Miracle-dz/UniFAR-FTUs huggingface dataset:
ESTIMATION OF OPTIMAL PARAMETER FOR RANGE NORMALIZATION OF MULTISPECTRAL AIRBORNE LIDAR INTENSITY DATA

Abstract. Range normalization is a common data pre-process that aims to improve the radiometric quality of airborne LiDAR data. This radiometric treatment considers the rate of energy attenuation sustained by the laser pulse as it travels through a medium back and forth from the LiDAR system to the surveyed object. As a result, the range normalized intensity is proportional to the range to the power of a factor a. Existing literature recommended different a values on different land cover types, which are commonly adopted in forestry studies. Nevertheless, there is a lack of study evaluating the range normalization on multispectral airborne LiDAR intensity data. In this paper, we propose an overlap-driven approach that is able to estimate the optimal a value by pairing up the closest data points of two overlapping LiDAR data strips, and subsequently estimating the range normalization parameter a based on a least-squares adjustment. We implemented the proposed method on a set of multispectral airborne LiDAR data collected by a Optech Titan, and assessed the coefficient of variation of four land cover types before and after applying the proposed range normalization. The results showed that the proposed method was able to estimate the optimal a value, yielding the lowest cv, as verified by a cross validation approach. Nevertheless, the estimated a value is never identical for the four land cover classes and the three laser wavelengths. Therefore, it is not recommended to label a specific a value for the range normalization of airborne LiDAR intensity data within a specific land cover type. Instead, the range normalization parameter is deemed to be data-driven and should be estimated for each LiDAR dataset and study area.
Conditional Beta: Evidence from Emerging Stock Markets

Using the Pettengill et al. (1995) asset pricing model, this paper examines the relationship between conditional beta and returns in 12 emerging stock markets over the period of 2005 to 2017. In applying weekly and monthly data, the evidence shows that there is a flat relationship between beta and returns using the unconditional CAPM. However, the opposite is true when applying the Pettengill et al. (1995) model. The findings indicate that the relationship between beta and returns is positive in a bullish market and negative in a bearish market. In addition, the results support the conditional CAPM for all months of the year. Finally, the results show that market excess returns are positive and the risk-return relationship is symmetrical in both bullish and bearish markets. We conclude that beta is still a valuable risk measure, which helps portfolio managers in emerging markets make optimal investment decisions. (JEL: G12, G15)
Registration of multi-modal volumetric images of embryos by establishing correspondences between cells

Early development of an animal from an egg involves a rapid increase in cell number and several cell fate speciﬁcation events which are accompanied by dynamic morphogenetic changes. In order to correlate the morphological changes with the underlying genetic events, one typically needs to monitor the living system with several imaging modalities oﬀering diﬀerent spatial and temporal resolution. Live imaging allows monitoring the embryo at a high temporal resolution and observing the morphological changes during the early development. Confocal images of specimens ﬁxed and stained for the expression of certain genes provide high spatially-resolved static snapshots and enable observing the transcription states of an embryo at speciﬁc time points during development. The two modalities cannot, by deﬁnition, be applied to the same specimen and thus, separately obtained images of diﬀerent specimens need to be registered. Biologically, the most meaningful way to register the images is by identifying cellular correspondences between these two imaging modalities. In this way, one can bring the two sources of information into a single domain and combine dynamic information on morphogenesis with static gene expression data. The problem of establishing cellular correspondence is non-trivial due to the stochasticity of developmental processes and the non-linear deformation of the specimen during staining protocols. Here we propose a new computational pipeline for identifying cell-to-cell correspondences between images from multiple modalities and for using these correspondences to register 3D images within and across imaging modalities. We demonstrate this pipeline by combining four dimensional time-lapse showing embryogenesis of Spi-ralian ragworm Platyneries dumerilii with three dimensional scans of ﬁxed Platyneries dumerilii embryos stained for the expression of a variety of important developmental transcription factors. We compare our approach with methods for aligning point clouds and show that we match the accuracy of these state-of-the-art registration pipelines on synthetic data. We show that our approach outperforms these methods on real biological imaging datasets. In addition, our approach uniquely provides, in addition to the registration, also the non-redundant matching of corresponding, biologically meaningful entities within the registered specimen which is the prerequisite for generating biological insights from the combined datasets. The complete pipeline is available for public use through a Fiji ([20]) plugin.
Answering Binary Causal Questions: A Transfer Learning Based Approach

Causal question answering is a task of answering causality related questions. The questions are referred to as binary causal questions when the questions e.g., "Could X cause Y? " can be answered by yes/no answers. Answer to the previous question is yes if X is a cause of Y, and otherwise no. The binary causal question answering systems can be used to validate causal relationships, which can be particularly useful for decision making. For example, it could be useful for the tourism authorities to know the answer to the question "Could growing social tension cause reduction in tourism? ". We aim to automatically answer such binary causal questions by developing a machine learning model. However, training a machine learning model to detect causal relationships is challenging due to the lack of large and high quality labeled datasets. In this paper, we propose a transfer learning-based approach which fine-tunes pretrained transformer based language models on a small dataset of cause-effect pairs to detect causality and answer binary causal questions. The proposed approach achieves performance comparable to a number of benchmark approaches on five benchmark test datasets extracted by human experts conditioned on the same small training dataset.
Brain Tumor Segmentation Using K-Means Clustering and Fuzzy C-Means Algorithms and Its Area Calculation and Disease Prediction Using Naive-Bayes Algorithm

: This paper shows the implementation of Simple Algorithm for detection of range and shape of tumor in brain MR images and predicts the disease risk details from the given area of tumor. Tumor is an uncontrolled growth of tissues in any part of the human body. Tumors are of various types and they have various characteristics and different treatment. A brain tumor is inherently serious and life-threatening because of its character in the limited space of the intracranial cavity (space formed inside the skull). In developed countries most research shows that the numbers of peoples who have brain tumors were died due to the fact of inaccurate detection. Generally, a CT scan or MRI that is conducted into intracranial cavity creates a complete image of brain. Researching a lot statistical search which is based on those people whose are affected in brain tumor some general Risk factors and Symptoms have been found. The development of technology in science day night tries to develop new techniques of treatment. This image is visually checked by the physician for detection & diagnosis of brain tumor. However this technique accurate shows the accurate of stage & size of tumor and also predicts the disease details from the area of tumor. This work uses segmentation of brain tumor based on the k-means and fuzzy c-means algorithms. This method allows the segmentation of tumor tissue with accuracy comparable to manual segmentation. In addition, it also decrease the time for analysis and predicts the disease details from the given area of tumor.
The BioMar marine habitat classification – its application in mapping, sensitivity and management. ICES CM 2000/T:03

A marine habitat classification has been developed for intertidal and nearshore subtidal waters of Britain and Ireland as part of the EC Life-funded BioMar project (1992-97). The classification system was developed to aid the management and conservation of marine habitats, and is now well established as a management tool in UK nature conservation, particularly in the implementation of the EC Habitats Directive. The BioMar classification has been applied, through a national mapping system, to habitat mapping at broad and fine levels of detail. It is being used to systematically assess seabed sensitivity to a wide range of human activities. Through consistent characterisation of seabed communities, it facilitates monitoring of both spatial and temporal trends in communities as well as inter-site evaluation of aspects of biodiversity and rarity. This paper will examine a variety of practical applications of the classification, thereby illustrating the potential benefits of broader classifications, such as the European EUNIS classification and that being developed for the OSPAR/ICES areas.
Concert hall geometry optimization with a simplified acoustic diffusion model

The design of rooms for musical interpretation is the most complex from an acoustic point of view, as each type of music requires a room with specific and differentiated acoustic characteristics. Over the last few decades, significant progress has been made in relating subjective assessments of the acoustic quality of a room to a series of objective (physically measurable) parameters. In particular, in concert hall design, the reverberation time (RT) and the strength factor (G), related to loudness, have been considered of crucial importance. In this research, an optimal preliminary design methodology of concert hall geometry is proposed with the objective of keeping the mentioned acoustical parameters within desirable ranges. The methodology is based on a combination of a simplified acoustic diffusion model (SADM) and the simulated annealing (SA) optimization technique. The SADM allows one to accurately and quickly evaluate the effects of different geometries on acoustic parameters and the SA method is used to direct the search of the optimum set of design geometric variables.
Attitude and Achievement in Euclidean Geometry

The study investigates the relationship between the attitude of a sample of grade ten pupils working in a rural environment towards Euclidean geometry and their achievement in Euclidean geometry. A great concern amongst South African mathematics educationists is the poor performance of pupils in Euclidean geometry. Similarly, despite its importance, mathematics seems to be an unpopular subject among most of South African pupils. According to the South African education system, every pupil has to take mathematics up to grade nine thereafter one has an option of either continuing or discontinuing with mathematics. Furthermore, pupils electing to continue with mathematics are normally those who have shown some aptitude for mathematics. Four dimensions of attitude namely: enjoyment, motivation, perception of the importance of geometry and freedom from fear of geometry are looked at in the study. Whereas achievement refers to the ability by pupils to solve riders and construct proofs of theorems. The analysis of data was done using SAS. Two dimensions of the attitude scale, namely, enjoyment and importance showed no significant relationship to achievement. A weak but significant relationship was shown between motivation and achievement, whereas a negative relationship was obtained between freedom from fear and achievement. In general, these pupils showed a better attitude towards Euclidean geometry than one would expect from their performance.
Stain-free 3D Nanoscopy of Living Cells

The cell is the basis of all life on earth. Therefore, being capable of directly observing and measuring the internal parts of a living cell would open a wealth of possibilities. However, due to the limitation of light used as a biological probe, revealing this internal structure is either strongly limited or requires strong and tedious modifications of observed cells. State-of-the art microscopy techniques rely on complicated procedures (risk of errors), which require difficult and time-consuming preparations, are invasive to the cells (risk of damaging and results falsification), and still give rather limited results. This article presents a novel non-invasive and tomographic imaging approach for quantitative analysis and differentiation of cell components. Changes in refractive index within the cell itself provide the contrast for labeling various organelles with high precision and at a high resolution. The method can be applied to the 3D examination of living cells and biological samples under various external stimuli or drugs.
The Diurnal Temperature Range in CMIP6 Models: Climatology, Variability, and Evolution

The diurnal temperature range (DTR) is an identifiable and sensitive indicator of the synchronicity of changes in diurnal temperature extrema, but capturing DTR dynamics is challenging for climate models. This study investigates the climatology, variability, and changes of DTR in recent models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The results show that the CMIP6 models underestimate DTR climatology relative to observations. Most individual models overestimate December–February variability, particularly at high latitudes of the Northern Hemisphere. The models show substantially different changes over land surfaces and do not fully capture the observed spatiotemporal evolution of DTR. Large intermodel differences seem to be controlled by daily minimum air temperature. In the Northern Hemisphere, precipitation and cloud longwave and shortwave radiative effects appear to make important contributions to the intermodel discrepancies. Evaporative fraction is an important factor contributing to the intermodel differences in DTR during the summer in the Northern Hemisphere. In general, CMIP6 models have not improved their ability to simulate temporal DTR changes in a consistent way over the entire analysis period (1901–2005) relative to their CMIP5 counterparts. For periods of rapid DTR decline (e.g., 1951–80) CMIP6 models are typically better than the CMIP5 versions at simulating DTR, whereas for other periods CMIP6 models underperform their CMIP5 counterparts.
Spatial distribution patterns of fish assemblages relative to macroinvertebrates and environmental conditions in Andean piedmont streams of the Colombian Amazon

Abstract This work provides insight into an Andean–Amazonian river system by conducting a comprehensive study of the biodiversity and ecological processes in a poorly studied neotropical freshwater ecosystem. Hacha River in western Colombia harbours a rich and largely unknown biodiversity and is experiencing an increase in anthropogenic impacts from the city of Florencia. Changes in natural habitat characteristics and environmental quality were analysed and related to the distribution of fish and macroinvertebrates. Four environmental quality indices were applied, and concentrations of potential water pollutants were measured. All environmental quality indices used were reliable because they produced similar assessments of the ecological integrity of the study sites and were inversely related to the potential water pollutant concentrations. Fish and macroinvertebrate communities notably changed in response to differences in environmental quality, reinforcing the ecological integrity assessments. Nevertheless, natural gradients that influence fish and macroinvertebrate distributions showed an overlap with changes in environmental quality, thereby confounding the ability to discern the influence of natural and anthropogenic-induced alterations and to confirm an initials hift in fish assemblages due to anthropogenic impacts.
DARK SOLITONS IN MULTICOMPONENT YAJIMA-OIKAWA SYSTEM K. Sakkaravarthi and T. Kanna

We considered an integrable multicomponent Yajima-Oikawa system and construct the dark oneand twosoliton solutions by using the Hirota’s bilinearization method. Then we investigate the propagation and collision dynamics of multicomponent dark solitons. Especially, we reveal the role of arbitrary nonlinearity coefficients on the soliton dynamics.
Wordhood and Disyllabicity in Chinese

Words pose a theoretical challenge in Chinese (i.e. Sinitic languages like Mandarin), but words pose a challenge in any language. Even though Chinese is written with monosyllabic, monomorphemic characters and no overt word boundaries, there is as much evidence here as there is in English or any other language for a level between the morpheme and the phrase, interfacing between the lexicon and the grammar. Yet their interface role makes words dynamic things, subject to distinct and often conflicting constraints from processing, semantics, phonology, morphology, and syntax. To emphasize the universality of this situation, I start in section 2 with a quick look at the dynamic nature of English words, then turn in section 3 to Chinese words, which a wide variety of data reveal as surprisingly English-like, including a strong preference for disyllabicity. In section 4 I sketch out a formalism that may help capture the universal yet dynamic nature of wordhood, trying it out on some of the Chinese facts. Section 5 gives some brief conclusions.
V IDEO T RANSMISSION IN V-BLAST E NCODED MIMO MC-CDMA W IRELESS S YSTEM : P ERFORMANCE S TUDY

In this paper, the bit error rate (BER) performance study on video transmission in a V-BLAST encoded 2×2 MIMO MC-CDMA wireless communication system has been made. The system deploys two channel encoding schemes (1/2-rated Convolution and CRC) under four digital modulations (BPSK, DPSK, QPSK and QAM) and zero forcing (ZF) channel detection method. It is apparent that the BPSK modulation outperforms as compared to DPSK, QPSK and QAM modulation schemes in V-BLAST based MIMO MC-CDMA communication system under AWGN channel. The 16QAM digital modulation shows worst performance in video transmission as compared to other low order digital modulations. It is obvious from the present MATLAB based simulation study that the V-BLAST encoded 2×2 MIMO MC-CDMA wireless system with the deployment of ZF signal detection and ½-rated Convolution and CRC channel coding schemes shows superior performance using BPSK digital modulation in video transmission.
The influence of genetically modified glyphosate-tolerant maize CC-2 on rhizosphere bacterial communities revealed by MiSeq sequencing

Genetically modified (GM) crops have brought huge economic benefits to mankind, however, at the same time, their safety issues are drawing growing attention. This investigation was conducted to assess whether the long-term cultivation of GM glyphosate resistant maize CC-2 effects bacterial communities in the rhizosphere soil. A 2-year follow-up trial was conducted, and soils were sampled at various plant developmental stages. The bacterial community structure of the rhizosphere soil was analysed by the high-throughput sequencing and compared with the near-isogenic non-GM maize Zheng 58. We showed here that long-term cultivation of CC-2 has no significant effect on the structure and diversity of bacterial communities, while different growth stages had significant effect. These results provided a reliable theoretical basis for the future cultivation and increased commercialisation of CC-2.
Estimation of Traffic Volume Using Deep Learning in Stereo CCTV Image

Traffic estimation mainly involves surveying equipment such as automatic vehicle classification, vehicle detection system, toll collection system, and personnel surveys through CCTV (Closed Circuit TeleVision), but this requires a lot of manpower and cost. In this study, we proposed a method of estimating traffic volume using deep learning and stereo CCTV to overcome the limitation of not detecting the entire vehicle in case of single CCTV. COCO (Common Objects in Context) dataset was used to train deep learning models to detect vehicles, and each vehicle was detected in left and right CCTV images in real time. Then, the vehicle that could not be detected from each image was additionally detected by using affine transformation to improve the accuracy of traffic volume. Experiments were conducted separately for the normal road environment and the case of weather conditions with fog. In the normal road environment, vehicle detection improved by 6.75% and 5.92% in left and right images, respectively, than in a single CCTV image. In addition, in the foggy road environment, vehicle detection was improved by 10.79% and 12.88% in the left and right images, respectively.
Did the Federal Reserve Break the Phillips Curve? Theory and Evidence of Anchoring Inflation Expectations

In a macroeconomic model with drifting long-run inflation expectations, the anchoring of inflation expectations manifests in two testable predictions. First, expectations about inflation far in the future should no longer respond to news about current inflation. Second, better-anchored inflation expectations weaken the relationship between unemployment and inflation, flattening the reduced-form Phillips curve. We evaluate both predictions and find that communication of a numerical inflation objective better anchored inflation expectations in the United States but failed to anchor expectations in Japan. Moreover, the improved anchoring of U.S. inflation expectations can account for much of the observed flattening of the Phillips curve. Finally, we present evidence that initial Federal Reserve communication around its longer-run inflation objective may have led inflation expectations to anchor at a level below 2 percent.
Response of almond (Prunus dulcis) to different drip irrigation levels vis-à-vis various phenological stages on flowering and yield of almond cv. Shalimar

Experiment was conducted to assess the effect of different irrigation levels vis-à-vis various stages of flowering, fruit set and yield of almond (Prunus dulcis Mill.) plantation cv. Shalimar. The results revealed that irrigation applied at various stages of growth and development improved flowering, fruit set and yield of almond significantly. Maximum bloom intensity, fruit set and yield were recorded with highest evaporation replenishment of 100 ETc applied throughout the season during first and second year respectively. The study further confirmed that highest flowering and yield attributes of almond recorded at 100% ETc level of irrigation were found to be statistically at par with measured parameters recorded at 75% ETc level of irrigation when applied at fruit growth stage and throughout the growth stage. The results revealed clearly that flowering and early fruit growth stages are the most critical stages of irrigation in almond and that post-harvest irrigation may prove to be beneficial.