Miracle-dz/UniFAR-FTUs huggingface dataset:
Applied Harmonic Analysis, Massive Data Sets, Machine Learning, and Signal Processing

Advances in technology and the ever-growing role of digital sensors and computers in science have led to an exponential growth in the amount and complexity of data we collect. Uncertainty, scale, non-stationarity, noise, and heterogeneity are fundamental issues impeding progress at all phases of the pipeline that creates knowledge from data. This means that the amount of new mathematical challenges arising from the need of data analysis and information processing is enormous, with their solution requiring fundamentally new ideas and approaches, with significant consequences in the practical applications. The analysis of massive, high-dimensional, noisy, time-varying data sets has become a critical issue for a large number of scientists and engineers. Massive data sets have their own architecture. Each data source has an inherent structure, which we should attempt to detect in order to utilize it for applications, such as denoising, clustering, anomaly detection, knowledge extraction, recovery, etc. Harmonic analysis revolves around creating new structures for decomposition, rearrangement and reconstruction of operators and functions—in other words inventing and exploring new architectures for information and inference. Indeed, in the last three decades Applied Harmonic Analysis has been at the center of many significant new ideas and methods crucial in a wide range of signal and image processing applications, and in the analysis and processing of large data sets. For example, compressive sensing, sparse approximations and models, geometric multiscale analysis and diffusion geometry represent some quite recent important breakthroughs [4, 8, 6, 21, 1]. In particular the novel paradigm of sparsity and sparse approximations has had a tremendous impact on various areas in applied mathematics such as imaging sciences. It states that functions and signals which come from applications typically exhibit the property of admitting a representation in a suitable orthonormal basis or—more often due to the advantageous property of redundancy—a frame with very few non-zero coefficients or, in a weakened version, an approximation by such an expansion. Suitable representation systems were and are still being developed in the areas of applied harmonic analysis such as wavelets or curvelets. Although compression schemes such as JPEG2000 might be considered the first breakthrough of this general approach, quite recently, the new area of compressive sensing revealed another applications with tremendous impact. Roughly speaking, it showed that signals exhibiting a sparse approximation can be recovered efficiently from what would previously have been considered highly incomplete measurements. This discovery has led to a canon of fundamentally new approaches for various previously considered almost insolvable problems, for instance, for signal and image recovery problems. Compressive sensing also paves
The Los Humeros and Acoculco Geothermal Resources in the Trans-Mexican Volcanic Belt: Magnetotelluric Phase Tensor Analysis and its Significance for Tectonic Interpretation

The Acoculco and Los Humeros geothermal systems in the eastern sector of the Trans-Mexican Volcanic Belt are investigated for future exploitation and expansion, respectively. Electromagnetic methods are used to investigate the subsurface resistivity distribution in order to determine worthwhile future drilling targets. Magnetotelluric data are interpreted by using phase tensor analysis in order to derive the orientation of conductive features. For the Los Humeros geothermal system the caldera crossing NW-SE aligned Maxtayola fault can be identified by a clear change in phase tensor ellipses at high frequency ranges. At lower frequencies, the homogenous distribution of strongly elliptical phase tensors points at a strong conductivity contrast at greater depth. For the geothermal system at Acoculco the electromagnetic data indicate a partitioned subsurface resistivity distribution. Phase tensor ellipses on both sides of a NW-SE lineament, located east of
A Convection Nowcasting Method Based on Machine Learning

In this study, a convection nowcasting method based on machine learning was proposed. First, the historical data were back-calculated using the pyramid optical flow method. Next, the generated optical flow field information of each pixel and the Red-Green-Blue (RGB) image information were input into the Convolutional Long Short-Term Memory (ConvLSTM) algorithm for training purposes. During the extrapolation process, dynamic characteristics such as the rotation, convergence, and divergence in the optical flow field were also used as predictors to form an optimal nowcasting model. The test analysis demonstrated that the algorithm combined the image feature extraction ability of the convolutional neural network (CNN) and the sequential learning ability of the long short-term memory network (LSTM) model to establish an end-to-end deep learning network, which could deeply extract high-order features of radar echoes such as structural texture, spatial correlation, and temporal evolution compared with the traditional algorithm. Based on learning through the above features, this algorithm can forecast the generation and dissipation trends of convective cells to some extent. The addition of the optical flow information can more accurately simulate nonlinear trends such as the rotation, or merging, or separation of radar echoes. The trajectories of radar echoes obtained through nowcasting are closer to their actual movements, which prolongs the valid forecasting period and improves forecast accuracy.
Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic and Signal Conditions

Author(s): Hao, Peng; Wei, Zhensong; Bai, Zhengwei; Barth, Matthew J. | Abstract: The Eco-Approach and Departure (EAD) application has been proved to be environmentally efficient for a Connected and Automated Vehicles (CAVs) system. In the real-world traffic, traffic conditions and signal timings are usually dynamic and uncertain due to mixed vehicle types, various driving behaviors and limited sensing range, which is challenging in EAD development. This research proposes an adaptive strategy for connected eco-driving towards a signalized intersection under real world conditions. Stochastic graph models are built to link the vehicle and external (e.g., traffic, signal) data and dynamic programing is applied to identify the optimal speed for each vehicle-state efficiently. From energy perspective, adaptive strategy using traffic data could double the effective sensor range in eco-driving. A hybrid reinforcement learning framework is also developed for EAD in mixed traffic condition using both short-term benefit and long-term benefit as the action reward. Micro-simulation is conducted in Unity to validate the method, showing over 20% energy saving.View the NCST Project Webpage
Sparse Representation for Different Animal Vertebra Classification along the Fixation Trajectory of Pedicle Screw

Pedicle screw (PS) implantation is an ideal method for the treatment of severe multilevel vertebral instability. The key problem is the accuracy of PS fixation. In this paper, the spectrum of different tissues along the fixation trajectory of PS is studied to tackle the accuracy problem. Fresh porcine vertebrae, bovine vertebrae, and ovine vertebrae were measured by using the near-infrared spectrum (NIRs) device to obtain the reflected spectrum from these vertebrae. Along the fixation trajectory of PS, the classification method based on the sparse representation-based classifier (SRC) was applied to different vertebral tissues (cortical bones and cancellous bones). Considering the large amount of spectral data, sparse preserving projection (SPP) was applied to improve the performance of SRC. The proposed method based on the SPP method for dimensionality reduction and the SRC method for tissue recognition was first used in vertebrae classification and showed superior performance compared with other classification methods, such as SVM and 1NN. The results gained from this project are vital significant to the development of hi-tech medical instruments with independent intellectual property rights.
Models of random regular graphs

This is a survey of results on properties of random regular graphs, together with an exposition of some of the main methods of obtaining these results. Related results on asymptotic enumeration are also presented, as well as various generalisations to random graphs with given degree sequence. A major feature in this area is the small subgraph conditioning method. When applicable, this establishes a relationship between random regular graphs with uniform distribution, and non-uniform models of random regular graphs in which the probability of a graph G is weighted according to the number of subgraphs G has of a certain type. Information can be obtained in this way on the probability of existence of various types of spanning subgraphs, such as Hamilton cycles and decompositions into perfect matchings. Uniformly distributed labelled random regular graphs receive most of the attention, but also included are several non-uniform models which come about in a natural way. Some of these appear as spin-offs from the small subgraph conditioning method, and some arise from algorithms which use simple approaches to generating random regular graphs. A quite separate role played by algorithms is in the derivation of random graph properties by analysing the performance of an appropriate greedy algorithm on a random regular graph. Many open problems and conjectures are given.
Dose Response Relationship from Four-Way Cross Over Trials

This paper is an application of randomization test for clinical, four ways cross over, trials. The response variable was the proportion of nights with hypoglycaemic episode i.e., lowering the concentration of sugar in the blood. The hypothetical data has been used to examine how persuasively the probability of an episode depends on doses. We also observed how the power, of nonparametric randomization test, was affected when data possessed missing observations with varying sample sizes at 5% level of significance. One consequence in case of missing observations was the reduction of the power of the test, due to the reduction in the actual sample size. As a remedial mean imputation approach used, dose wise and found better power results. rank correction used, in order to examine the association between dose and response. In clinical trials it is common to have missing data. A remedial for missing data is imputation in this paper missing observations imputed through mean imputation approach. Design of the Paper/Methodology In clinical trials, the interest sometime lies in the effectiveness of a new drug as compared to placebo. In this study one dose is placebo and three different doses of active treatments. Our interest is if the probability of getting an episode=1 is less given the active doses as compared to placebo? The following situation, about dose response relationship, has been used in this paper. Doses are effective in higher order of magnitude i.e., dose 4 is the most effective dose as compare to rest of three doses. Dose 3 is better than dose 2 and placebo while, dose 2 is better than placebo. So, appropriate alternative hypothesis can be: Subject Day Dose Episode 1 1 1 1 1 2 1 1 1 3 1 1 1 1 2 1 1 2 2 1 1 3 2 0 1 1 3 0 1 2 3 1 1 3 3 1 1 1 4 0 1 2 4 0 1 3 4 1 . Table 1: Hypothetical data for one subject. Citation: Naqvi IB, Bring J (2019) Dose Response Relationship from Four-Way Cross Over Trials. J Biom Biostat 10: 428.
Data supplement for Scheele et al., Treatment-Resistant Depression and Ketamine Response in a Patient With Bilateral Amygdala Damagey. Am J Psychiatry (doi: 10.1176/appi.ajp.2019.18101219)

The study was approved by the local ethics committee of the Medical Faculty of the University of Bonn, Germany. All participants gave written informed consent and the study was conducted in accordance with the latest revision of the Helsinki Declaration. The 12 control patients with major depressive disorder (MDD) received inpatient treatment at the University Hospital Bonn and were treated according to current guidelines. The treatment included psychotherapy and antidepressant medication. All MDD patients were scanned within three days after admission (mean age ± SD = 42.75 ± 13.23 years; mean MADRS score ± SD = 28.00 ± 6.61). Furthermore, to examine possible structural changes in the MDD patients, structural images of 16 healthy women (mean age ± SD = 39.88 ± 11.79 years; mean BDI score ± SD = 1.31 ± 1.53) were used that were collected on the same MRI system.
Neurological Principles and Rehabilitation of Action Disorders: Rehabilitation Interventions

This third paper discusses the evidence for the rehabilitation of the most common movement disorders of the upper extremity. We also present a framework, building on the CAP model, for incorporating some of the principles discussed in the two previous papers by Frey et al. and Sathian et al. in the practice of rehabilitation, and for discussing potentially helpful interventions based on emergent neuroscience principles.
Can the guarantee instrument fight poverty?

The role of access to finance for economic development has received extensive attention recently, which has brought the issue to the top of the policy agenda. The growing attention has resulted in several public policy initiatives to encourage access to financial services, especially in low-income countries where credit constraints are severe. The guarantee instrument has the potential of mobilizing finance, lowering borrowing costs and addressing market failures, why PASS with support from Sida has issued a guarantee that targets smallholder farmers in the agricultural sector in Tanzania. The linkages between access to finance, productivity and welfare are supported by abundant evidence in the previous literature, but few studies have examined the productivity and welfare effects of increased access to finance in the context of the guarantee instrument. This study aims to fill the research gap and evaluate the effects of the PASS guarantee by analysing the productivity and welfare effects of increased access to finance for paddy farmers in the Morogoro region in Tanzania. The study is based on 86 structured interviews and the data is analysed by frequency statistics and cross-sectional regressions estimated with OLS. The results show that farmers provided with a formal bank loan have higher productivity than non-borrowers and that those who use the business plan in their operations are more productive. This highlights the importance of running the business according to the suggestions in the business plan. Furthermore, financial access has a positive and significant effect on household welfare, meaning that households provided with a loan attain a higher welfare level. The results confirm that the guarantee has positive effects on both productivity and welfare, why it can be considered as an effective tool for poverty reduction. Given the results of the study, we suggest that effort to promote financial access should be encouraged by both local governments, international development agencies and NGOs. Furthermore, PASS is recommended to encourage the farmers to use the business plan and provide necessary opportunities to facilitate the use. As the most common reason for not using the business plan is the language barrier, we recommend PASS to offer translation services to increase the productivity of the farmers as well as the probability of repayment. In addition, the results indicate low financial literacy among the farmers, why emphasis to reinforce the understanding of the loan conditions should be prioritised. Finally, the results show that formal education has a significant effect on household welfare, but when considering productivity, formal education is not significant on any level. However, knowledge can be assumed to affect productivity positively, why strategies to increase both formal and informal education, such as business training, should be considered.
Modeling Exclusion with a Differentiable Factor Graph Constraint

With the adoption of general neural network architectures, many researchers have opted to trade informative priors for powerful models and big data. However, for many structured prediction tasks the complex relationships between variables in the output space are often difficult to learn from the available data alone. Such relationships often centre around the notion of exclusion: that predicting one structure prohibits the prediction of others. In this work we formulate a differentiable factor graph exclusion constraint to incorporate this prior belief into neural end-to-end architectures. We demonstrate the effectiveness of this method in the context of extracting event information from clusters of related news articles, and introduce metainference learning to determine the ideal number of inference iterations to simulate.
Ensuring Fast Crash Recovery for Secure NVMs

Emerging NVM technologies (e.g., Phase Change Memory) are closing the gap between fast and volatile DRAM and slow but non-volatile storage technologies (e.g. Flash drive, SSDs). The near-zero idle power, scalability, and other desirable features of NVMs are driving the vendors to introduce NVM based main memories to the market (e.g. 3DXPoint [1]). Adopting such technology in the memory hierarchy requires careful extension or modification in the memory management architecture while taking persistency, crash consistency, wear leveling, reliability, and security into account [2]–[4]. While the non-volatile characteristics of the NVM memory cells facilitate persistent applications and fast accessible filesystem (e.g., DAX filesystem), they make the system more vulnerable to adversarial attacks. For instance, data remanence in NVM cells invite a wide range of attacks, demanding the encrypted presence of the stored data. Apart from encryption, secure architectures also aim to provides protection against data tampering and replay attacks [4]. State-of-the-art NVM security models make use of the Counter Mode Encryption for confidentiality and the Merkle Tree for integrity protection [2]. Although the encryption counters and tree nodes are stored in the NVM memory, they are cached in fast volatile cache inside the processor chip. While caching security metadata provides outstanding performance improvements and reduces NVM writes, it makes the system crash inconsistent as it loses the updates made in the volatile cache if a system crash happens. Osiris [5], a state-of-the-art, solves the crash consistency problem by re-purposing the ECC to recover the counters. However, it takes a long time to recover as the entire memory (can be terabytes in emerging NVM) needs to be scanned for possible corrections. Moreover, the SGX-style counter tree structure does not allow for recovery even with the cost of high recovery time. Anubis [6]1, addressing the problem, tries to reduce the recovery time for all integrity tree structures from hours to sub-seconds in a performance-friendly way.
The Risk of Alar Necrosis Associated with Dermal Filler Injection

BACKGROUND Injection of dermal fillers is one of the most commonly performed cosmetic procedures. Serious complications from fillers are rare but potentially devastating to patients and physicians. Skin necrosis, such as nasal alar necrosis, is one of the most feared serious complications of dermal fillers, but there is a paucity of literature on the incidence of such events, as well as potential treatment options.
Algebraic solutions of German out-of-field elementary school teachers

In Germany out-of-field elementary school teachers are a frequent phenomenon but not regarded as unproblematic. In order to foster the pupils’ algebraic thinking, teachers themselves need to be aware of the structures. Additionally, tasks which foster process-related competences too can only be set when the teacher knows which discoveries can be made by using a specific task. In this article theoretical background is given to process-related competences, teacher training and out-of-field teaching at German primary schools. A teacher training course is presented and first insights into the algebraic solutions of the participating teachers are given.
The Effect of Engagement on Job Satisfaction of the Employees of PT Asuransi Bangun Askrida Medan Branch

Job satisfaction is a person's feeling about the jobs and aspects contained in it, such as aspects of promotion, coworkers, salary, the job itself and also relationships with superiors. The subject of employee engagement and job satisfaction have been thoroughly researched, especially in the current times, where the companies try to hire and keep their best personnel satisfied and engaged, in order to have them more productive. This study aims to examine the effect of employee engagement on job satisfaction of PT Askrida Branch Medan employee. Questionnaire with 5-point Likert scale was used to measure employee engagement and job satisfaction of 32 respondents. Multiple linear regression was used to analyze the effect of the variables. The results showed that simultaneously employee engagement consisting of variables Equity, Achevement dan Camaraderie have positive and significant effect to employee job satisfaction. Variables Camaraderie is partially positive and significant effect to employee job satisfaction.
Equilibrium dynamics of entry and exit: industry-wide learning and endogenous heterogeneity

Endogenous heterogeneity induced by the market explains important empirical regularities on industry dynamics. In a deterministic dynamic competitive industry where cost reducing investment generates industry-wide learning, we show that the market may create incentives for differences in entry and exit decisions and in age, size and performance of ex ante identical firms. Under verifiable conditions, entry occurs with delay, entry continues indefinitely, shake out of firms occurs and shake out continues indefinitely. Older firms are larger and less likely to exit (last in first out); exiting firms are younger and smaller. Market structure converges in the long run. JEL: L11,D41,D92,O3
Research on Cooperative Control of the Hydraulic System of Multiple Intelligent Vehicles Combined Transportation

The multi-vehicle combined transportation of large-scale equipment or goods is studied, and various combined transportation modes are obtained. The research on four-vehicle combined transportation is studied, the four transport vehicles must ensure synchronization in the process of running, and the steering must be coordinated, otherwise major accidents may occur. Aiming at the stability control of multi-vehicle running synchronization, the system transfer function of pump-controlled motor in driving system is established, and the PID control is added. The simulation results show that adding the PID control algorithm can improve the speed stability of the transport vehicle. And the geometric model of the steering mechanism is established, the functional relationship between the steering angle and the stroke of the steering cylinder is obtained, and the relationship between the electric signal of proportional valve and the steering angle is deduced. On this basis, the coordinated control system of four-vehicle running synchronization and steering coordination based on CAN (controller area network) bus is designed. The master-slave synchronization control strategy and the PID control are applied to the four-vehicle combined transportation. According to the data collected from the test, it is proved that the control strategy fully meets the transportation requirements, and can provide theoretical basis and design method reference for the safe and reliable combined transportation of various types of transport vehicles.