Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Segmentation of Large-Scale 3D Point Clouds Using Sparse Convolutional Neural Networks

Semantic analysis of large-scale 3D point clouds play an important role in computer vision and urban scene understanding while 3D acquisition technology developing rapidly. In this paper, we pro-pose a novel framework for fully automatic semantic segmentation of large-scale 3D point clouds derived from photogrammetric re-constructions. Firstly, the super points in the original point cloud are extracted for size reducing. And then learn end-to-end a sparse convolutional neural network to infer super-point semantic labels for local regions. Finally, all super-point semantic labels are fused and mapped back to the original point cloud according to down-sampling correspondences. We performed a qualitative and quantitative evaluation on Pix4D data, the experimental results show that not only the accuracy of this framework has reached level of state-of-art, but also the efficiency and scalability have been improved.
Can Language Models Identify Wikipedia Articles with Readability and Style Issues?

Wikipedia is frequently criticised for having poor readability and style issues. In this article, we investigate using GPT-2, a neural language model, to identify poorly written text in Wikipedia by ranking documents by their perplexity. We evaluated the properties of this ranking using human assessments of text quality, including readability, narrativity and language use. We demonstrate that GPT-2 perplexity scores correlate moderately to strongly with narrativity, but only weakly with reading comprehension scores. Importantly, the model reflects even small improvements to text as would be seen in Wikipedia edits. We conclude by highlighting that Wikipedia's featured articles counter-intuitively contain text with the highest perplexity scores. However, these examples highlight many of the complexities that need to be resolved for such an approach to be used in practice.
Feasibility of Play-Based Dance to Promote Physical Activity in Second Grade Students

ABSTRACT Background Physical activity (PA) is associated with positive health outcomes. However, children’s access to physical activities they enjoy may be limited. Purpose To evaluate second-grader’s PA against the U.S. DHHS goals to see if a before-school, play-based dance program contributed to these goals. Operational feasibility was evaluated using activity trackers to measure minutes and intensity of PA. Methods A SWOT analysis contributed to intervention implementation. Twenty-seven second-graders enrolled in the Intervention or Comparison group and wore activity trackers three days/week for eight weeks. A multivariate analysis of variance was used to determine differences between the PA intensity of the two groups each week. Results All children achieved the U.S. DHHS activity goals. The Intervention group (n =11) averaged 18 more minutes of daily moderate-to-vigorous intensity PA than the Comparison group (n = 10). More girls than boys chose to attend the morning dance, which was implemented for 31 of 50 school days. Discussion Our findings support dance as a feasible PA that contributed to activity goals when designed with health and fitness in mind. Translation to Health Education Practice Certified Health Education Specialists can use a SWOT analysis and activity trackers to assess opportunities to add PA with guided dance.
Gut Microbial Diversity in Female Patients With Invasive Mole and Choriocarcinoma and Its Differences Versus Healthy Controls

Objective To investigate variation in gut microbiome in female patients with invasive mole (IM) and choriocarcinoma (CC) and compare it with healthy controls. Methods Fecal microbiome of 12 female patients with IM, 9 female patients with CC, and 24 healthy females were analyzed based on 16s rDNA sequencing. Alpha (α) diversity was evaluated using Shannon diversity index and Pielou evenness index, while beta (β) diversity was assessed using principle coordinate analysis (PCoA) of unweighted Unifrac distances. The potential functional changes of microbiomes were predicted using Tax4Fun. The relative abundance of microbial taxa was compared using Welch’s t test. The role of varied gut microbiota was analyzed via receiver operating characteristic (ROC) curve. Results The α diversity and β diversity were significantly different between IM patients and controls, but not between CC patients and controls. In addition, the abundance of cancer-related genes was significantly increased in IM and CC patients. Notably, a total of 19 families and 39 genera were found to have significant differences in bacterial abundance. ROC analysis indicated that Prevotella_7 may be a potential biomarker among IM, CC, and controls. Conclusion Our study demonstrated that the diversity and composition of gut microbiota among IM patients, CC patients, and healthy females were significantly different, which provides rationale for using gut microbiota as diagnostic markers and treatment targets, as well as for further study of gut microbiota in gestational trophoblastic neoplasia (GTN).
Local mechanical stimuli shape tissue growth in vertebrate joint morphogenesis

The correct formation of synovial joints is essential for proper motion throughout life. Movement-induced forces are critical to creating correctly shaped joints, but it is unclear how cells sense and respond to these mechanical cues. To determine how mechanical stimuli drive joint morphogenesis, we combined experiments on regenerating axolotl forelimbs with a poroelastic model of bone rudiment growth. Animals either regrew forelimbs normally (control) or were injected with a TRPV4 agonist to impair chondrocyte mechanosensitivity during joint morphogenesis. We quantified growth and shape in regrown humeri from whole mount light sheet fluorescence images of the regenerated limbs. Results revealed statistically significant differences in morphology and cell proliferation between the two groups, indicating that mechanical stimuli play a role in the shaping of the joint. Local tissue growth in our finite element model was dictated by a biological contribution, proportional to chondrocyte density, and a mechanical one, driven by fluid pore pressure dynamics. Computational predictions agreed with experimental outcomes, suggesting that interstitial pressure might promote local tissue growth. Predictive computational models informed by experimental findings allow us to explore potential physical mechanisms and regulatory dynamics involved in tissue growth to advance our understanding of the mechanobiology of joint morphogenesis.
Efficient decoding of large-scale neural population responses with Gaussian-process multiclass regression

Neural decoding methods provide a powerful tool for quantifying the information content of neural population codes and the limits imposed by correlations in neural activity. However, standard decoding methods are prone to overfitting and scale poorly to high-dimensional settings. Here, we introduce a novel decoding method to overcome these limitations. Our approach, the Gaussian process multi-class decoder (GPMD), is well-suited to decoding a continuous low-dimensional variable from high-dimensional population activity, and provides a platform for assessing the importance of correlations in neural population codes. The GPMD is a multinomial logistic regression model with a Gaussian process prior over the decoding weights. The prior includes hyperparameters that govern the smoothness of each neuron’s decoding weights, allowing automatic pruning of uninformative neurons during inference. We provide a variational inference method for fitting the GPMD to data, which scales to hundreds or thousands of neurons and performs well even in datasets with more neurons than trials. We apply the GPMD to recordings from primary visual cortex in three different species: monkey, ferret, and mouse. Our decoder achieves state-of-the-art accuracy on all three datasets, and substantially outperforms independent Bayesian decoding, showing that knowledge of the correlation structure is essential for optimal decoding in all three species.
Age-related neural dedifferentiation for individual stimuli: An across-participant pattern similarity analysis

Age-related neural dedifferentiation – reductions in the regional specificity and precision of neural representations – is proposed to compromise the ability of older adults to form sufficiently distinct neural representations to support episodic memory encoding. The computational model that spurred investigations of age-related neural dedifferentiation initially characterized this phenomenon as a reduction in the specificity of neural patterns for individual items or stimuli. Most investigations have focused on reductions in neural differentiation for patterns of neural activity associated with category level information, such as reduced neural selectivity between categories of visual stimuli (e.g., scenes, objects, and faces). Here, I report a novel across-participant pattern similarity analysis method to measure neural distinctiveness for individual stimuli that were presented to participants on a single occasion. Measures of item level pattern similarity during encoding showed a graded positive subsequent memory effect in younger, with no significant subsequent memory effect in older adults. These results suggest that age-related reductions in the distinctiveness of neural patterns for individual stimuli during age differences in memory encoding. Moreover, a measure of category level similarity demonstrated a significant subsequent memory effect associated with item recognition (regardless of an object source memory detail), whereas the effect in older was associated with source memory. These results converge with predictions of computational models of dedifferentiation showing age-related reductions in the distinctiveness of neural patterns across multiple levels of representation.
Cell tip growth underlies injury response of marine macroalgae

Regeneration is a widely observed phenomenon by which the integrity of an organism is recovered after damage. So far, studies on the molecular and cellular mechanisms of regeneration have been limited to a handful of model multicellular organisms. Here, we systematically surveyed the regeneration ability of marine macroalgae (Rhodophyta, Phaeophyceae, Chlorophyta) after thallus severing and applied live cell microscopy on them to uncover the cellular response to the damage. We observed three types of responses – budding, rhizoid formation and/or sporulation – in 25 species among 66 examined, demonstrating the high potential of regeneration of macroalgae. In contrast, callus formation, which often accompanies plant regeneration, was never observed. We monitored the cellular and nuclear dynamics during cell repair or rhizoid formation of four phylogenetically diverged Rhodophyta and Chlorophyta species (Colaconema sp., Dasya sessilis, Cladophora albida, Codium fragile). We observed tip growth of the cells near the damaged site as a common response, despite the difference in the number of nuclei and cells across species. Nuclear translocation follows tip growth, enabling overall uniform distribution of multinuclei (Dasya sessilis, Cladophora albida, Codium fragile) or central positioning of the mononucleus (Colaconema sp.). In contrast, the control of cell cycle events, such as nuclear division and septation, varied in these species. In Dasya sessilis, the division of multinuclei was synchronised, whereas it was not the case in Cladophora albida. Septation was tightly coupled with nuclear division in Colaconema and Dasya but not in others. These observations show that marine macroalgae utilise a variety of regeneration pathways, with some common features. This study also provides a novel methodology of live cell biology in macroalgae, offering a foundation for the future of this under-studied taxon.
Multimodal measurements of phototoxicity in nonlinear optical microscopy

Nonlinear optical imaging modalities, such as two-photon microscopy and stimulated Raman scattering (SRS) microscopy, make use of pulsed-laser excitation with high peak intensity that can perturb the native state of cells. In this study, we investigated the short and long-term effects of pulsed laser induced phototoxicity. We used bulk RNA sequencing, quantitative measurement of cell proliferation, and measurement of the generation of reactive oxygen species (ROS) to assess phototoxic effects, at different time scales, for a range of laser excitation settings relevant to SRS imaging. We define a range of laser excitation settings for which there was no significant ROS generation, differential gene expression, or change in proliferation rates of mouse Neuro2A cells. Changes in proliferation rate and ROS generation were observed under imaging conditions with an excitation intensity of over 600 mW/μm2. Repeated imaging of the same field of view at this excitation intensity of over 600 mW/μm2 resulted in visual damage to N2A cells. Laser induced perturbations in live cells may impact downstream measurements of cell state including subsequent imaging or molecular measurements. This study provides guidance for imaging parameters that minimize photo-induced perturbations in SRS microscopy to ensure accurate interpretation of experiments with time-lapse imaging or with paired measurements of imaging and sequencing on the same cells.
Thermodynamics and vacuum distillation studies of liquid Al–Ga and In–Sn alloys

ABSTRACT Activities of components in liquid Al–Ga and In–Sn alloys, the separation coefficients and vapour–liquid phase equilibrium in vacuum distillation were predicted using the molecular interaction volume model as a function of the activity coefficients. The results indicated that both Al and In are preferentially volatilised into vapour phase while Ga and Sn remain in residue. Similarly, we found that both the mass fraction and the content of Al and In in vapour phase increase as distillation temperature increases such that when the content of Al is 0.005985 wt% and In is 0.004141 wt% in vapour phase, respectively, in liquid phase, it was 70 wt% at T = 1073 K for both. The calculated values of activity and activity coefficients at various temperatures are presented. Comparison of the predicted values with experimental data indicates good agreement, thus verifying from statistical thermodynamics viewpoint that the model is stable and reliable.
EMOTION TWEETS CLASSIFICATION USING MACHINE LEARNING

- Twitter is a Social verbal exchange Platform which permits customers to label their tweets in extraordinary codecs public can effortlessly explicit their perspectives and thoughts on a extensive form of cluster on subjects thru social networking websites. Sentiment evaluation is a Study comes beneath Natural Language Processing It allows to discover the real Sentiment hidden in a text. In these studies, we attention on Finding the Sentiments for Twitter statistics set. It has greater exacting because of its unstructured statistics, constrained size, use of slangs, misspells, emojis, abbreviations, prevent words. Most of the researchers dealt with diverse sorts of methods of sentiment evaluation and evaluate their results the use of diverse methods had been underexplored inside the literature Hence, in our studies we strive to carry out sentiment evaluation on twitter statistics the use of herbal language processing. Here we are taking two data sets for testing and training. In train data set we have label means two values 0 and 1. It is to indicate racist and non-racist tweets. In this research we are making the data as a structured manner with nlp techniques like tokenization, stemming, lemmatize etc and we have used svm classifier for prediction of the data and we need to find the confusion matrix and accuracy. Compares its performance with other well-known classifiers. Comparison results shows that SVM classifier better predicts the outcome.
Review on Evolutionary Change and Expressiveness in Lyrics of Indian Filmy Songs with Natural Language Processing

: Examination in Natural Language Processing is extending in various spaces and is saturating all parts of existence with time. With each progression, the assortment of text that can be prepared is developing. One such area is verses handling. Songs are fundamental to the music and entertainment world and can be broke down to acquire significant data, for example, type, topic, mind-set, and so forth of the tune and supplement the data assembled by the investigation of its sound highlights. Bollywood, the Indian entertainment world makes a ton of income utilizing melodies. The quantity of tunes produced by Indian film industry is gigantic and is a rich wellspring of sound and literary information for Natural Language Processing assignments. It additionally offers us a chance to deal with information in Hindi which is a generally less investigated field.
FAST GAUSS-RELATED QUADRATURE FOR HIGHLY OSCILLATORY INTEGRALS WITH LOGARITHM AND CAUCHY-LOGARITHMIC TYPE SINGULARITIES

This paper presents an efficient method for the computation of two highly oscillatory integrals having logarithmic and Cauchy-logarithmic singularities. This approach first requires the transformation of the original oscillatory integrals into a sum of line integrals with semi-infinite intervals. Afterwards, the coefficients of the three-term recurrence relation that satisfy the orthogonal polynomial are obtained by using the method based on moments, where classical Laguerre and Gautschi’s logarithmic weight functions are employed. The algorithm reveals that with fixed n, the method is capable of achieving significant figures within a short time. Furthermore, the approach yields higher accuracy as the frequency increases. The results of numerical experiments are given to substantiate our theoretical analysis.
Decision-making Model Using XCS in Artiﬁcial Market

—This paper proposes a decision-making model using XCS in the artiﬁcial market to analyze an uncertain price ﬂuctuation. We report the price ﬂuctuation created by the interaction of agents using that decision-making models. The revealed the following remarkable implications: (1) the decision-making model using XCS is necessary more than eight ways (action size 3 bits) by action selection to represent the dynamics of price ﬂuctuations in state size 4 bits of XCS; and (2) The increase of the action selection indicates the effect of low market price in big dynamic phenomena.
Recent Advances in Microneedle-Based Sensors for Sampling, Diagnosis and Monitoring of Chronic Diseases

Chronic diseases (CDs) are noncommunicable illnesses with long-term symptoms accounting for ~70% of all deaths worldwide. For the diagnosis and prognosis of CDs, accurate biomarker detection is essential. Currently, the detection of CD-associated biomarkers is employed through complex platforms with certain limitations in their applicability and performance. There is hence unmet need to present innovative strategies that are applicable to the point-of-care (PoC) settings, and also, provide the precise detection of biomarkers. On the other hand, especially at PoC settings, microneedle (MN) technology, which comprises micron-size needles arranged on a miniature patch, has risen as a revolutionary approach in biosensing strategies, opening novel horizons to improve the existing PoC devices. Various MN-based platforms have been manufactured for distinctive purposes employing several techniques and materials. The development of MN-based biosensors for real-time monitoring of CD-associated biomarkers has garnered huge attention in recent years. Herein, we summarize basic concepts of MNs, including microfabrication techniques, design parameters, and their mechanism of action as a biosensing platform for CD diagnosis. Moreover, recent advances in the use of MNs for CD diagnosis are introduced and finally relevant clinical trials carried out using MNs as biosensing devices are highlighted. This review aims to address the potential use of MNs in CD diagnosis.
Suppression of neuronal cholesterol biosynthesis impairs brain functions through insulin-like growth factor I-Akt signaling

Some relationship between abnormal cholesterol content and impairment of insulin/insulin-like growth factor I (IGF-1) signaling has been reported in the pathogenesis of Alzheimer's disease (AD). However, the underlying mechanism of this correlation remains unclear. It is known that 3-β hydroxycholesterol Δ 24 reductase (DHCR24) catalyzes the last step of cholesterol biosynthesis. To explore the function of cholesterol in the pathogenesis of AD, we depleted cellular cholesterol by targeting DHCR24 with siRNA (siDHCR24) or U18666A, an inhibitor of DHCR24, and studied the effect of the loss of cholesterol on the IGF-1-Akt signaling pathway in vitro and in vivo. Treatment with U18666A reduced the cellular cholesterol level and blocked the anti-apoptotic function of IGF-1 by impairing the formation of caveolae and the localization of IGF-1 receptor in caveolae of the PC12 cells. Downregulation of the DHCR24 expression induced by siRNA against DHCR24 also yielded similar results. Furthermore, the phosphorylation levels of IGF-1 receptor, insulin receptor substrate (IRS), Akt, and Bad in response to IGF-1 were all found to decrease in the U18666A-treated cells. Rats treated with U18666A via intracerebral injection also exhibited a significant decrease in the cholesterol level and impaired activities of IGF-1-related signaling proteins in the hippocampus region. A significant accumulation of amyloid β and a decrease in the expression of neuron-specific enolase (NSE) was also observed in rats with U18666A. Finally, the Morris water maze experiment revealed that U18666A-treated rats showed a significant cognitive impairment. Our findings provide new evidence strongly supporting that a reduction in cholesterol level can result in neural apoptosis via the impairment of the IGF-1-Akt survival signaling in the brain.
CUMIN(Cuminum cyminum L.) EFFECTS ON BIOCHEMICAL AND QUALTY ATTRIBUTES OF RED TILAPIA FILLETS (Oreochromus niloticus) STORED IN ICE

The effect of powdered cumin sprinkling on the biochemical and sensory properties of fresh and ice-stored tilapia fillets during 21 days was evaluated. Initially, significant differences in pH, TMA-N, odour, flavour, appearance and overall acceptability were found between cumin-treated and untreated tilapia fillets at (p<0.05). Cumin powder addition (2% w/w) exhibited a preservative effect in treated fillets since significant lower levels of TMA-N were observed during ice storage. Both of TVBN and TMAN values were lower than acceptability limits during the storage of 21 days. The Sensory quality of cooked tilapia fillets was assessed by ten semitrained panels at zero time and after 3, 6, and 9 days of ice storage. The present findings showed that cumin-treated tilapia fillets maintained their quality better than untreated fillets during the ice storage, since they achieved higher scores than untreated fillets.
THE EFFECT OF TRANSFORMATIONAL LEADERSHIP AND WORK ENGAGEMENT ON INNOVATIVE WORK BEHAVIOR WITH KNOWLEGDE SHARING AS A MEDIATOR : RESEARCH PROPOSAL

Innovation plays a major role in the sustainability of the organization. The role of human resources is very important to improve innovative work behavior. Many factors influence innovative work behavior. Researchers used four variables according to the locus of research conducted at Palembang Metropolitan Police Station. The variables are transformational leadership, work engagement, knowledge sharing, and innovative work behavior. There are eight hypotheses proposed in this study as empirical research that aims to determine the effect of transformational leadership and work engagement on innovative work behavior with knowledge sharing as a mediator. The research was conducted in the public sector, namely the Indonesian National Police (POLRI), precisely at the Palembang city Police Resort. Respondents in this study will distributed to 350 personnel of Palembang city Police Resort as a plan. The research method used was SEM-PLS. The data calculation was carried out by using SMART PLS 3.