Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel Training: An ACP-Based Training Framework for Iterative Learning in Uncertain Driving Spaces

The traffic environment and driving behaviors are of great complexity and uncertainty in our physical world. Therefore, training in the digital world with low cost and diverse complexities become popular for autonomous driving in recent years. However, the current training methods tend to be limited to static data sets and deterministic models that do not sufficiently take into account the uncertainty and diversity prevalent in real traffic scenarios. These approaches also limit more possibilities for the comprehensive development and optimization of vision systems. In this paper, we develop a parallel training method based on artificial systems, computational experiments, and parallel execution (ACP) for the intelligent optimization and learning of the aforementioned agents in uncertain driving spaces. Parallel training creates a virtual driving space following the instruction of the ACP approach and conducts large-scale rehearsal experiments for possible scenarios. By enhancing the diversity of virtual scenarios, intelligent vehicles are trained to respond and adapt to the diverse uncertainties in the physical real-world driving space. Specifically, parallel training first proposes a standard operating procedure for intelligent driving systems, namely the projection-emergence-convergence-operation (PECO) loop. Digital quadruplets for parallel training, i.e., physical, descriptive, predictive, and prescriptive coaches, are also proposed. With the guidance of parallel training, virtual and real-world driving spaces are set up in parallel and interact frequently. They are closely linked and unified in opposition to each other, ultimately building a parallel driving system that fulfills safety, security, sustainability, sensitivity, service, and smartness (6S).
Gauge invariant perturbations of black holes. II. Kerr space-time

We cast the perturbed Bianchi identities into a form involving only tetrad and coordinate-gauge invariant field quantities. From the perturbed Bianchi identities we then derive a system of gauge invariant wavelike gravitational perturbation equations. These are the Teukolsky equation, a gauge invariant analog of the Regge–Wheeler equation, and a new gravitational perturbation equation. The Bianchi identities also provide the transformations between the perturbation equations. In particular, the transformation between the Teukolsky equation and the analog of the Regge–Wheeler equation arises in this way. This work extends our previous analysis of the perturbations of the Schwarzschild black hole to the Kerr case.
Agent-Based Approach for (Peri-)Urban Inter-Modality Policies: Application to Real Data from the Lille Metropolis

Transportation authorities have adopted more and more incentive measures (fare-free public transport, construction of park-and-ride facilities, etc.) to reduce the use of private cars by combining them with public transit. However, such measures remain difficult to assess with traditional transport models. This article proposes a different approach: an agent-oriented model. To reproduce realistic applications in an urban context (a metropolis), we investigate the preferences and choices of different agents based on utilities and focus on a modal choice performed through a multinomial logit model. Moreover, we propose some methodological elements to identify the individuals’ profiles using public data (census and travel surveys). We also show that this model, applied in a real case study (Lille, France), is able to reproduce travel behaviors when combining private cars and public transport. Moreover, we focus on the role played by park-and-ride facilities in this context. Thus, the simulation framework makes it possible to better understand individuals’ intermodal travel behavior and assess its development policies.
Design of three-class ultra-efficient stray light suppression baffle

The ultra-efficient standard light baffle plays an important role in the calibration of the stray light test system. In this paper, a three-class baffle is designed. And a design scheme of the baffle with honeycombed wall is proposed, by analyzing the stray light transmission model and suppression mechanism, which can meet the requirements of a baffle with ultra-efficient stray light suppression capability. A simulation model of the baffle was established. The simulation of baffle show that when the stray light suppression angle is greater than 8°, the PST value of the baffle reaches 10-6 magnitude. When the stray light suppression angle is greater than 17°, the PST value of the baffle reaches 10-12 magnitude, which can meet the needs of ultra-efficient calibration of laboratory stray light test systems.
The experimental wave resistance of an accelerating two-dimensional pressure distribution

A series of experiments designed to measure the wave resistance of an accelerating two-dimensional air-cushion vehicle is described. A model was towed at a constant acceleration from rest over water of various depths. In addition, the effects of different levels of acceleration and different cushion pressures were examined. The results are compared with linearized potential-flow theory and show particularly encouraging agreement. The predicted humps and hollows in the curve of wave resistance vs. time are verified, but with a small shift with respect to time. Also, the theoretical region of negative wave resistance in water of finite depth is demonstrated.
Convergence of 3D Bioprinting and Nanotechnology in Tissue Engineering Scaffolds

Three-dimensional (3D) bioprinting has emerged as a promising scaffold fabrication strategy for tissue engineering with excellent control over scaffold geometry and microstructure. Nanobiomaterials as bioinks play a key role in manipulating the cellular microenvironment to alter its growth and development. This review first introduces the commonly used nanomaterials in tissue engineering scaffolds, including natural polymers, synthetic polymers, and polymer derivatives, and reveals the improvement of nanomaterials on scaffold performance. Second, the 3D bioprinting technologies of inkjet-based bioprinting, extrusion-based bioprinting, laser-assisted bioprinting, and stereolithography bioprinting are comprehensively itemized, and the advantages and underlying mechanisms are revealed. Then the convergence of 3D bioprinting and nanotechnology applications in tissue engineering scaffolds, such as bone, nerve, blood vessel, tendon, and internal organs, are discussed. Finally, the challenges and perspectives of convergence of 3D bioprinting and nanotechnology are proposed. This review will provide scientific guidance to develop 3D bioprinting tissue engineering scaffolds by nanotechnology.
On the Quasi-Heredity of Birman–Wenzl Algebras☆

Abstract In this paper we consider the Birman–Wenzl algebras over an arbitrary field and prove that they are cellular in the sense of Graham and Lehrer. Furthermore, we determine for which parameters the Birman–Wenzl algebras are quasi-hereditary. So the general theory of cellular algebras and quasi-hereditary algebras applies to Birman–Wenzl algebras. As a consequence, we can determine all irreducible representations of the Birman–Wenzl algebras by linear algebra methods. We prove also that the new Hecke algebras induced from Birman–Wenzl algebras are Frobenius over a field (but not always cellular).
ATR scheme based on 1-D HRR profiles

A new automatic target recognition (ATR) scheme based on one-dimensional high range resolution profiles is presented, which features joint range alignment and classification. This method can also be used to form mean- or eigen-templates to further improve the ATR performance. Experimental results are provided to demonstrate the performance of the new algorithm.
Knowing when to discontinue Temozolomide therapy in responding aggressive pituitary tumors and carcinomas: a systematic review and Padua (Italy) case series

ABSTRACT Introduction Pituitary adenomas can show a tendency to grow, despite multimodal treatment. Temozolomide (TMZ) has been used in the last 15 years in patients with aggressive pituitary tumors. TMZ requires a careful balance of different expertise, especially for selection criteria. Areas covered We conducted: (1) a systematic review of the published literature from 2006 to 2022, collecting only cases with a complete description of patient follow-up after TMZ discontinuation; (2) a description of all patients with aggressive pituitary adenoma or carcinoma treated in Padua (Italy). Expert opinion There is considerable heterogeneity in the literature: TMZ cycles duration ranged from 3 to 47 months; the follow-up time after TMZ discontinuation ranged from 4 to 91 months (mean 24 months, median 18 months), at least a stable disease has been reported in 75% of patients after a mean 13 months (range 3–47 months, median 10 months). The Padua (Italy) cohort reflects the literature. Future directions to explore are to understand the pathophysiological mechanism of TMZ resistance escape, to develop predicting factors to TMZ treatment (especially through the delineation of the underlying transformation processes), and to further expand the therapeutic applications of TMZ (as neoadjuvant, combined with radiotherapy).
Wood Recognition Based on Histogram Matching Algorithms

The influences that different histogram matching algorithms have on identifying the softwood are research in this paper. The experiment partitions images according to three different ways of early wood-late wood transition, early wood, and randomization, and extracts the PCA(Principle Component Analysis) features of these wood images separately. Dividing Histogram matching algorithm into two categories of, bin-to-bin and cross-bin, experiments are conducted on wood recognition by four different histogram matching algorithms of the Euclidean, Chi-Square, EMD and Quadratic-Chi. The experimental results indicate that the histogram matching algorithms of cross-bin have the good effects on wood recognition, especially the quadratic-Chi distance put forward recently.
A 280 GHz (x8) Frequency Multiplier Chain in 250 nm InP HBT Technology

We report a 280 GHz x8 frequency multiplier chain in 250 nm InP HBT technology. Emitter coupled pair in push-push configuration act as a balanced frequency doubler. The x8 frequency multiplier chain consists of three cascaded frequency doublers. Passive transformers are used to generate single-ended to differential conversion. At 280 GHz, the multiplier chain generates -0.6 dBm output power with 3-dB bandwidth of 48 GHz with input power of -2 dBm at 35 GHz. The unwanted harmonics are suppressed by more than 28 dBc over the frequency range of interest. It consumes 112 mW of DC power and occupies 0.4 mm2 of the chip area. To the authors’ knowledge, these results demonstrate record spectral purity for H-band frequency multipliers working around 280 GHz with low DC power consumption.
Twenty Novel MicroRNAs in the Aqueous Humor of Pseudoexfoliation Glaucoma Patients

The microRNAs (miRNAs) are short non-coding RNAs (19–25 nt) that regulate the level of gene expression at the post-transcriptional stage. Altered miRNAs expression can lead to the development of various diseases, e.g., pseudoexfoliation glaucoma (PEXG). In this study, we assessed the levels of miRNA expression in the aqueous humor of PEXG patients using the expression microarray method. Twenty new miRNA molecules have been selected as having the potential to be associated with the development or progression of PEXG. Ten miRNAs were downregulated in PEXG (hsa-miR-95-5p, hsa-miR-515-3p, hsa-mir-802, hsa-miR-1205, hsa-miR-3660, hsa-mir-3683, hsa -mir-3936, hsa-miR-4774-5p, hsa-miR-6509-3p, hsa-miR-7843-3p) and ten miRNAs were upregulated in PEXG (hsa-miR-202 -3p, hsa-miR-3622a-3p, hsa-mir-4329, hsa-miR-4524a-3p, hsa-miR-4655-5p, hsa-mir-6071, hsa-mir-6723-5p, hsa-miR-6847-5p, hsa-miR-8074, and hsa-miR-8083). Functional analysis and enrichment analysis showed that the mechanisms that can be regulated by these miRNAs are: extracellular matrix (ECM) imbalance, cell apoptosis (possibly retinal ganglion cells (RGCs)), autophagy, and elevated calcium cation levels. Nevertheless, the exact molecular basis of PEXG is unknown and further research is required on this topic.
Intracellular Nitric Oxide and cAMP Are Involved in Cellulolytic Enzyme Production in Neurospora crassa

Although molecular regulation of cellulolytic enzyme production in filamentous fungi has been actively explored, the underlying signaling processes in fungal cells are still not clearly understood. In this study, the molecular signaling mechanism regulating cellulase production in Neurospora crassa was investigated. We found that the transcription and extracellular cellulolytic activity of four cellulolytic enzymes (cbh1, gh6-2, gh5-1, and gh3-4) increased in Avicel (microcrystalline cellulose) medium. Intracellular nitric oxide (NO) and reactive oxygen species (ROS) detected by fluorescent dyes were observed in larger areas of fungal hyphae grown in Avicel medium compared to those grown in glucose medium. The transcription of the four cellulolytic enzyme genes in fungal hyphae grown in Avicel medium was significantly decreased and increased after NO was intracellularly removed and extracellularly added, respectively. Furthermore, we found that the cyclic AMP (cAMP) level in fungal cells was significantly decreased after intracellular NO removal, and the addition of cAMP could enhance cellulolytic enzyme activity. Taken together, our data suggest that the increase in intracellular NO in response to cellulose in media may have promoted the transcription of cellulolytic enzymes and participated in the elevation of intracellular cAMP, eventually leading to improved extracellular cellulolytic enzyme activity.
QED between parallel mirrors: Light signals faster than c, or amplified by the vacuum

Because it is scattered by the zero-point oscillations of the quantized fields, light of frequency omega travelling normally to two parallel mirrors experiences the vacuum between them as a dispersive medium with refractive index n( omega ). An earlier low-frequency result that n(0)<1 is combined with the Kramers-Kronig dispersion relation for n and with the classic Sommerfeld-Brillouin argument to show (under certain physically reasonable assumptions) that either n( infinity )<1, in which case the signal velocity c/n( infinity ) exceeds c; or that the imaginary part of n is negative at least for some ranges of frequency, in which case the vacuum between the mirrors fails to respond to a light probe like a normal passive medium. Further, the optical theorem suggests that n exhibits no dispersion to order e4, i.e. that n( infinity )=n(0) up to corrections of order e6 at most.
Role of Oligodendrocyte Lineage Cells in Multiple System Atrophy

Multiple system atrophy (MSA) is a debilitating movement disorder with unknown etiology. Patients present characteristic parkinsonism and/or cerebellar dysfunction in the clinical phase, resulting from progressive deterioration in the nigrostriatal and olivopontocerebellar regions. MSA patients have a prodromal phase subsequent to the insidious onset of neuropathology. Therefore, understanding the early pathological events is important in determining the pathogenesis, which will assist with developing disease-modifying therapy. Although the definite diagnosis of MSA relies on the positive post-mortem finding of oligodendroglial inclusions composed of α-synuclein, only recently has MSA been verified as an oligodendrogliopathy with secondary neuronal degeneration. We review up-to-date knowledge of human oligodendrocyte lineage cells and their association with α-synuclein, and discuss the postulated mechanisms of how oligodendrogliopathy develops, oligodendrocyte progenitor cells as the potential origins of the toxic seeds of α-synuclein, and the possible networks through which oligodendrogliopathy induces neuronal loss. Our insights will shed new light on the research directions for future MSA studies.
A Simple Structure Orthomode Transducer for Millimeter-Wave Applications

– This article presents the design of an orthomode transducer (OMT) with a simple structure for ease of fabrication at the 230 GHz band. The structure consists of a square waveguide T junction with transitions to rectangular waveguides for the V-and H-polarized ports. The design is facilitated by a numerical simulation tool based on the ﬁnite element method. This OMT features the simplest possible conﬁguration amenable for standard computer numerical control machining for operation at the J band (consistent with WR-03 waveguide standard). The fabricated and measured OMT exhibits an operational bandwidth of 12%, over which greater than 10 dB return loss and greater than 30 dB cross-polarization isolation are achieved. The insertion loss is minimized to an average less than 2 dB using time-extended electroless gold plating to improve the surface conductivity and ﬁll the waveguides’ seams. This OMT is integrated within a fully polarimetric radar operating at 220 GHz to 230 GHz.
A Semicircular Substrate Integrated Waveguide- Based Self-Diplexing Slot Antenna With Polarization Flexibility

The substrate integrated waveguide (SIW) based self-diplexing antenna having polarization flexibility is presented in this letter for satellite communications. The presented self-diplexing antenna can provide the linear polarization, right-hand circular polarization, and left-hand circular polarization at the two closely spaced frequencies with the respective port. The antenna is realized on a semicircular SIW cavity, which is further modified to form the two dominant mode quadrant cavities by incorporating a via wall at the center of the semicircular cavity. The different polarizations are achieved by changing the orientation of the radiating slots. The linear polarization is achieved by loading the curved slots in a back-to-back manner and circular polarization in a crossed manner. The resonant frequencies can be tuned with the help of the length of the slots. The equivalent circuit model of the proposed antenna is developed and the equivalent parameters are extracted. The testing of two fabricated antenna prototypes with different polarizations is presented here.
Update on Lean Body Mass Diagnostic Assessment in Critical Illness

Acute critical illnesses can alter vital functions with profound biological, biochemical, metabolic, and functional modifications. Despite etiology, patient’s nutritional status is pivotal to guide metabolic support. The assessment of nutritional status remains complex and not completely elucidated. Loss of lean body mass is a clear marker of malnutrition; however, the question of how to investigate it still remains unanswered. Several tools have been implemented to measure lean body mass, including a computed tomography scan, ultrasound, and bioelectrical impedance analysis, although such methods unfortunately require validation. A lack of uniform bedside measurement tools could impact the nutrition outcome. Metabolic assessment, nutritional status, and nutritional risk have a pivotal role in critical care. Therefore, knowledge about the methods used to assess lean body mass in critical illnesses is increasingly required. The aim of the present review is to update the scientific evidence regarding lean body mass diagnostic assessment in critical illness to provide the diagnostic key points for metabolic and nutritional support.
MSC-CSMC: A multi-objective semi-supervised clustering algorithm based on constraints selection and multi-source constraints for gene expression data

Many clustering techniques have been proposed to group genes based on gene expression data. Among these methods, semi-supervised clustering techniques aim to improve clustering performance by incorporating supervisory information in the form of pairwise constraints. However, noisy constraints inevitably exist in the constraint set obtained on the practical unlabeled dataset, which degenerates the performance of semi-supervised clustering. Moreover, multiple information sources are not integrated into multi-source constraints to improve clustering quality. To this end, the research proposes a new multi-objective semi-supervised clustering algorithm based on constraints selection and multi-source constraints (MSC-CSMC) for unlabeled gene expression data. The proposed method first uses the gene expression data and the gene ontology (GO) that describes gene annotation information to form multi-source constraints. Then, the multi-source constraints are applied to the clustering by improving the constraint violation penalty weight in the semi-supervised clustering objective function. Furthermore, the constraints selection and cluster prototypes are put into the multi-objective evolutionary framework by adopting a mixed chromosome encoding strategy, which can select pairwise constraints suitable for clustering tasks through synergistic optimization to reduce the negative influence of noisy constraints. The proposed MSC-CSMC algorithm is testified using five benchmark gene expression datasets, and the results show that the proposed algorithm achieves superior performance.
Development and Analysis of a CNN- and Transfer-Learning-Based Classification Model for Automated Dairy Cow Feeding Behavior Recognition from Accelerometer Data

Due to technological developments, wearable sensors for monitoring the behavior of farm animals have become cheaper, have a longer lifespan and are more accessible for small farms and researchers. In addition, advancements in deep machine learning methods provide new opportunities for behavior recognition. However, the combination of the new electronics and algorithms are rarely used in PLF, and their possibilities and limitations are not well-studied. In this study, a CNN-based model for the feeding behavior classification of dairy cows was trained, and the training process was analyzed considering a training dataset and the use of transfer learning. Commercial acceleration measuring tags, which were connected by BLE, were fitted to cow collars in a research barn. Based on a dataset including 33.7 cow × days (21 cows recorded during 1–3 days) of labeled data and an additional free-access dataset with similar acceleration data, a classifier with F1 = 93.9% was developed. The optimal classification window size was 90 s. In addition, the influence of the training dataset size on the classifier accuracy was analyzed for different neural networks using the transfer learning technique. While the size of the training dataset was being increased, the rate of the accuracy improvement decreased. Beginning from a specific point, the use of additional training data can be impractical. A relatively high accuracy was achieved with few training data when the classifier was trained using randomly initialized model weights, and a higher accuracy was achieved when transfer learning was used. These findings can be used for the estimation of the necessary dataset size for training neural network classifiers intended for other environments and conditions.