Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Patch-Based Conditional Generative Adversarial Neural Net Models with Emphasis on Model Robustness for Use in Head and Neck Cases for MR-Only planning

A total of twenty paired CT and MR images were used in this study to investigate two conditional generative adversarial networks, Pix2Pix, and Cycle GAN, for generating synthetic CT images for Headand Neck cancer cases. Ten of the patient cases were used for training and included such common artifacts as dental implants; the remaining ten testing cases were used for testing and included a larger range of image features commonly found in clinical head and neck cases. These features included strong metal artifacts from dental implants, one case with a metal implant, and one case with abnormal anatomy. The original CT images were deformably registered to the mDixon FFE MR images to minimize the effects of processing the MR images. The sCT generation accuracy and robustness were evaluated using Mean Absolute Error (MAE) based on the Hounsfield Units (HU) for three regions (whole body, bone, and air within the body), Mean Error (ME) to observe systematic average offset errors in the sCT generation, and dosimetric evaluation of all clinically relevant structures. For the test set the MAE for the Pix2Pix and Cycle GAN models were 92.4 $\pm$ 13.5 HU, and 100.7 $\pm$ 14.6 HU, respectively, for the body region, 166.3 $\pm$ 31.8 HU, and 184 $\pm$ 31.9 HU, respectively, for the bone region, and 183.7 $\pm$ 41.3 HU and 185.4 $\pm$ 37.9 HU for the air regions. The ME for Pix2Pix and Cycle GAN were 21.0 $\pm$ 11.8 HU and 37.5 $\pm$ 14.9 HU, respectively. Absolute Percent Mean/Max Dose Errors were less than 2% for the PTV and all critical structures for both models, and DRRs generated from these models looked qualitatively similar to CT generated DRRs showing these methods are promising for MR-only planning.
Joint User Selection, Power Allocation, and Precoding Design With Imperfect CSIT for Multi-Cell MU-MIMO Downlink Systems

In this paper, a new optimization framework is presented for the joint design of user selection, power allocation, and precoding in multi-cell multi-user multiple-input multiple-output (MU-MIMO) systems when imperfect channel state information at transmitter (CSIT) is available. By representing the joint optimization variables in a higher-dimensional space, the weighted sum-spectral efficiency maximization is formulated as the maximization of the product of Rayleigh quotients. Although this is still a non-convex problem, a computationally efficient algorithm, referred to as generalized power iteration precoding (GPIP), is proposed. The algorithm converges to a stationary point (local maximum) of the objective function and therefore it guarantees the first-order optimality of the solution. By adjusting the weights in the weighted sum-spectral efficiency, the GPIP yields a joint solution for user selection, power allocation, and downlink precoding. The GPIP can be extended to the multi-cell scenario where cooperative base stations perform joint user-cell selection and design their precodes by taking into account the inter-cell interference by sharing global imperfect CSIT. System-level simulations show the gains of the proposed approach with respect to conventional user selection and linear downlink precoding.
An Integrated Neuronal Model of Claustral Function in Timing the Synchrony Between Cortical Areas

It has been suggested that the function of the claustrum (CL) may be to orchestrate and integrate the activity of the different cortical areas that are involved in a particular function by boosting the synchronized oscillations that occur between these areas. We propose here a model of how this may be done, thanks to the unique synaptic morphology of the CL and its excitatory and inhibitory connections with most cortical areas. Using serial visual search as an example, we describe how the functional anatomy of the claustral connections can potentially execute the sequential activation of the representations of objects that are being processed serially. We also propose that cross-frequency coupling (CFC) between low frequency signals from CL and higher frequency oscillations in the cortical areas will be an efficient means of CL modulating neural activity across multiple brain regions in synchrony. This model is applicable to the wide range of functions one performs, from simple object recognition to reading and writing, listening to or performing music, etc.
A General Framework for Fair Regression

Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness constraints into kernel regression methods, applicable to Gaussian processes, support vector machines, neural network regression and decision tree regression. Further, we focus on examining the effect of incorporating these constraints in decision tree regression, with direct applications to random forests and boosted trees amongst other widespread popular inference techniques. We show that the order of complexity of memory and computation is preserved for such models and tightly binds the expected perturbations to the model in terms of the number of leaves of the trees. Importantly, the approach works on trained models and hence can be easily applied to models in current use and group labels are only required on training data.
Targeted Next Generation Sequencing Revealed a Novel Homozygous Loss-of-Function Mutation in ILDR1 Gene Causes Autosomal Recessive Nonsyndromic Sensorineural Hearing Loss in a Chinese Family

Hereditary hearing impairment is one of the major and common birth defects in Chinese population. Non-syndromic sensorineural hearing loss (NSHL) is the most common types of hereditary hearing impairment. Genotypically and phenotypically NSHL is extremely heterogenous and follow either autosomal dominant or autosomal recessive or X-linked mode of inheritance. Presently, 127 genes have been identified to be associated with both syndromic and (NSHL). Here, we studied a Chinese family with moderate and profound hearing impairment. The proband is a 30-year old Chinese man. The proband was born with normal hearing and at the age of 5-years, the proband was first noticed with hearing impairment. Gradually and progressively the proband was presented with loss of hearing in his both right and left ears at the age of 30 years. The clinical symptoms, age of onset or progression to loss of hearing was similar in both the proband and his younger brother. The proband’s parents are phenotypically normal and non-consanguineous. Clinical diagnosis of the proband and his younger brother has been done by classical pure tone audiogram (PTA). Computed Tomography (CT) found no abnormality in bilateral external ear, middle ear and inner ear. Targeted next generation sequencing was performed with a panel of 127 genes reported to be associated with hereditary hearing impairment. A novel homozygous single nucleotide deletion (c.427delT) in exon 4 of ILDR1 gene has been identified in proband and in his younger brother. Sanger sequencing confirmed that proband’s father and mother are carrying this mutation in a heterozygous manner. This mutation has not been identified in 100 normal healthy control individuals. This mutation (c.427delT) causes frameshift (p.Tyr143Ilefs∗19) which leads to the formation of a truncated ILDR1 protein of 162 amino acids instead of the wild type ILDR1 protein of 546 amino acids. ILDR1 associated hereditary hearing impairment is very rare and this is the first report of identifying a loss-of-function mutation in ILDR1 gene associated with hereditary hearing impairment in Chinese population. Our present study also emphasized the significance of rapid, accurate and cost-effective screening for the patient with hereditary hearing impairment by targeted next generation sequencing.
Uniform-in-Time Weak Error Analysis for Stochastic Gradient Descent Algorithms via Diffusion Approximation

Diffusion approximation provides weak approximation for stochastic gradient descent algorithms in a finite time horizon. In this paper, we introduce new tools motivated by the backward error analysis of numerical stochastic differential equations into the theoretical framework of diffusion approximation, extending the validity of the weak approximation from finite to infinite time horizon. The new techniques developed in this paper enable us to characterize the asymptotic behavior of constant-step-size SGD algorithms for strongly convex objective functions, a goal previously unreachable within the diffusion approximation framework. Our analysis builds upon a truncated formal power expansion of the solution of a stochastic modified equation arising from diffusion approximation, where the main technical ingredient is a uniform-in-time weak error bound controlling the long-term behavior of the expansion coefficient functions near the global minimum. We expect these new techniques to greatly expand the range of applicability of diffusion approximation to cover wider and deeper aspects of stochastic optimization algorithms in data science.
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning

In this paper we consider the problem of how a reinforcement learning agent that is tasked with solving a sequence of reinforcement learning problems (a sequence of Markov decision processes) can use knowledge acquired early in its lifetime to improve its ability to solve new problems. We argue that previous experience with similar problems can provide an agent with information about how it should explore when facing a new but related problem. We show that the search for an optimal exploration strategy can be formulated as a reinforcement learning problem itself and demonstrate that such strategy can leverage patterns found in the structure of related problems. We conclude with experiments that show the benefits of optimizing an exploration strategy using our proposed approach.
Efficient Folded SC Polar Line Decoder

This paper aims at designing a hardware efficient architecture for successive cancellation (SC) polar decoder. Existing literatures have shown that SC polar decoder can be implemented with $\mathcal{O}(M)$ processing elements and $\mathcal{O}(M)$ memory elements, where $M$ is the code length. In this paper, a folding scheme based on line architecture of successive cancellation is proposed to achieve such hardware complexity. Given $2^{m}$-bit code and $2^{n}$ nodes, we can conveniently achieve an efficient decoder implementation with the proposed design approach. Also a tractable mapping relationship between node activation and clock index is proposed. To validate the efficiency of the proposed approach, line architecture SC decoder with $m= 3$ and $n= 1$ and line architecture SC decoder with $m = 3$ and $n = 2$ is implemented with Altera Stratix V FPGA.
Research on Defect Detection of Castings Based on Deep Residual Network

In this study, we proposed a method for detecting the appearance defect of castings based on deep residual network, which is used to solve the problems of low accuracy, difficult application conditions and insufficient robustness of traditional defect detection methods. This method divides the casting into multiple regions, preprocesses the image of each region, and then inputs the processed image into the convolutional neural network to extract the features, and finally determines whether the sample has defects. The deep residual network ResNet-34 was chosen as the network model, and its activation function was improved. The ASoftReL U function was proposed to alleviate the neuron-death problem and improve the accuracy and fitting speed of the network. Finally, the improved defect detection system was tested on the data set of castings. Through the comparison and analysis of the experimental results, the network model with the highest accuracy and the most generalization ability was obtained. Experimental results show that the accuracy of this method is much higher than the traditional method.
Off-Grid DOA Estimation in Mutual Coupling via Robust Sparse Bayesian Learning

The most of existing off-grid direction of arrival (DOA) estimation methods are based on the perfect array-manifold. However, in practice, it is often hard to obtain a perfect array-manifold. In this paper, to achieve the DOA estimation under mutual coupling condition with low computational complexity, we propose a robust root Sparse Bayesian Learning (SBL) method. In the proposed method, firstly, we adopt the banded complex symmetric Toeplitz structure of the mutual coupling matrix to remove the negative influence of mutual coupling on DOA estimation. Then the DOA with off-grid is estimated by formulating the root-SBL strategy. Compared with the existing SBL-based algorithms, our method can not only maintain superior DOA estimation performance under the condition of mutual coupling, especially with strong mutual coupling, but also have lower computational complexity. Simulation results demonstrate that the proposed method can still accurately estimate DOAs under strong mutual coupling conditions, while other SBL-based methods fail to work.
Applying of Genetic Algorithm in Clustering Methods for Communication Networks Planning

a combined algorithm for automated finding the optimal configuration of cluster distribution is presented. Clusters are considered as idealized subnets of a multi-level computer network, which can have different methods of data transferring. The use of genetic algorithm is considered as a method for finding the best solution in a multidimensional problem with a large number of local minimums. In assessing the optimality, the distance from the center of the cluster to the place of its location brought into line to the expenses of its operation. The combined algorithm was software-implemented and was analyzed by results of solving a particular problem of transferring data.
Engineering Big Data to Small Businesses: Lessons Learned from a Case Study

Big Data has become an important technical force to advance many industries. Many big data techniques have been invented and open-sourced for public use. The small businesses, which form the major part of the whole business world, still face great challenges in applying big data solutions to their own businesses. However, there are few cases that have been published to report the procedure of engineering big data in small businesses. This fact leads to insufficient references for small businesses to take to figure out their own scenarios of applying big data. In this paper, we report a pilot case study on a small business applying big data to an electric signal process project. It describes in detail the procedure of analyzing the business logics, identifying big data requirements, and selecting appropriate big data techniques for engineering solutions. We also share lessons learned from this case study, which can be a general reference for those small businesses on trying their own big data application cases.
Torque Ripple Reduction in Double-Inverter Fed Wound Rotor Induction Machine Drives Using PWM Techniques

This paper deals with torque ripple estimation and reduction in a vector controlled double-inverter-fed wound rotor induction machine (DI-WRIM) drive by employing different pulsewidth modulation (PWM) techniques applied to stator and rotor side voltage source inverters (VSIs). The influence of PWM switching sequences on torque ripple is studied in terms of stator and rotor flux ripples. A novel rotor speed independent transformation has been proposed to visualize the nature of stator and rotor flux ripples from a common frame of reference. The expression for instantaneous torque ripple is formulated and a PWM combination is proposed resulting in lower root mean square (rms) and peak–peak torque ripple. The estimated instantaneous, rms, and peak–peak torque ripple is verified through simulation and experimental results from a 3-kW vector controlled DI-WRIM drive.
Metaphor Detection: Leveraging Culturally Grounded Eventive Information

Metaphors are compact packages of information with rich cultural background information. As one of the most powerful linguistic forms with non-literal meaning, metaphor detection in natural language processing can be both challenging and rewarding. We propose an innovative method for metaphor detection and classification leveraging culturally grounded eventive information. This culturally grounded information is organized based on ontological structure, which in turn facilitates further semantic processing of the result of our classification. As a culturally bound ontological system, the Chinese writing system has basic concepts organized according to semantic radicals, which are symbols containing rich eventive information that represent categorical concepts. This paper illustrates the basic design principles of applying ontological structures in metaphor detection by taking into account radicals representing body parts, instruments, materials, and movements. Our approach to leverage the eventive information of the Chinese writing system in metaphor detection is based on the fact that such information is available as an integral part of the writing system of any text. We hypothesize that eventive information can be accessed through the “embodied” source domain information represented by the radicals without syntactic processing or annotation. In terms of the theory of metaphor, we further hypothesize that eventive types in the embodied source domain maps to, and hence can help to predict, eventive meaning in the target domain of metaphor. Our studies show that the event information encoded in lexical items can facilitate classification of metaphoric events and identification of metaphors in Chinese texts effectively. We achieved improvements in Chinese metaphor detection over state-of-the-art approaches in our first classification experiment, and our proposed approach is shown to be generalizable in a second experiment involving new sets of characters with the same radicals.
Synchronization Control in the Cable Space for Cable-Driven Parallel Robots

Cable-driven parallel robots (CDPRs) have low moving inertia and provide high-speed motion over a large workspace. The main challenges of CDPRs stem from the fact that cables should be in tension during motion control. Cable tension is closely related to the synchronization motion relation between cables which is often omitted in the existing controllers for CDPRs. To solve it, this paper proposes two synchronization controllers in the cable space to realize the synchronization motion between cables, and finally increase the tracking accuracy of the moving platform. The two synchronization controllers are proven to guarantee asymptotic convergence to zero of both tracking error and synchronization error. The trajectory tracking experiments are implemented on a self-built three-DOF CDPR, and the experiments are compared with the traditional augmented proportional-derivative controllers neglecting synchronization. The experimental results indicate that the tracking error and the synchronization error of the moving platform decrease greatly by using the synchronization controllers.
SIMULATION OF BIPARTITE OR DIRECTED GRAPHS WITH PRESCRIBED DEGREE SEQUENCES USING MAXIMUM ENTROPY PROBABILITIES

We propose an algorithm for simulating bipartite or directed graphs with given degree sequences, motivated by the study of financial networks with partial information. Our algorithm sequentially computes certain "maximum entropy" matrices, and uses the entries of these matrices to assign probabilities to edges between nodes. We prove the correctness of the algorithm, showing that it always returns a valid graph and that it generates all valid graphs with positive probability. We illustrate the algorithm in an example of an inter-bank network.
Statistics-Aided Codebook Designs For Limited Feedback Beamforming Systems Over Millimeterwave Channels

In this paper, we consider statistics-aided beamformer code-book designs for millimeter wave (mmWave) channels with limited feedback. The dimension of the beamformer, which scales with the number of transmit antenna, is typically large in mmWave systems and a large dimension adds to the difficulty of codebook designs. Two low-cost design methods are proposed. The first one, a VQ based approach, reduces the dimensionality of the codebook design problem by using training AoDs (angles of departure) instead of training channels. The second one, requiring no codebook training, designs the beamformer codebook from the statistics of the channel alone based on spectral analysis of the channel. Simulations are given to demonstrate the usefulness of the proposed methods.
Resilience Evaluation for Approximating SystemC Designs Using Machine Learning Techniques

As digital circuits have become more complicated than ever, abstract description languages such as SystemC have been introduced, allowing designers to work on more abstract levels during the design process. Design metrics such as performance and energy consumption are a central concern for designers at all levels of abstraction. Approximate computing is a promising way to optimize these criteria, sacrificing accuracy. Defining which parts of a design can be approximated (and to what degree) is a crucial and non-trivial design decision, which is usually connected to a larger programming effort, especially when exploring the design space manually. In this paper, we propose an automated approach based on machine learning techniques in order to detect the resilience of a given SystemC design's modules. This is used to identify components of the design that can be approximated. The effectiveness of the proposed method is evaluated using several SystemC benchmarks from various domains.
Improved Segmentation Technique Based on Graph Theory

A method based on graph theory is proposed, which adjusts the parameters by using a method of chi square with Gauss weighted distance and adaptive parameter adjustment of shared neighbor weighted, in this paper. The pixels are arranged with the vector form of local neighborhood information, considering the different effects of each pixel in the neighborhood of the central pixel, the weight should also be different, the Gauss function will be used chi square distance combinations, to avoid over segmentation or under segmentation. And by integrating the nearest neighbor weighted adaptive method, each pixel is automatically given a scale parameter to reduce the need to adjust the parameters. Gauss weighted local neighborhood information is introduced in this paper, to construct the similarity matrix directly on the original image.