Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the performances of commercial and non-commercial software for ground filtering of UAV-based point clouds

ABSTRACT In recent years, the advent of unmanned aerial vehicles (UAV)-based photogrammetry has enabled the collection of accurate and comprehensive information from the surface of the Earth. Owing to low-altitude flights, it is possible to generate high-density point clouds, which are useful for accurate representation of topography of the land surface. Ground filtering is the removal of the points belonging to above-ground objects in order to retrieve ground points to be used in generating digital terrain models. It is essential in most applications for modelling the environment and is performed by using various types of commercial and non-commercial software. This study investigates the performances of seven widely used ground filtering algorithms on UAV-based point clouds. These algorithms are (1) the adaptive triangulated irregular network implemented into the commercial Agisoft Photoscan Professional software, (2) the multi-scale curvature classification implemented into the commercial global mapper software, (3) the cloth simulation filtering (CSF) applied with a MATLAB script, (4) the interpolation-based Boise Centre Aerospace Laboratory-lidar algorithm embedded in the commercial environment for visualizing images software, (5) the interpolation-based gLiDAR non-commercial software, (6) the 2D progressive morphological algorithms, and (7) elevation threshold with expand window algorithms embedded in the non-commercial airborne lidar data processing and analysis tools software. The results showed that the CSF algorithm presented the best filtering results for both test sites with Total Errors of 6% and 4.5% in the sites 1 and 2, respectively.
Prognostication in Traumatic Brain Injury- A Prospective Study of 100 Patients in Tertiary Care Hospital in Rural Area

BACKGROUND Traumatic brain injury is a major health issue responsible for considerable mortality and morbidity worldwide especially in subjects under the age of 40 yrs. It is important to assess and grade the TBI as soon as possible to guide management and decrease the comorbidities. Various guidelines have been issued by the neurosurgical societies to immediately assess and intervene when ever required. In this study, we have tried to assess the role of basal cisternal effacement in the management and prognosis of RTA patients, and hence tried to simplify the prognostication process and improve the patient management. NCCT (Philips Medical Systems, USA). Other parameters like pupillary reaction, GCS at the time of presentation, midline shift and associated fractures and bleed were assessed. All the patients were followed up till the time of discharge. The data so obtained was analysed. Parameters like age, pupillary reflex, GCS at presentation, associated intracranial bleed, associated cranial vault fractures and presence or absence of midline shift correlated well with the final outcome with p value consistently <0.05. We analysed that the degree of obliteration of perimesencephalic cistern was a good prognostic marker in traumatic head injury patients. 36% of patients had favourable outcome out of which none of the patients had obliterated perimesencephalic cistern or interpeduncular cisterns. 64% patients had unfavourable outcome out of which 60% and 48% had obliterated or partially obliterated perimesencephalic cisterns and interpeduncular cisterns respectively, and only 4% and 16% had normal perimesencephalic cisterns and interpeduncular cisterns respectively. reactivity correlate obliteration
The effects of protein supplementation, fumagillin treatment, and colony management on the productivity and long-term survival of honey bee (Apis mellifera) colonies in Canada

In this study, we intensively measured the longitudinal productivity and survival of 362 commercially managed honey bee colonies in Canada, over a two-year period. A full factorial experimental design was used, whereby two treatments were repeated across apiaries situated in three distinct geographic regions: Northern Alberta, Southern Alberta and Prince Edward Island, each having unique bee management strategies. In the protein supplemented treatment, colonies were continuously provided a commercial protein supplement containing 25% w/w pollen, in addition to any feed normally provided by beekeepers in that region. In the fumagillin treatment, colonies were treated with the label dose of Fumagilin-B® each year during the fall. Our study shows that that neither treatment provided consistent benefits across all sites and dates. Fumagillin was associated with a large increase in honey production only at the Northern Alberta site, while protein supplementation produced an early season increase in brood production only at the Southern Alberta site. The protein supplement provided no long-lasting benefit at any site and was also associated with an increased risk of death and decreased colony size later in the study. Differences in colony survival and productivity among regions, and among colonies within beekeeping operations, were far larger than the effects of either treatment, suggesting that returns from extra feed supplements and fumagillin were highly contextually dependent. We conclude that use of fumagillin is safe and sometimes beneficial, but that beekeepers should only consider excess protein supplementation when natural forage is limiting.
Exploring Causal Directions through Word Occurrences: Semi-supervised Bayesian Classification Framework

Determining causal directions in sentences plays a critical role into understanding a cause-and-effect relationship between entities. In this paper, we show empirically that word occurrences from several Internet domains resemble the characteristics of causal directions. Our research contributes to the knowledge of the underlying data generation process behind causal directions. We propose a two-phase method: 1. Bayesian framework, which generates synthetic data from posteriors by incorporating word occurrences from the Internet domains. 2. Pre-trained BERT, which utilises semantics of words based on the context to perform classification. The proposed method achieves an improvement in performance for the Cause-Effect relations of the SemEval-2010 dataset, when compared with random guessing.
The sub-band cepstrum as a tool for local spectral analysis in forensic voice comparison

This paper exploits band-limited cepstral coefficients (BLCCs) in forensic voice comparison (FVC), with the primary aim of locating speaker-sensitive spectral regions. BLCCs are sub-band cepstral coefficients (CCs) which are easily obtained by a linear transformation of full-band CCs. The transformation gives the flexibility of selecting any sub-band region without the recurrent cost of spectral analyses. Using multi-band BLCCs obtained by sliding a 600-Hz sub-band every 400 Hz across the full [0-5kHz] range, FVC experiments were attempted using citation recordings of the 5 Japanese vowels from 297 adult-male, native speakers. The FVC results give locations and ranges for the most speaker-sensitive sub-bands, and show that combining 3-4 of these yields comparable FVC performance with full-band CCs. Owing to their ability to easily extract locally-encoded speaker information from full-band CCs, it can be conjectured that BLCCs have a significant role to play in the search for meaningful interpretations of the numerical outcome of forensic analyses.
Analysis of higher order interactions quantifies co-ordination in the epigenome and reveals novel biological relationships in Kabuki syndrome

Complex direct and indirect relationships between multiple variables are a characteristic of all natural systems and are defined as higher order interactions (HOIs). Traditional differential and network analyses fail to account for the richness of omic datasets and miss HOIs. We investigated genome-wide peripheral blood DNA methylation data from Kabuki syndrome type 1 (KS1) and control individuals, identified 2,002 differentially methylated points (DMPs), and inferred 17 differentially methylated regions, which represent only 189 DMPs. We followed these results with quantification of HOIs by applying hypergraph network models on all the CpGs in the two datasets and revealed differences in co-ordination of the DMPs along with lower entropy and higher co-ordination of the peripheral epigenome in KS1 implying reduced network complexity. We demonstrate that the hypergraph approach captures substantially more information, enables factoring trans-relationships, and identifies biologically relevant pathways that escape the standard analyses. These findings construct the basis of a suitable model that is not computationally intensive for the analysis of the organisation of the epigenome in rare diseases. This approach can be applied to other types of omic datasets, and to other fields of science and medicine to investigate mechanism in big data.
Development of Autonomous Mobile Robot with 3DLidar Self-Localization Function Using Layout Map

In recent years, there has been growing interest in autonomous mobile robots equipped with Simultaneous Localization and Mapping (SLAM) technology as a solution to labour shortages in production and distribution settings. SLAM allows these robots to create maps of their environment using devices such as Lidar, radar, and sonar sensors, enabling them to navigate and track routes without prior knowledge of the environment. However, the manual operation of these robots for map construction can be labour-intensive. To address this issue, this research aims to develop a 3D SLAM autonomous mobile robot system that eliminates the need for manual map construction by utilizing existing layout maps. The system includes a PC for self-position estimation, 3DLidar, a camera for verification, a touch panel display, and the mobile robot itself. The proposed SLAM method extracts stable wall point cloud information from 3DLidar, matches it with the wall surface information in the layout map, and uses a particle filter to estimate the robot’s position. The system also includes features such as route creation, tracking, and obstacle detection for autonomous movement. Experiments were conducted to compare the proposed system with conventional 3D SLAM methods. The results showed that the proposed system significantly reduced errors in self-positioning and enabled accurate autonomous movement on specified routes, even in the presence of slight differences in layout maps and obstacles. Ultimately, this research demonstrates the effectiveness of a system that can transport goods without the need for manual environment mapping, addressing labour shortages in such environments.
Limnological parameters and plankton community responses in Nile tilapia ponds under chicken dung and NPK

: Limnological parameters and plankton community responses in Nile tilapia ponds under chicken dung and NPK (4-14-8) fertilizers. Two types of fertilizers, organic (chicken dung) and inorganic (NPK), were used in three Nile tilapia fishponds to evaluate the influence of enrichment on plankton community and water quality, during the dry period, namely, in July, August, and September. Inlet water was also analyzed. A sampling site was assigned to each pond to measure water quality parameters. Water pH, temperature, residence time, nitrate, and orthophosphate were not significantly different (p>0.05) when temporal variations and pond interactions were taken into account. Comparison between ponds and inlet water showed that free CO 2 , nitrite and ammonia in water were not significantly different (p>0.05). On the other hand, total phosphorus and chlorophyll-a were significantly different (p<0.05) in ponds and in inlet water. Fish growth rate was higher and planktonic organisms, such as green algae (Chlorophyta) and Copepoda-Calanoida, were more abundant in fertilized fishponds. Rotifera and Chaoborus sp densities increased only in fertilized ponds. Results indicate benefits when inorganic fertilizers were added.
Modeling the effects of power efﬁcient approximate multipliers in radio astronomy correlators

Large scale Radio Telescopes for Radio Astronomy highly depend on the availability of large (digital) processing capacities for imaging. Estimates concerning power efﬁ-ciency for future Radio Telescopes lead to anticipated power consumption numbers beyond feasibility. To reduce the power budget, the use of approximate multipliers within the correlator is explored. A baseband equivalent executable model of a radio synthesis telescope is constructed to assess the effects of approximate multipliers. Besides ideal multipliers with ﬂoating point accuracy, the use of accurate 8-bit multipliers and 4 different types of approximate multipliers is explored. For each of these multipliers, the energy efﬁciency of an individual multiplier is known and used to determine the energy efﬁciency improvement of a correlator when using approximate multipliers. The effects of approximation are quantiﬁed by 3 metrics (Signal-to-Noise-Ratio (SNR), Spurious-Free-Dynamic-Range (SFDR) and Root-Mean-Square (RMS) level) derived from maps constructed by the executable model based on an empty sky with only a single point source. This is considered to be the worst case scenario. For illustration purposes, a more realistic input is processed by the model as well. The metrics have been determined based on different SNR levels at the input of each antenna element. For input SNR levels up to 10 dB, all types of approximate multipliers used in this paper can be exploited to improve energy efﬁciency of correlators, leading to a maximum energy reduction of 19 %. For input SNR values up to 30 dB an energy improvement up to 12 % can be achieved. These percentages are based on implementations in a 40nm low power IC technology at 1 GHz
Nonlinear system identiﬁcation and control using state transition algorithm

By transforming identiﬁcation and control for nonlinear system into optimization problems, a novel optimization method named state transition al-gorithm (STA) is introduced to solve the problems. In the proposed STA, a solution to a optimization problem is considered as a state, and the up-dating of a solution equates to a state transition, which makes it easy to understand and convenient to implement. First, the STA is applied to identify the optimal parameters of the estimated system with previously known structure. With the accurate estimated model, an oﬀ-line PID controller is then designed optimally by using the STA as well. Experimental results have demonstrated the validity of the methodology, and comparisons to STA with other optimization algorithms have testiﬁed that STA is a promising alternative method for system identiﬁcation and control due to its stronger search ability, faster convergence rate and more stable performance.
Toward Open-World Human-Robot Interaction: What Types of Gestures Are Used in Task-Based Open-World Referential Communication?

Gestures play a critical role in human-human and human-robot interaction. In task-based contexts, deictic gestures like pointing are particularly important for directing attention to task-relevant entities. While most work on task-based human-human and human-robot dialogue focuses on closed-world domains, recent research has begun to consider open-world tasks, where task-relevant objects may not be known to interactants a priori. In open-world tasks, we argue that a more nuanced consideration of gesture is necessary, as interactants may use gestures that bridge traditional gesture categories, in order to navigate the open-world dimensions of their task environment. In this work, we explore the types of gestures used in open-world task contexts, and their frequencies of use. Our results suggest a need to rethink the way that gesture analysis is approached in the study of human-human and human-robot interaction.
A 10.5 ppm/°C Modi ﬁ ed Sub-1 V Bandgap in 28 nm CMOS Technology with Only Two Operating Points

: Reference voltage/current generation is essential to the Analog circuit design. There have been several ways to generate quality reference voltage using bandgap reference (BGR) and there are mainly two types: current mode and voltage mode. The current-mode bandgap reference (CBGR) is widely accepted in industry due to having an output voltage which is below 1 V. However, its drawbacks include a lack of proportional to absolute temperature (PTAT) current availability, a large silicon area, multiple operating points, and a large temperature coe ﬃ cient (TC). In this paper, various operating points are explained in detail with diagrams. Similar to the conventional voltage mode bandgap reference (VBGR) circuits, modi ﬁ cations of the existing circuits with only two operating points have also been proposed. Moreover, the proposed BGR occupies a much smaller area due to eliminating the complimentary to absolute temperature (CTAT) current-gener-ating resistor. A new self-biased opamp was introduced to operate from a 1.05 V supply, reducing systematic o ﬀ set and TC of the BGR. The proposed solution has been implemented in 28 nm CMOS TSMC technology, and extraction simulations were performed to prove the robustness of the proposed circuit. The targeted mean BGR output is 500 mV, and across the industrial temperature range ( − 40 to 125 °C), the simulated TC is approximately 10.5 ppm/°C. The integrated output noise within the observable frequency band is 19.6 µV (rms). A 200-point Monte Carlo simulation displays a histogram with a 2.6 mV accuracy of 1.2% (±3-sigma). The proposed BGR circuit consumes 32.8 µW of power from a 1.05 V supply in a fast process and hot (125 °C) corner. It occupies a silicon area of 81 × 42 µm (including capacitors). This design can aim for use in biomedical and sensor applications.
Effects of offering free-choice hay for the first 5 days postpartum on productivity, serum inflammatory markers, gut permeability, and colon gene expression in fresh dairy cows

The objective of the present study was to evaluate the effects of offering free-choice hay to cows during the first 5 d immediately after calving on feed intake, milk yield, plasma metabolites, serum inflammatory markers, rumination, gut permeability, and colon gene expression. It was hypothesized that cows offered free-choice hay would have lower gut permeability, lower inflammation, and higher milk production, compared with cows not offered hay. Thirty-two multiparous cows were fed a closeup total mixed ration (TMR; 21.5% starch, 32.1% forage neutral detergent fiber [NDF] on a dry matter basis) until calving. In the postpartum period, all cows were fed a fresh cow TMR (26.8% starch and 23.4% forage NDF) from calving until 21 DIM, and were assigned randomly to receive 1 of 2 treatments as follows: (1) free-choice timothy hay (61.6% NDF; 9.6% crude protein), offered outside of the TMR in a separate manger, for the first 5 d post-partum (FCH; n = 20), or 2) no free-choice hay (NH; n = 12). The FCH cows tended to have lower serum haptoglobin concentration on d 3, compared with NH (0.95 vs. 1.52 mg/mL). Within the FCH group, cows with greater hay intake had a smaller increase in serum amyloid A from d 1 to 3 after calving (r = 0.37), and tended to have a smaller increase in serum haptoglo-bin concentration (r = 0.36). Cows in the FCH group had a lower ratio of starch intake (kg) to forage NDF intake (kg) on d 1 and 2, compared with NH (0.91 vs. 1.14 ± 0.03), and cows that had a lower starch: forage NDF ratio tended to have a smaller increase in serum haptoglobin concentration from d 1 to 3 after calving (r = 0.32). Cows in the FCH group had lower TMR dry matter intake (DMI; 15.0 vs. 17.1 ± 0.93 kg/d) and lower total DMI (TMR + hay DMI; 15.9 vs. 17.1 ± 0.87 kg/d), from d 1 to 5 when free-choice hay was offered, compared with NH. However, the hay treatment did not affect plasma energy metabolite concentration, gut permeability, colon gene expression, milk yield, rumination time, or change in body weight or body condition score. Overall, these findings suggest that offering free-choice hay for the first 5 d after calving may reduce serum inflammatory marker concentration, but milk yield may not increase, due to lower intake.
Exploring Self-Reinforcement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models

Learnersourcing involves students generating and sharing learning resources with their peers. When learnersourcing multiple-choice questions, creating explanations for the generated questions is a crucial step as it facilitates a deeper understanding of the related concepts. However, it is often difficult for students to craft effective explanations due to limited subject understanding and a tendency to merely restate the question stem, distractors, and correct answer. To help scaffold this task, in this work we propose a self-reinforcement large-language-model framework, with the goal of generating and evaluating explanations automatically. Comprising three modules, the framework generates student-aligned explanations, evaluates these explanations to ensure their quality and iteratively enhances the explanations. If an explana-tion’s evaluation score falls below a defined threshold, the framework iteratively refines and reassesses the explanation. Importantly, our framework emulates the manner in which students compose explanations at the relevant grade level. For evaluation, we had a human subject-matter expert compare the explanations generated by students with the explanations created by the open-source large language model Vicuna-13B, a version of Vicuna-13B that had been fine-tuned using our method, and by GPT-4. We observed that, when compared to other large language models, GPT-4 exhibited a higher level of creativity in generating explanations. We also found that explanations generated by GPT-4 were ranked higher by the human expert than both those created by the other models and the original student-created explanations. Our findings represent a significant advancement in enriching the learnersourcing experience for students and enhancing the capabilities of large language models in educational applications.
Photometric Intensity Profiles Analysis for Thick Segment Recognition and Geometric Measures

Abstract The segmentation or the geometric analysis of specular objects is known as a difficult problem in the computer vision domain. It is also true for the problem of line detection where the specular reflection implies numerous false positive line detection or missing lines located on the dark parts of the object. This limitation reduces its potential use for concrete industrial applications where metallic objects are frequent. In order to overcome this limitation, a new strategy to detect thick segment is proposed. It is not based on the image gradient as usually, but rather exploits the image intensity profile defined inside a parallel strip primitive. Associated to a digital straight segment recognition algorithmwhich is robust to noise, this strategy was implemented to track metallic tubular objects in gray-level images. The efficiency of the proposed method is demonstrated through extensive tests using an actual industrial application. An alternate release intended to overcome the possible impact of the digitization process on the achieved performance is also introduced. Both strategies are discussed at the end of the article.