Ended To Differential Step-by-step Current Pulse amplification Bandgap Reference DAC SPI Threhold Input LSD 2018

The single photon source is an important part of the quantum key distribution (QKD) system. At present, the single photon source is large in size and complex in structure for a lot of discrete components which are used. The miniaturization of the photon source is the tendency of the QKD system. We integrate all laser driver electronic module into one single ASIC chip, which can be used to drive the 1550nm DFB laser in random pulse mode and it can greatly reduce the volume of the single photon source. We present the design of the chip named LSD2018 and simulation results before the tape-out. The LSD2018 is fabricated with a 130 nm CMOS process and consists of a discriminator, an adjustable pulse generator, a bandgap reference, an SPI bus, and an amplitude-adjustable current pulse driver. The electronic random pulse from the driver can go 20mA to 120mA in amplitude and 400ps to 4ns in pulse width. The parameters can be set by an SPI bus.
Numerical solution of a semilinear parabolic degenerate Hamilton-Jacobi-Bellman equation with singularity

We consider a semilinear parabolic degenerated Hamilton-Jacobi-Bellman (HJB) equation with singularity which is related to a stochastic control problem with fuel constraint. The fuel constraint translates into a singular initial condition for the HJB equation. We first propose a transformation based on a change of variables that gives rise to an equivalent HJB equation with nonsingular initial condition but irregular coefficients. We then construct explicit and implicit numerical schemes for solving the transformed HJB equation and prove their convergences by establishing an extension to the result of Barles and Souganidis (1991).
Attack Detection for Iris Recognition : An Assessment of the State of the Art ADAM CZAJKA

Iris recognition is increasingly used in large-scale applications. As a result, presentation attack detection for iris recognition takes on fundamental importance. This survey covers the diverse research literature on this topic. Different categories of presentation attack are described and placed in an application-relevant framework, and the state of the art in detecting each category of attack is summarized. One conclusion from this is that presentation attack detection for iris recognition is not yet a solved problem. Datasets available for research are described, research directions for the nearand medium-term future, and a short list of recommended readings are suggested.
Predicting Stock Price Trend Using MACD Optimized by Historical Volatility

With the rapid development of the financial market, many professional traders use technical indicators to analyze the stock market. As one of these technical indicators, moving average convergence divergence (MACD) is widely applied by many investors. MACD is a momentum indicator derived from the exponential moving average (EMA) or exponentially weighted moving average (EWMA), which reacts more significantly to recent price changes than the simple moving average (SMA). Traders find the analysis of 12- and 26-day EMA very useful and insightful for determining buy-and-sell points. The purpose of this study is to develop an effective method for predicting the stock price trend. Typically, the traditional EMA is calculated using a fixed weight; however, in this study, we use a changing weight based on the historical volatility. We denote the historical volatility index as HVIX and the new MACD as MACD-HVIX. We test the stability of MACD-HVIX and compare it with that of MACD. Furthermore, the validity of the MACD-HVIX index is tested by using the trend recognition accuracy. We compare the accuracy between a MACD histogram and a MACD-HVIX histogram and find that the accuracy of using MACD-HVIX histogram is 55.55% higher than that of the MACD histogram when we use the buy-and-sell strategy. When we use the buy-and-hold strategy for 5 and 10 days, the prediction accuracy of MACD-HVIX is 33.33% and 12% higher than that of the traditional MACD strategy, respectively. We found that the new indicator is more stable. Therefore, the improved stock price forecasting model can predict the trend of stock prices and help investors augment their return in the stock market.
A Stackelberg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowdsensing

Mobile crowdsensing has shown great potential in addressing large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participate in a crowdsensing platform if they can receive a satisfactory reward. In this paper, to effectively and efficiently recruit a sufficient number of mobile users, i.e., participants, we investigate an optimal incentive mechanism of a crowdsensing service provider. We apply a two-stage Stackelberg game to analyze the participation level of the mobile users and the optimal incentive mechanism of the crowdsensing service provider using backward induction. In order to motivate the participants, the incentive mechanism is designed by taking into account the social network effects from the underlying mobile social domain. We derive the analytical expressions for the discriminatory incentive as well as the uniform incentive mechanisms. To fit into practical scenarios, we further formulate a Bayesian Stackelberg game with incomplete information to analyze the interaction between the crowdsensing service provider and mobile users, where the social structure information, i.e., the social network effects, is uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium is analytically validated by identifying the best response strategies of the mobile users. The numerical results corroborate the fact that the network effects significantly stimulate a higher mobile participation level and greater revenue for the crowdsensing service provider. In addition, the social structure information helps the crowdsensing service provider achieve greater revenue gain.
ipilimumab in the treatment of metastatic melanoma: management of adverse events

: Recently, “ipilimumab,” an anti-cytotoxic T-lymphocyte antigen-4 (CTLA-4) monoclonal antibody, has been demonstrated to improve overall survival in metastatic melanoma. “CTLA-4” is an immune-checkpoint molecule that downregulates pathways of T-cell activation. Ipilimumab, by targeting CTLA-4, is able to remove the CTLA-4 inhibitory signal, allowing the immune system to react to cancer cells. Due to its immune-based mechanism of action, ipilimumab causes the inhibition of CTLA-4-mediated immunomodulatory effects, the enhance-ment of antitumor specific immune response mediated by the weakening of self-tolerance mechanisms while exacerbating the development of autoimmune diseases and immune-related adverse events, including dermatitis, hepatitis, enterocolitis, hypophysitis, and uveitis.
Epigenetic modifications driving ground state pluripotency exit require an NF-κB-independent chromatin IκBα function

Inflammatory signals are key in development and cell differentiation but their orchestration with pluripotency and stemness signals is poorly understood. Our previous work identified a chromatin function of IκBα, the NF-κB inhibitor, that is crucial for differentiation in different types of somatic stem cells. Here we demonstrate that deficiency of IκBα imposes a profound chromatin rewiring defect that impacts on DNA methylation, histone post-translational modifications and transcriptional regulation, stabilizing mouse embryonic stem cells (ESCs) in a ground state of pluripotency while preventing them from pluripotency exit and differentiation. By engineering separation-of-function mutants of IκBα with specific binding to either NF-κB or histones, we demonstrate that regulation of pluripotency state by IκBα is independent of NF-κB but requires the chromatin-related IκBα function.
Automated Shack-Hartmann seeing measurements at the South Pole

The statistics and dynamics of the atmospheric seeing at the South Pole have been studied over a period of 101 days in winter. These measurements have been made with the first fully autonomous dierential image motion monitor, the A-DIMM. The analysis shows an average seeing of 1:9 00 with a standard deviation of 0:6 00 . The extensive set of data has allowed the study of the seeing time variations, showing that the seeing varies by a factor of two within a characteristic time of 2 hours.
2022 Non-Asymptotic Convergence Analysis of the Multiplicative Gradient Algorithm for PET-Type Problems

We analyze the non-asymptotic convergence rate of the multiplicative gradient (MG) algo-rithm for the PET-type problems, and show that it exhibits O (ln( n ) /t ) convergence rates, where n is the dimension of the optimization variable, in both ergodic and non-ergodic senses. Furthermore, we show that the distances from the iterates to the set of optimal solutions converge (to zero) at rate O (1 / √ t ).
Spine-like Joint Link Mechanism to Design Wearable Assistive Devices with Comfort and Support

When we develop wearable assistive devices comfort and support are two main issues needed to be considered. In conventional design approaches, the degree of freedom of wearer's joint movement tends to be oversimplified. Accordingly, the wearer's motion becomes restrained and bone/ligament injuries might occur in case of unexpected fall. To mitigate those issues, this letter proposes a novel joint link mechanism inspired by a human spine structure as well as functionalities. The key feature of the proposed spine-like joint link mechanism is that hemispherical blocks are concatenated via flexible synthetic fiber lines so that their concatenation stiffness can be adjusted according to a tensile force. This feature has a great potentiality for designing a wearable assistive devices that can support aged people's sit-to-stand action or augment a spinal motion by regulating the concatenation stiffness. In addition, the concatenated hemispherical blocks enables the wearer to move his/her joint with the full degree of freedom, which in turn, increases wearer's mobility and prevents joint misalignment. The experimental results with a testbed and a pilot wearer substantiated that the spine-like joint link mechanism can serve as a key component to design the wearable assistive devices for better mobility and safety.
Massive and dusty Hα emitters in protocluster revealed by ALMA and JVLA

Abstract We investigate the physical properties of Hα emitters (HAEs) associated to the protocluster 4C23.56 at z ~ 2.5 using continuum observations at submm (270 GHz) and radio (3 GHz) frequencies with Atacama Large Mm/submm Array (ALMA) and K. Jansky Very Large Array (JVLA). For more details see Lee et al. (in prep).
Particle swarm optimization for problems with variable number of dimensions

ABSTRACT Some real-life optimization problems, apart from dependence on the combination of state variables, also show dependence on the complexity of the model describing the problem. Changing model complexity implies changing the number of decision space dimensions. A new method called Particle Swarm Optimization for Variable Number of Dimensions is developed here. The well-known particle swarm optimization procedure is modified to handle spaces with a variable number of dimensions within a single run. Some well-known benchmark problems are modified to depend on the number of dimensions. Novel performance metrics are defined in the article to evaluate convergence properties of the method. Some recommendations for setting the optimization are made according to results of the method on the proposed benchmark test suite. The method is compared with conventional swarm strategies able to solve problems with variable number of dimensions.
Domain-Specific Patterns of Activity Support Metacognition in Human Prefrontal Cortex

Metacognition is the capacity to evaluate the success of one’s own cognitive processes in various domains, e.g. memory and perception. It remains controversial whether metacognition relies on a domain-general resource that is applied to different tasks, or whether self-evaluative processes are domain-specific. Here we directly investigated this issue by examining the neural substrates engaged when metacognitive judgments were made during perceptual and memory tasks matched for stimulus and performance characteristics. By comparing patterns of functional magnetic resonance imaging (fMRI) activity while subjects evaluated their performance, we revealed both domain-specific and domain-general metacognitive representations. Multi-voxel activity patterns in right frontopolar cortex predicted levels of confidence in a domain-specific fashion, whereas domain-general signals predicting confidence and accuracy were found in a widespread network in the frontal and posterior midline. Moreover, individual differences in perceptual and memory metacognitive ability were predicted by the degree of confidence decoding in frontal and parietal cortex, respectively. The demonstration of domain-specific metacognitive representations suggests the presence of a content-rich mechanism available to introspection and cognitive control.
Structured Joint Sparse Principal Component Analysis for Fault Detection and Isolation

In order to improve the performance of fault isolation and diagnosis of principal component analysis (PCA) based methods, this article proposes a novel fault detection and isolation approach using the structured joint sparse PCA (SJSPCA). The objective function involves two regularization terms: the <inline-formula><tex-math notation="LaTeX">$l_{2,1}$</tex-math></inline-formula> norm and the graph Laplacian. By imposing the <inline-formula><tex-math notation="LaTeX">$l_{2,1}$</tex-math></inline-formula> norm, SJSPCA is able to achieve row-wise sparsity, and introducing the graph Laplacian term can incorporate structured variable correlation information. The row-sparsity property of <inline-formula><tex-math notation="LaTeX">$l_{2,1}$</tex-math></inline-formula> norm ensures that the score indices associated with normal variables approaching zero and the graph Laplacian constraint helps the isolation of correlated faulty variables. Once a fault is detected, a two-stage fault-isolation strategy is considered and a score index is calculated for each variable. It is proved that the proposed two-stage strategy is capable of isolating faulty variables. The improved fault-isolation performance of SJSPCA is illustrated by a simulation example and a gas flow fault observed in an industrial blast furnace iron-making process.
Medical Condition : Towards Entity-specific Rankings of Knowledge Base Properties [ Extended Version ] ?

In knowledge bases such as Wikidata, it is possible to assert a large set of properties for entities, ranging from generic ones such as name and place of birth to highly profession-specific or background-specific ones such as doctoral advisor or medical condition. Determining a preference or ranking in this large set is a challenge in tasks such as prioritisation of edits or natural-language generation. Most previous approaches to ranking knowledge base properties are purely data-driven, that is, as we show, mistake frequency for interestingness. In this work, we have developed a human-annotated dataset of 350 preference judgments among pairs of knowledge base properties for fixed entities. From this set, we isolate a subset of pairs for which humans show a high level of agreement (87.5% on average). We show, however, that baseline and state-of-the-art techniques achieve only 61.3% precision in predicting human preferences for this subset. We then analyze what contributes to one property being rated as more important than another one, and identify that at least three factors play a role, namely (i) general frequency, (ii) applicability to similar entities and (iii) semantic similarity between property and entity. We experimentally analyze the contribution of each factor and show that a combination of techniques addressing all the three factors achieves 74% precision on the task. The dataset is available at www.kaggle. com/srazniewski/wikidatapropertyranking.
Scholarship at UWindsor Scholarship at UWindsor Why did you really do it? Examining the distinction between kinds Why did you really do it? Examining the distinction between kinds of reasons of reasons

: Studies in cognitive psychology have shown that many of our actions seem to be often influenced by irrelevant features of the environment, of which we are not aware. But exactly what reasons has the psychological research uncovered? In philosophy, a distinction has been made between normative, motivating, and explanatory reasons. Hence it is necessary to determine which of them have been revealed as the real reasons for our actions by the psychological research.