Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Complexity Model Predictive Control of AC/DC Converter With Constant Switching Frequency

Conventional finite-control-set model predictive control (FCS-MPC) and low-complexity MPC (LC-MPC) may cause variable switching frequency and high sampling frequency. To address these concerns, a new LC-MPC scheme with a constant switching frequency has been proposed in this paper, the virtual voltage vector generated by two adjacent effective vectors applied to decrease the power control error. The scheme has been tested for power control of an AC/DC converter. Simulation and experimental results obtained have shown that the scheme generates smaller (near zero) power control error per switching period, and have a faster dynamic response and a constant switching frequency. These features have been attained without demanding for an excessively high sampling frequency. Finally, the comparison with the conventional methods is carried out in this paper, the advantages of the proposed method have been clearly illustrated.
C1222C Deletion in Exon 8 of ABL1 Is Involved in Carcinogenesis and Cell Cycle Control of Colorectal Cancer Through IRS1/PI3K/Akt Pathway

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. ABL1 (c-Abl) is a non-receptor tyrosine kinase, whose role, and molecular mechanism in CRC remain largely unclear. The aim of this study was to elucidate the role of ABL1 to obtain information on colon cancer gene mutation. We analyzed the tissue samples obtained from patients with CRC, CRC cell lines, and the immunodeficient mice. The proliferation, cell cycle, and apoptosis of CRC cells were examined. IPA software was used to analyze the molecules involved in CRC after ABL1 RNA interference. We found ABL1 was highly expressed in CRC tissues and cells. This high expression was associated with the TNM stage of CRC patients. In exon 8 of the ABL1 gene, we identified a novel mutation of C1222C deletion, which was related to the CRC stage. Depletion of ABL1 resulted in the inhibition of proliferation and escalation of apoptosis in two CRC cell lines, SW480, and HCT-116. Our in vivo study also demonstrated that depletion of ABL1 reduced CRC tumor progression. The results of the ingenuity pathway analysis indicated that the expression of 732 genes was upregulated and that of 691 genes was downregulated in mice transplanted with ABL1-downregulated CRC cells, among which we confirmed that depletion of ABL1 inhibited TGF-β1 via IRS1/PI3K/AKT pathway in CRC progression. These findings demonstrated that ABL1 plays an important role and that it can be a potential molecular target for CRC therapy.
Hidden population modes in social brain morphology: Its parts are more than its sum

The complexity of social interactions is a defining property of the human species. Many social neuroscience experiments have sought to map ‘perspective taking’, ‘empathy’, and other canonical psychological constructs to distinguishable brain circuits. This predominant research paradigm was seldom complemented by bottom-up studies of the unknown sources of variation that add up to measures of social brain structure; perhaps due to a lack of large population datasets. We aimed at a systematic de-construction of social brain morphology into its elementary building blocks in the UK Biobank cohort (n=~10,000). Coherent patterns of structural co-variation were explored within a recent atlas of social brain locations, enabled through translating autoencoder algorithms from deep learning. The artificial neural networks learned rich subnetwork representations that became apparent from social brain variation at population scale. The learned subnetworks carried essential information about the co-dependence configurations between social brain regions, with the nucleus accumbens, medial prefrontal cortex, and temporoparietal junction embedded at the core. Some of the uncovered subnetworks contributed to predicting examined social traits in general, while other subnetworks helped predict specific facets of social functioning, such as feelings of loneliness. Our population-level evidence indicates that hidden subsystems of the social brain underpin interindividual variation in dissociable aspects of social lifestyle.
Mindfulness-Based Restoration Skills Training (ReST) in a Natural Setting Compared to Conventional Mindfulness Training: Psychological Functioning After a Five-Week Course

Restoration skills training (ReST) is a mindfulness-based course that draws on restorative nature experience to facilitate the meditation practice and teach widely applicable adaptation skills. Previous studies comparing ReST to conventional mindfulness training (CMT) showed that ReST has important advantages: it supports beginning meditators in connecting with restorative environmental qualities and in meditating with less effort; it restores their attention regulation capabilities; and it helps them complete the course and establish a regular meditation habit. However, mindfulness theory indicates that effortful training may be necessary to achieve generalized improvements in psychological functioning. Therefore, this study tests whether the less effortful and more acceptable ReST approach is attended by any meaningful disadvantage compared to CMT in terms of its effects on central aspects psychological functioning. We analyze data from four rounds of development of the ReST course, in each of which we compared it to a parallel and formally matched CMT course. Randomly assigned participants (total course starters = 152) provided ratings of dispositional mindfulness, cognitive functioning, and chronic stress before and after the 5-week ReST and CMT courses. Round 4 also included a separately recruited passive control condition. ReST and CMT were attended by similar average improvements in the three outcomes, although the effects on chronic stress were inconsistent. Moderate to large improvements in the three outcomes could also be affirmed in contrasts with the passive controls. Using a reliable change index, we saw that over one third of the ReST and CMT participants enjoyed reliably improved psychological functioning. The risk of experiencing deteriorated functioning was no greater with either ReST or CMT than for passive control group participants. None of the contrasts exceeded our stringent criterion for inferiority of ReST compared with CMT. We conclude that ReST is a promising alternative for otherwise healthy people with stress or concentration problems who would be less likely to complete more effortful CMT. By adapting the meditation practices to draw on restorative setting characteristics, ReST can mitigate the demands otherwise incurred in early stages of mindfulness training without compromising the acquisition of widely applicable mindfulness skills.
Forecasting CDS Term Structure Based on Nelson-Siegel Model and Machine Learning

In this study, we analyze the term structure of credit default swaps (CDSs) and predict future term structures using the Nelson–Siegel model, recurrent neural network (RNN), support vector regression (SVR), long short-term memory (LSTM), and group method of data handling (GMDH) using CDS term structure data from 2008 to 2019. Furthermore, we evaluate the change in the forecasting performance of the models through a subperiod analysis. According to the empirical results, we confirm that the Nelson–Siegel model can be used to predict not only the interest rate term structure but also the CDS term structure. Additionally, we demonstrate that machine-learning models, namely, SVR, RNN, LSTM, and GMDH, outperform the model-driven methods (in this case, the Nelson–Siegel model). Among the machine learning approaches, GMDH demonstrates the best performance in forecasting the CDS term structure. According to the subperiod analysis, the performance of all models was inconsistent with the data period. All the models were less predictable in highly volatile data periods than in less volatile periods. This study will enable traders and policymakers to invest efficiently and make policy decisions based on the current and future risk factors of a company or country.
PAVED: Pareto Front Visualization for Engineering Design

Design problems in engineering typically involve a large solution space and several potentially conflicting criteria. Selecting a compromise solution is often supported by optimization algorithms that compute hundreds of Pareto‐optimal solutions, thus informing a decision by the engineer. However, the complexity of evaluating and comparing alternatives increases with the number of criteria that need to be considered at the same time. We present a design study on Pareto front visualization to support engineers in applying their expertise and subjective preferences for selection of the most‐preferred solution. We provide a characterization of data and tasks from the parametric design of electric motors. The requirements identified were the basis for our development of PAVED, an interactive parallel coordinates visualization for exploration of multi‐criteria alternatives. We reflect on our user‐centered design process that included iterative refinement with real data in close collaboration with a domain expert as well as a summative evaluation in the field. The results suggest a high usability of our visualization as part of a real‐world engineering design workflow. Our lessons learned can serve as guidance to future visualization developers targeting multi‐criteria optimization problems in engineering design or alternative domains.
Bovine in vitro embryo production and oocyte preservation

Several experiments were done with the overall objective to improve the production of in vitro embryos. In the first experiment, the optimum period for ovary removal and the effect of FSH priming at standing estrus were investigated to maximize viable oocyte yield from culled cows. Animals were ovariectomized either 2 d after induced estrus, 2 d after a single dose of FSH on the day of standing estrus or randomly irrespective of the stage of the estrous cycle. Oocyte recovery and, cleavage and blastocyst formation rates were recorded for each treatment. Highest oocyte recovery was obtained from FSH-primed cows. Cleavage and blastocyst formation rates were not significantly different among treatments. These results indicated that FSH treatment increases oocyte yield in culled cows. The second experiment examined methods for the short-term preservation of bovine oocytes. Oocytes were matured in either i) straws containing maturation media (MM) at 38.5 °C without 5% C02, ii) straws containing MM at room temperature without 5% C0 2 or iii) culture plates containing MM at 38.5 °C and 5% C02. After 24 h, oocytes were fertilized and cultured in vitro. Oocytes, which were matured at room temperature without 5% C02, showed significantly lower maturation, fertilization, cleavage, and blastocyst formation rates than those matured at 38.5 °C. Oocytes matured at 38.5 °C with or without 5% C0 2 showed similar maturation, fertilization and cleavage rates, however, those with 5% C0 2 showed a significantly higher blastocyst formation rate. Therefore, 38.5 °C and a 5% C0 2 environment are optimal for oocyte maturation and embryo development. The third experiment was done to find a suitable cryopreservation method for the long-term
Single-cell analyses of the corneal epithelium: Unique cell types and gene expression profiles

Corneal Epithelial Stem Cells (CESCs) and their proliferative progeny, the Transit Amplifying Cells (TACs), are responsible for homeostasis and maintaining corneal transparency. Owing to our limited knowledge of cell fates and gene activity within the cornea, the search for unique markers to identify and isolate these cells remains crucial for ocular surface reconstruction. We performed single-cell RNA sequencing of corneal epithelial cells from stage 49-51 Xenopus larvae. We identified five main clusters with distinct molecular signatures, which represent apical, basal and keratocyte cell types as well as two discrete proliferative cell types in the bi-layered epithelium. Our data reveal several novel genes expressed in corneal cells and spatiotemporal changes in gene expression during corneal differentiation. Through gene regulatory network analysis, we identified key developmental gene regulons, which guide these different cell states. Our study offers a detailed atlas of single-cell transcriptomes in the frog corneal epithelium. In future, this work will be useful to elucidate the function of novel genes in corneal homeostasis, wound healing and cornea regeneration, which includes lens regeneration in Xenopus. SUMMARY STATEMENT This study identifies cell types and transcriptional heterogeneity in the corneal epithelium that regulate its differentiation, and facilitates the search for corneal stem cell markers.
Functional Organization of Midget and Parasol Ganglion Cells in the Human Retina

The functional organization of diverse retinal ganglion cell (RGC) types, which shapes the visual signal transmitted to the brain, has been examined in many species. The unique spatial, temporal, and chromatic properties of the numerically dominant RGC types in macaque monkey retina are presumed to most accurately model human vision. However, the functional similarity between RGCs in macaques and humans has only begun to be tested, and recent work suggests possible differences. Here, the properties of the numerically dominant human RGC types were examined using large-scale multi-electrode recordings with fine-grained visual stimulation in isolated retina, and compared to results from dozens of recordings from macaque retina using the same experimental methods and conditions. The properties of four major human RGC types -- ON-parasol, OFF-parasol, ON-midget, and OFF-midget -- closely paralleled those of the same macaque RGC types, including the spatial and temporal light sensitivity, precisely coordinated mosaic organization of receptive fields, ON-OFF asymmetries, spatial response nonlinearity, and sampling of photoreceptor inputs over space. Putative smooth monostratified cells and polyaxonal amacrine cells were also identified based on similarities to cell types previously identified in macaque retina. The results suggest that recently proposed differences between human and macaque RGCs probably reflect experimental differences, and that the macaque model provides an accurate picture of human RGC function.
Practical Product Sampling by Fitting and Composing Warps

We introduce a Monte Carlo importance sampling method for integrands composed of products and show its application to rendering where direct sampling of the product is often difficult. Our method is based on warp functions that operate on the primary samples in [0,1)n, where each warp approximates sampling a single factor of the product distribution. Our key insight is that individual factors are often well‐behaved and inexpensive to fit and sample in primary sample space, which leads to a practical, efficient sampling algorithm. Our sampling approach is unbiased, easy to implement, and compatible with multiple importance sampling. We show the results of applying our warps to projected solid angle sampling of spherical triangles, to sampling bilinear patch light sources, and to sampling glossy BSDFs and area light sources, with efficiency improvements of over 1.6× on real‐world scenes.
Adaptive Platoon Control for Marine Surface Vehicles in the Presence of Constraints

This paper presents the platoon formation control design for unmanned surface vehicles with input and output constraints. First, Barrier Lyapunov Function (BLF) is employed to ensure the desired line-of-sight (LOS) range. The LOS range should be maintained within the predefined regions between each marine vehicle and its predecessor while the connected platoons track the trajectory. Next, to handle the model uncertainties and unknown external disturbance, we propose an adaptive neural network controller that approximates the unknown nonlinearities. Furthermore, the Nussbaum function is applied to compensate for input saturations. In addition, formation errors can be guaranteed by stability analysis. The distance between two consecutive agents does not violate collision avoidance and connectivity in the presence of input saturation. Finally, the feasibility of the theoretical results is illustrated through the simulation results.
Milling Tool Wear Prediction Method Based on Deep Learning Under Variable Working Conditions

Tool wear prediction is essential to ensure part quality and machining efficiency. Tool wear is affected by factors such as the material, structure, process, and processing time of the parts. Tool wear under the variable working conditions and the above factors show a complex coupling and timing correlation, which makes it challenging to predict tool wear under variable working conditions. This article aims to resolve this issue. First, we establish a unified representation of working condition factors. The stacked autoencoder (SAE) model adaptively extracts tool wear features from the machining signal. The extracted wear features and respective working conditions then combine into a working condition feature sequence for predicting tool wear. Finally, the advantages of the long short-term memory (LSTM) model to solve memory accumulation effects learn the regular wear pattern of the working condition feature sequence to realize the prediction of the tool wear. An experiment illustrates the effectiveness of the proposed method.
Packet Length Spectral Analysis for IoT Flow Classification Using Ensemble Learning

With the proliferation of ubiquitous and autonomous devices for sensing, control, monitoring and conditioning, the Internet of Things (IoT) holds a great potential for the development of innovative applications. At the same time, network operators must support these devices with differentiated services, which rely on the ability to automatically recognize and classify the nature of the communication flows. In this paper, we present a supervised learning approach to discriminate between IoT and non-IoT traffic, and to determine the class of the device originating the packet flows. We make use of a reduced set of features based on the spectral analysis of the packet lengths of a flow, and evaluate an ensemble learning algorithm that uses a Random Forest classifier. We first discuss the datasets and the procedure that we use to extract the features, with examples from different devices. The evaluation is performed using both 10-fold cross validation and a split between training, validation and test-set. The latter is used for hyperparameter tuning. The results show that for reasonably large datasets the classifier achieves very high accuracy, as well as Precision and Recall rates. We further improve the performance on individual devices by selectively replicating the flows in the dataset, to achieve a better balance. We then evaluate a real-time implementation, and propose a runtime procedure to evaluate the model confidence level and trigger a retraining phase to adapt to a changing environment. A detailed analysis of the performance shows that the algorithm can support networks up to 100 Gbps on standard computing platforms.
Practical Measurement and Reconstruction of Spectral Skin Reflectance

We present two practical methods for measurement of spectral skin reflectance suited for live subjects, and drive a spectral BSSRDF model with appropriate complexity to match skin appearance in photographs, including human faces. Our primary measurement method employs illuminating a subject with two complementary uniform spectral illumination conditions using a multispectral LED sphere to estimate spatially varying parameters of chromophore concentrations including melanin and hemoglobin concentration, melanin blend‐type fraction, and epidermal hemoglobin fraction. We demonstrate that our proposed complementary measurements enable higher‐quality estimate of chromophores than those obtained using standard broadband illumination, while being suitable for integration with multiview facial capture using regular color cameras. Besides novel optimal measurements under controlled illumination, we also demonstrate how to adapt practical skin patch measurements using a hand‐held dermatological skin measurement device, a Miravex Antera 3D camera, for skin appearance reconstruction and rendering. Furthermore, we introduce a novel approach for parameter estimation given the measurements using neural networks which is significantly faster than a lookup table search and avoids parameter quantization. We demonstrate high quality matches of skin appearance with photographs for a variety of skin types with our proposed practical measurement procedures, including photorealistic spectral reproduction and renderings of facial appearance.
Improving Learning to Rank By Leveraging User Dynamics and Continuation Methods

Learning to Rank (LtR) techniques leverage assessed samples of query-document relevance to learn ranking functions able to exploit the noisy signals hidden in the features used to represent queries and documents. In this paper, we explore how to enhance the state-of-the-art LambdaMart algorithm by integrating in the training process an explicit knowledge of the underlying user-interaction model and the possibility of targeting diﬀerent objective functions, that can eﬀectively drive the algorithm towards promising areas of the search space. We enrich the learning algorithm in two ways: (i) by considering complex query-based user dynamics instead than simply discounting the gain by the rank position; (ii) by designing a learning path across diﬀerent loss functions that can capture diﬀerent signals in the training data. Our extensive experiments, conducted on publicly available datasets, show that the proposed solution permits to improve various ranking quality measures by statistically signiﬁcant margins.
Real‐time Anticipation of Occlusions for Automated Camera Control in Toric Space

Efficient visibility computation is a prominent requirement when designing automated camera control techniques for dynamic 3D environments; computer games, interactive storytelling or 3D media applications all need to track 3D entities while ensuring their visibility and delivering a smooth cinematic experience. Addressing this problem requires to sample a large set of potential camera positions and estimate visibility for each of them, which in practice is intractable despite the efficiency of ray‐casting techniques on recent platforms. In this work, we introduce a novel GPU‐rendering technique to efficiently compute occlusions of tracked targets in Toric Space coordinates – a parametric space designed for cinematic camera control. We then rely on this occlusion evaluation to derive an anticipation map predicting occlusions for a continuous set of cameras over a user‐defined time window. We finally design a camera motion strategy exploiting this anticipation map to minimize the occlusions of tracked entities over time. The key features of our approach are demonstrated through comparison with traditionally used ray‐casting on benchmark scenes, and through an integration in multiple game‐like 3D scenes with heavy, sparse and dense occluders.