Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Gravity Gradient Inversion-Ant Colony Optimization Algorithm for Motion Planning of Mobile Robots

 Abstract — Motion planning is a common task required to be fulfilled by robots. A strategy combining Ant Colony Optimization (ACO) and gravity gradient inversion algorithm is proposed for motion planning of mobile robots. In this paper, in order to realize optimal motion planning strategy, the cost function in ACO is designed based on gravity gradient inversion algorithm. The obstacles around mobile robot can cause gravity gradient anomalies; the gradiometer is installed on the mobile robot to detect the gravity gradient anomalies. After obtaining the anomalies, gravity gradient inversion algorithm is employed to calculate relative distance and orientation between mobile robot and obstacles. The relative distance and orientation deduced from gravity gradient inversion algorithm is employed as cost function in ACO algorithm to realize motion planning. The proposed strategy is validated by the simulation and experiment results.
Simultaneous Full‐Color Printing and Holography Enabled by Centimeter‐Scale Plasmonic Metasurfaces

Optical metasurfaces enable novel ways to locally manipulate light's amplitude, phase, and polarization, underpinning a newly viable technology for applications, such as high‐density optical storage, holography, and displays. Here, a high‐security‐level platform enabled by centimeter‐scale plasmonic metasurfaces with full‐color, high‐purity, and enhanced‐information‐capacity properties is proposed. Multiple types of independent information can be embedded into a single metamark using full parameters of light, including amplitude, phase, and polarization. Under incoherent white light, the metamark appears as a polarization‐ and angle‐encoded full‐color image with flexibly controlled hue, saturation, and brightness, while switching to multiwavelength holograms under coherent laser illumination. More importantly, for actual applications, the extremely shallow functional layer makes such centimeter‐scale plasmonic metamarks suitable for cost‐effective mass production processes. Considering these superior performances of the presented multifunctional plasmonic metasurfaces, this work may find wide applications in anticounterfeiting, information security, high‐density optical storage, and so forth.
Development and plasticity of the mouse primary visual cortex

A strain difference was observed in juvenile OD plasticity between C57BL/6J and C57BL/6JOlaHsd mice whereby open eye 'homeostatic' potentiation was completely absent in the C57BL/6JOlaHsd strain. This was accompanied by an absence of dark exposure induced synaptic scaling as measured ex vivo. In contrast in adulthood both strains showed comparable open eye potentiation, suggesting a mechanistic difference between juvenile and adult plasticity. Preliminary data suggests that while juvenile open eye potentiation is homeostatic, in adulthood it may be more LTP like and dependent upon CaMKII autophosphorylation.
Determinants of Raw Milk Supply and Value Addition Participation in Mecha Woreda, Amhara National Regional State, Western Ethiopia

Dairy sector has important role in supporting the national economy and livelihood of milk producers. Milk producers are one of key actors that play great role along the dairy value chain in the study area. Milk producers’ participation in milk and milk products marketing played roles in improving household welfare, and also means improved food security and reduced poverty. Though there are studies conducted on milk production, processing and utilization, there is insufficient information on determinants of milk producers’milk supply and milk value addition participation in Mecha woreda. Thus, this study was conducted in Mecha woreda with objectives of analyzing determinants of smallholder milk producers’ participation decisions and intensity in cow milk supply and value addition. Formal survey techniques were employed to collect data. The data were collected from 200 milk producers using systematic random sampling techniques. Bivariate probit model was employed for participation decisions while recursive bivariate tobit model was used to analyze determinants of participation intensities. The econometric model result revealed that age of household head, number of local cows owned, distance of dairy products market, household milk consumption, family size, dairy farm experience, milk yield size per day, and membership in dairy cooperatives were influential variables that affected milk supply participation decision significantly. The household land size, milk consumption, distance of dairy market, milk yield per day, cooperative membership and milk market experience affected milk supply participation intensity significantly. Value addition participation decision was influenced by land size, distance of market and value addition experience positively and significantly. Distance of dairy product market, dairy farm experience, and membership in dairy cooperatives influenced value addition participation intensity significantly. In this study, improving market access by increasing number of dairy cooperatives, road and infrastructures, upgrading traditional milk products, introducing labor saving technologies, and empowering cooperatives and producers are recommended to increase smallholders’ participation in dairy products marketing. Keywords: Bivariate Tobit-Probit, milk supply participation, value addition participation DOI: 10.7176/JMCR/65-01 Publication date: February 29 th 2020
Trends in Raindrop Kinetic Energy with Modeled Climate Warming in the Lake Tahoe Basin

Two means by which climate change may increase surface soil erosion in mountainous terrain are: (1) increasing the frequency of extreme rainfall events and (2) decreasing the duration of snow cover on bare soil. We used output from four general circulation models (GCMs) and two greenhouse gas trajectories to produce a suite of hydrologic variables at a daily time‐step for historic and projected 21st Century conditions. We statistically disaggregated the daily rainfall to hourly, using hourly rainfall from a network of nine weather stations in the Tahoe Basin, and filtered out rain falling on a snowpack. We applied published equations to convert hourly intensity to raindrop kinetic energy (KE) for each day and grid cell in the Basin, averaged across grid cells, and created time series of total annual and maximum annual hourly kinetic energy (TKE and MKE) on snow‐free ground. Using the Generalized Extreme Value distribution, we calculated the significance of long‐term trends in KE on snow‐free ground, and estimated energy levels for return periods of 2, 20, and 100 years. We then detrended the snowpack data and compared the resulting trends in KE with the trends resulting from changes in both rainfall energy and snowpack under two GCMs. Principal findings include (1) upward trends in MKE, (2) stronger upward trends in TKE; and (3) an effect of increasing rainfall intensities on KE in some cases, and a strong effect of reduced snowpack in all cases examined.
Adolescent Employment and Postsecondary Education among Latino Children of Immigrants

ABSTRACT This study examined the connection between adolescent employment and future participation in postsecondary education in a sample of U.S. Latino high school juniors/seniors (n = 2164) from the 1992–2002 Children of Immigrants Longitudinal Study. Employed and non-employed Latino adolescents did not markedly differ in academic performance, ability, or expectations, suggesting that, for U.S. Latino adolescents, employment may depend on the degree of constraint imposed by social and economic statuses. Results of multilevel binomial logistic regression suggest plausible sources of educational disparities and reveal that high-intensity work during high school may be a source of educational disadvantage for Latino children.
New Molecular Markers in Acute Myeloid Leukemia

Silvia de la Iglesia Iñigo1, María Teresa Gómez Casares1, Carmen Elsa López Jorge1,2, Jezabel López Brito1 and Pedro Martin Cabrera3 1Servicio de Hematología, Hospital de Gran Canaria Doctor Negrín, Las Palmas de GranCanaria 2Unit Research, Hospital de Gran Canaria Doctor Negrín, Las Palmas de GranCanaria 3Secció de Citologia Hematològica, Servei d’Anatomia Patològica Hospital Universitari de Bellvitge, Barcelona, Spain
Design and Implementation of Smart Banking System

Swindles attacking the Automatic Teller Machine (ATM) in recent days have increased, now it has led us to the use of other technique of identifying a person with the help of the statistical analysis and the use of biological data of a person to identify him/her, the process which is used to identify and also to authenticate a person based on some of his perceptible and substantial data that are distinct and are in the form of precised data, which is also known as biometric that acquires high level security and exactness/accuracy in the banking system. The project describes a system which restores the use of ATM cards and PIN’s over the physiological method of biometric fingerprint and iris authentication. In addition to this the function of the One Time Password (OTP) discloses privacy to the person from recalling his/her PIN . The machine is programmed in such a way that it can differentiate between the actual reliable trait and forgery samples. A GSM module which is linked to the system will send a message that contains 6-digit code which is generated from the system which is accessible for limited period of time for the user which is sent to his/her registered mobile number. After the user enters the valid OTP the user can either deposit, withdraw money or can even view his/her account balance. In any kind/form of forgery admission to access the account will block the account. In the project preliminary effects are brought on the facts set of physiological factors i.e., fingerprint and iris data in real time with the use of fingerprint module niceties matching algorithm and a Graphical User Interface (GUI) based totally on Circular Hough Transform respectively.
A single machine scheduling problem with machine availability constraints and preventive maintenance

Considering the impact of machine availability is essential in production scheduling, especially when the target is to minimise total tardiness. In this study, a single machine scheduling problem derived from a rotor production workshop is addressed. We evaluate machine availability by its reliability, which can be restored by preventive maintenance (PM). PM with different improvement effectiveness is considered in the problem formulation. A genetic algorithm (GA) is developed to solve the problem. Emmons dominance rules are applied in the mutation processes of the GA. Computational experiments demonstrate the effectiveness and efficiency of the algorithm. Sensitivity analyses have been conducted to provide useful managerial insights for real workshop scheduling.
An Optimization Management Model for Countries with Mutually Competitive Regions

It is difficult to manage resources allocation and pollution in a multi-regional country with mutually competitive relations. This work therefore intends to propose an optimal control model for the management problem. Through introducing the union utility function to model competitive interactions, we construct an integrated economy, resource, and environment macroeconomic model according to the social practices in the country with some competitive regions. Numerical procedures are proposed to search for the optimal energy policy and antipollution strategy of the model. As shown, an optimal fair tradeoff between efficiency and equity can be balanced for each region in the country. More important, the model verifies the importance of the role of government for management problems in a country with complex internal competitive relations
The Leaders, the Laggers, and the “Vulnerables”

We examine the lead-lag effect between the large and the small capitalization financial institutions by constructing two global weekly rebalanced indices. We focus on the 10% of stocks that “survived” all the rebalancings by remaining constituents of the indices. We sort them according to their systemic importance using the marginal expected shortfall (MES), which measures the individual institutions’ vulnerability over the market, the network based MES, which captures the vulnerability of the risks generated by institutions’ interrelations, and the Bayesian network based MES, which takes into account different network structures among institutions’ interrelations. We also check if the lead-lag effect holds in terms of systemic risk implying systemic risk transmission from the large to the small capitalization, concluding a mixed behavior compared to the index returns. Additionally, we find that all the systemic risk indicators increase their magnitude during the financial crisis.
Sound Absorption of Sustainable Polymer Nanofibrous Thin Membranes Bonded to a Bulk Porous Material

In this paper, the standardized characterization of nanofibrous membranes used to coat three porous bulk acoustical materials (melamine foam, a polyester textile, and an MDF perforated panel) is presented. The membranes were manufactured from recyclable Polyamide 6 (PA6) and water-soluble polyvinyl alcohol (PVA) using the needleless electrospinning technique. This resulted in very thin membranes that had high porosity and very high airflow resistivity. The membranes were collected in a high-permeability nonwoven substrate. Measured results in both an impedance tube and a reverberation room showed significant improvements in the sound absorption performance of the bulk materials after incorporating the nanofibrous layer. The application of the membranes on the surface of a traditional air-backed perforated panel also improved the sound absorption, exhibiting a broad peak of sound absorption in the low-frequency range. This was particularly true when the membrane area weight was increased. It is concluded that these materials, manufactured as described in this paper, can be alternatives to glass, mineral, and ceramic fibrous materials, which have high carbon footprints.
Type I photosensitizers based on phosphindole oxide for photodynamic therapy: apoptosis and autophagy induced by endoplasmic reticulum stress

Photodynamic therapy (PDT) is considered a pioneering and effective modality for cancer treatment, but it is still facing challenges of hypoxic tumors. Recently, Type I PDT, as an effective strategy to address this issue, has drawn considerable attention. Few reports are available on the capability for Type I reactive oxygen species (ROS) generation of purely organic photosensitizers (PSs). Herein, we report two new Type I PSs, α-TPA-PIO and β-TPA-PIO, from phosphindole oxide-based isomers with efficient Type I ROS generation abilities. A detailed study on photophysical and photochemical mechanisms is conducted to shed light on the molecular design of PSs based on the Type I mechanism. The in vitro results demonstrate that these two PSs can selectively accumulate in a neutral lipid region, particularly in the endoplasmic reticulum (ER), of cells and efficiently induce ER-stress mediated apoptosis and autophagy in PDT. In vivo models indicate that β-TPA-PIO successfully achieves remarkable tumor ablation. The ROS-based ER stress triggered by β-TPA-PIO-mediated PDT has high potential as a precursor of the immunostimulatory effect for immunotherapy. This work presents a comprehensive protocol for Type I-based purely organic PSs and highlights the significance of considering the working mechanism in the design of PSs for the optimization of cancer treatment protocols.
Multi-Omic Signatures of Sarcoidosis and Progression in Bronchoalveolar Lavage Cells

Introduction Sarcoidosis is a heterogeneous, granulomatous disease that can prove difficult to diagnose, with no accurate biomarkers of disease progression. Therefore, we profiled and integrated the DNA methylome, mRNAs, and microRNAs to identify molecular changes associated with sarcoidosis and disease progression that might illuminate underlying mechanisms of disease and potential genomic biomarkers. Methods Bronchoalveolar lavage cells from 64 sarcoidosis subjects and 16 healthy controls were used. DNA methylation was profiled on Illumina HumanMethylationEPIC arrays, mRNA by RNA-sequencing, and miRNAs by small RNA-sequencing. Linear models were fit to test for effect of diagnosis and phenotype, adjusting for age, sex, and smoking. We built a supervised multi-omics model using a subset of features from each dataset. Results We identified 46,812 CpGs, 1,842 mRNAs, and 5 miRNAs associated with sarcoidosis versus controls and 1 mRNA, SEPP1 - a protein that supplies selenium to cells, associated with disease progression. Our integrated model emphasized the prominence of the PI3K/AKT1 pathway in sarcoidosis, which is important in T cell and mTOR function. Novel immune related genes and miRNAs including LYST, RGS14, SLFN12L, and hsa-miR-199b-5p, distinguished sarcoidosis from controls. Our integrated model also demonstrated differential expression/methylation of IL20RB, ABCC11, SFSWAP, AGBL4, miR-146a-3p, and miR-378b between non-progressive and progressive sarcoidosis. Conclusions Leveraging the DNA methylome, transcriptome, and miRNA-sequencing in sarcoidosis BAL cells, we detected widespread molecular changes associated with disease, many which are involved in immune response. These molecules may serve as diagnostic/prognostic biomarkers and/or drug targets, although future testing will be required for confirmation.
Automated Techniques for Creating Speech Corpora from Public Data Sources for ML Training

For machine learning (ML) to work well, there is a need for large amounts of good quality training data. Obtaining such data is often the key bottleneck for the entire ML development process. Using humans to do explicit collection has been the main approach, but this tends to be expensive and time-consuming. Therefore, there is significant interest in creating alternative data collection techniques. We explore these alternative data collection techniques in the context of speech data in this paper. We were initially motivated by the problem of wake word engine training, where we need a large number of utterances for specific wake words. Given that there are already large public repositories of media data (e.g., YouTube, DailyMotion), we were curious as to how feasible it is to find the utterances that we need. Our results are encouraging as we found many different types of words can readily be found and downloaded in the quantity and quality needed to create training corpora for DL training. Usually > 30% of the found words are suitable for corpus creation. Greater than 80% of the top 10,000 ranks words and > 50% of the top 20,000 words we selected easily produced > 5000 found words, which is sufficient to train a high quality Wake Word Engine. Besides general words, we specifically looked for words used in wake word engine construction such as Name/Place/Product Name. Here, again, we find most common names/places/products return more than a sufficient number of words for corpus creation. Only uncommon names and places (like Atticus or Maximus) are difficult to find in sufficient quantities for corpus creation. We demonstrate a wake word engine trained from words we found in YouTube has the equivalent performance to one trained with traditional human collected words. Even though we were focused on wake words, our approach is general. It can be applied to create speech corpus for various purposes.
macrosyntR : Drawing automatically ordered Oxford Grids from standard genomic files in R

Macrosynteny refers to the conservation of chromosomal to sub-chromosomal domains across species and its conservation can provide insight on the evolution of animal genomes. Pairwise comparison of de-novo assembled genomes based on predicted protein sequences often use a graphical visualization called an Oxford grid. We implemented an R package to draw Oxford grids from standard genomic file formats. The package can automatically order the chromosomes, to improve interpretability, and is thus helpful for both exploratory data analysis and production of publication quality graphics.
Multiclass Classification of SRBCT Tumors

The Problem: There currently exists no single biological or chemical test that can precisely distinguish small, round blue cell tumors of childhood (SRBCTs) into their subclasses, which include neuroblastoma (NB), rhabdomyosarcoma (RMS), non-Hodgkin lymphoma (NHL) and the Ewing family of tumors (EWS) [2]. A recent paper by Khan et al. demonstrated that using gene expression profiles obtained from cDNA microarrays of samples which included both tumor tissue as well as cell lines, artificial neural networks (ANNs) can accurately distinguish the tumor subtypes using 96 top genes obtained from Principal Component Analysis. In order to identify candidate targets for therapy, it is important to find a smaller subset of genes and yet retain high classification accuracy.
PanoraMIS: An ultra-wide field of view image dataset for vision-based robot-motion estimation

This article presents a new dataset of ultra-wide field of view images with accurate ground truth, called PanoraMIS. The dataset covers a large spectrum of panoramic cameras (catadioptric, twin-fisheye), robotic platforms (wheeled, aerial, and industrial robots), and testing environments (indoors and outdoors), and it is well suited to rigorously validate novel image-based robot-motion estimation algorithms, including visual odometry, visual SLAM, and deep learning-based methods. PanoraMIS and the accompanying documentation is publicly available on the Internet for the entire research community.
Application of Clustering Algorithms to TRMM Precipitation over the Tropical and South Pacific Ocean

Understanding multiscale rainfall variability in the South Pacific convergence zone (SPCZ), a southeastward-oriented band of precipitating deep convection in the South Pacific, is critical for both the human and natural systems dependent on its rainfall, and for interpreting similar off-equatorial diagonal convection zones around the globe. A k-means clustering method is applied to daily austral summer (December–February) Tropical Rainfall Measuring Mission (TRMM) satellite rainfall to extract representative spatial patterns of rainfall over the SPCZ region for the period 1998–2013. For a k = 4 clustering, pairs of clusters differ predominantly via spatial translation of the SPCZ diagonal, reflecting either warm or cool phases of El Niño–Southern Oscillation (ENSO). Within each of these ENSO phase pairs, one cluster exhibits intense precipitation along the SPCZ while the other features weakened rainfall. Cluster temporal behavior is analyzed to investigate higher-frequency forcings (e.g., the Madden–Julian oscillation and synoptic-scale disturbances) that trigger deep convection where SSTs are sufficiently warm. Pressure-level winds and specific humidity from the Climate Forecast System Reanalysis are composited with respect to daily cluster assignment to investigate differences between active and quiescent SPCZ conditions to reveal the conditions supporting enhanced or suppressed SPCZ precipitation, such as low-level poleward moisture transport from the equator. Empirical orthogonal functions (EOFs) of TRMM precipitation are computed to relate the “modal view” of SPCZ variability associated with the EOFs to the “state view” associated with the clusters. Finally, the cluster number is increased to illustrate the change in TRMM rainfall patterns as additional degrees of freedom are permitted.