Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-Based Video Coding Using Colour and Depth Cameras

In this paper, we present a model-based video coding method that uses input from colour and depth cameras, such as the Microsoft Kinect. The model-based approach uses a 3D representation of the scene, enabling several other applications besides video playback. Some of these applications are stereoscopic viewing, object insertion for augmented reality and free viewpoint viewing. The video encoding step uses computer vision to estimate the camera motion. The scene geometry is represented by key frames, which are encoded as 3D quads using a quad tree, allowing good compression rates. Camera motion in-between key frames is approximated to be linear. The relative camera positions at key frames and the scene geometry are then compressed and transmitted to the decoder. Our experiments demonstrate that the model-based approach delivers a high level of detail at competitively low bit rates.
Near‐Real‐Time Estimates on Earthquake Rupture Directivity Using Near‐Field Ground Motion Data From a Dense Low‐Cost Seismic Network

Rupture directivity of earthquakes could amplify ground shaking and cause serious earthquake hazards. Its timely information is therefore of great importance for early shaking alerts and emergency responses. To this end, we test the feasibility of using near‐field (<25 km) ground motion data from a dense low‐cost seismic network in Taiwan to constrain directivity information and provide warning for far‐field areas. The method is simple and robust, based on the real‐time shaking map interpolation and directional attenuation regression analysis. Applications to 16 moderate‐ to large‐magnitude earthquakes in Taiwan and California show that the directivity can be stably obtained within 17 s and agrees well with previous studies when available. An indicator of the strength of directivity amplification is also defined and reveals a prevalence of strong directivity effect for ML ≥ 6.0 earthquakes in Taiwan. Such near‐real‐time directivity information can therefore be useful in earthquake early warning systems for providing more accurate ground shaking alerts.
Improved pre-processing techniques to enhance the accuracy of extracted information from passive satellite data

Italiano . 4 Acronyms . 7 Figures . 10 Tables . 12 Equations . 13 INTRODUCTION . 16 CHAPTER 1: The extraction of Plastic Covered Greenhouse . 18 1.
A hierarchical, retinotopic proto-organization of the primate visual system at birth

The adult primate visual system comprises a series of hierarchically organized areas. Each cortical area contains a topographic map of visual space, with different areas extracting different kinds of information from the retinal input. Here we asked to what extent the newborn visual system resembles the adult organization. We find that hierarchical, topographic organization is present at birth and therefore constitutes a proto-organization for the entire primate visual system. Even within inferior temporal cortex, this proto-organization was already present, prior to the emergence of category selectivity (e.g., faces or scenes). We propose that this topographic organization provides the scaffolding for the subsequent development of visual cortex that commences at the onset of visual experience DOI: http://dx.doi. org/10.7554/eLife.26196. 001
Single-electron energy spectra of two coupled phosphorus donors in silicon

In this work, the electronic spectra of two coupled phosphorus donors in silicon has been computed using a model based on the multi-valley envelope function equations derived in the framework of the k · p pertubation technique applied around each of six equivalent conduction band minima. The 3D real-space multi-valley envelope function equations have been computed by means of the finite element methods using adaptive unstructured grid and a variational technique to compute corrections caused by the valley-orbit coupling. Obtained results evidence an interplay between the valley-orbit coupling and electronic coupling of two couped donors.
Interacting with the virtually recreated Peranakans

Virtual Reality (VR) technology opens up many new possibilities. One of the new and upcoming areas this VR technology is rapidly being used for is in the Digital Heritage domain. With the abundant content and the need for preservation and conservation for cultural heritage, there has been an explosion of Digital Heritage projects worldwide. VR technology provides an important educational tool to recreate the cultural heritage content in an immersive high-quality 3D environment for the users to enter and experience the culture in real-time. Intuitive interaction techniques providing users other tools as an alternative to the conventional mouse and keyboard input also add to enhanced educational value. Here, we present a Digital Heritage project focusing on the Singapore region. We have selected the Peranakans and their Culture as the main topic for reconstruction using the VR technology. Innovative interaction techniques specific to the selected Digital Heritage content is also under development. This paper outlines the motivation, early developments, and implementation currently in progress.
Visualising discourse coherence in nonlinear documents

To produce coherent linear documents, Natural Language Generation systems have traditionally exploited the structuring role of textual discourse markers such as relational and referential phrases. These coherence markers of the traditional notion of text, however, do not work in non-linear documents: a new set of graphical devices is needed together with formation rules to govern their usage, supported by sound theoretical frameworks. If in linear documents graphical devices such as layout and formatting complement textual devices in the expression of discourse coherence, in non-linear documents they play a more important role. In this paper, we present our theoretical and empirical work in progress, which explores new possibilities for expressing coherence in the generation of hypertext documents.
A Source-Channel Coding Approach to Digital Image Protection and Self-Recovery

Watermarking algorithms have been widely applied to the field of image forensics recently. One of these very forensic applications is the protection of images against tampering. For this purpose, we need to design a watermarking algorithm fulfilling two purposes in case of image tampering: 1) detecting the tampered area of the received image and 2) recovering the lost information in the tampered zones. State-of-the-art techniques accomplish these tasks using watermarks consisting of check bits and reference bits. Check bits are used for tampering detection, whereas reference bits carry information about the whole image. The problem of recovering the lost reference bits still stands. This paper is aimed at showing that having the tampering location known, image tampering can be modeled and dealt with as an erasure error. Therefore, an appropriate design of channel code can protect the reference bits against tampering. In the present proposed method, the total watermark bit-budget is dedicated to three groups: 1) source encoder output bits; 2) channel code parity bits; and 3) check bits. In watermark embedding phase, the original image is source coded and the output bit stream is protected using appropriate channel encoder. For image recovery, erasure locations detected by check bits help channel erasure decoder to retrieve the original source encoded image. Experimental results show that our proposed scheme significantly outperforms recent techniques in terms of image quality for both watermarked and recovered image. The watermarked image quality gain is achieved through spending less bit-budget on watermark, while image recovery quality is considerably improved as a consequence of consistent performance of designed source and channel codes.
A hybrid feature and discriminant classifier for high accuracy handwritten Odia numeral recognition

Unconstrained handwritten character recognition is a major research area where there is a lot of scope for improving accuracy. There are many statistical, structural feature extraction techniques being proposed for different languages. Many classifier models are combined with these features to obtain high recognition rates. There still exists a gap between the recognition accuracy of printed characters and unconstrained handwritten scripts. Odia is a popular and classical language of the eastern part of India. Though the research in Optical Character Recognition (OCR) has advanced in other Indian languages such as Devanagari and Bangla, not much attention has been given to Odia character recognition. We propose a hybrid feature extraction technique using Kirsch gradient operator and curvature properties of handwritten numerals, followed by a feature dimension reduction using Principal Component Analysis (PCA). We use Modified Quadratic Discriminant Function (MQDF), Discriminative Learning Quadratic Discriminant Function (DLQDF) classifiers as they provide high accuracy of recognition and compare both the classifier performances. We verify our results using the Odia numerals database of ISI Kolkata. The recognition accuracy for Odia numerals with our proposed approach is found to be 98.5%.
Exact Scheduling to Minimize Off-Chip Data Movement for Deep Learning Accelerators

Specialized hardware accelerators are increasingly utilized to provide performance/power efficiency for Deep Neural Network (DNN) applications. However their benefits are limited by expensive off-chip data movement between host memory and the accelerator’s on-chip scratchpad, which can consume significantly more energy than accelerator computation [13]. While application-level DNN operators can have arbitrary sizes, accelerators typically support fixed-sized operations due to constrained on-chip memory and micro-architectures. Consequently, mapping an application-level operator to an accelerator involves decomposing it into loops of smaller tiles. Different choices of tile sizes, loop orders and memory partition across tensors result in a vast design space with huge differences in off-chip data movement volume. To address this challenge, we introduce Shoehorn, a schedule optimization framework that jointly optimizes loop tiling, loop ordering, and memory partitioning for mapping application-level DNN operators to hardware accelerators. Shoehorn can generate optimal schedules in subseconds and outperforms state-of-the-art approaches, reducing up to 51% total off-chip memory traffic relative to competing schedulers for several widely-used DNN applications on three distinct hardware accelerator targets.
Experimental Animal Models of Pancreatic Carcinogenesis for Prevention Studies and Their Relevance to Human Disease

Pancreatic cancer is difficult to cure, so its prevention is very important. For this purpose, animal model studies are necessary to develop effective methods. Injection of N-nitrosobis(2-oxopropyl)amine (BOP) into Syrian golden hamsters is known to induce pancreatic ductal adenocarcinomas, the histology of which is similar to human tumors. Moreover, K-ras activation by point mutations and p16 inactivation by aberrant methylation of 5′ CpG islands or by homozygous deletions have been frequently observed in common in both the hamster and humans. Thus, this chemical carcinogenesis model has an advantage of histopathological and genetic similarity to human pancreatic cancer, and it is useful to study promotive and suppressive factors. Syrian golden hamsters are in a hyperlipidemic state even under normal dietary conditions, and a ligand of peroxizome proliferator-activated receptor gamma was found to improve the hyperlipidemia and suppress pancreatic carcinogenesis. Chronic inflammation is a known important risk factor, and selective inhibitors of inducible nitric oxide synthase and cyclooxygenase-2 also have protective effects against pancreatic cancer development. Anti-inflammatory and anti-hyperlipidemic agents can thus be considered candidate chemopreventive agents deserving more attention.
Toward a detailed computational model for the mammalian circadian clock

We present a computational model for the mammalian circadian clock based on the intertwined positive and negative regulatory loops involving the Per, Cry, Bmal1, Clock, and Rev-Erb α genes. In agreement with experimental observations, the model can give rise to sustained circadian oscillations in continuous darkness, characterized by an antiphase relationship between Per/Cry/Rev-Erbα and Bmal1 mRNAs. Sustained oscillations correspond to the rhythms autonomously generated by suprachiasmatic nuclei. For other parameter values, damped oscillations can also be obtained in the model. These oscillations, which transform into sustained oscillations when coupled to a periodic signal, correspond to rhythms produced by peripheral tissues. When incorporating the light-induced expression of the Per gene, the model accounts for entrainment of the oscillations by light-dark cycles. Simulations show that the phase of the oscillations can then vary by several hours with relatively minor changes in parameter values. Such a lability of the phase could account for physiological disorders related to circadian rhythms in humans, such as advanced or delayed sleep phase syndrome, whereas the lack of entrainment by light-dark cycles can be related to the non-24h sleep-wake syndrome. The model uncovers the possible existence of multiple sources of oscillatory behavior. Thus, in conditions where the indirect negative autoregulation of Per and Cry expression is inoperative, the model indicates the possibility that sustained oscillations might still arise from the negative autoregulation of Bmal1 expression.
Multiscale homogenization with bounded ratios and anomalous slow diffusion

We show that the effective diffusivity matrix D(Vn) for the heat operator ∂t − (Δ/2 − ∇Vn∇) in a periodic potential Vn = Σ k=0nUk(x/Rk) obtained as a superposition of Hölder‐continuous periodic potentials Uk (of period 𝕋d := ℝd/ℤd, d ∈ ℕ*, Uk(0) = 0) decays exponentially fast with the number of scales when the scale ratios Rk+1/Rk are bounded above and below. From this we deduce the anomalous slow behavior for a Brownian motion in a potential obtained as a superposition of an infinite number of scales, dyt = dωt − ∇V∞(yt)dt. © 2002 Wiley Periodicals, Inc.
Reinforcement Learning-Assisted Garbage Collection to Mitigate Long-Tail Latency in SSD

NAND flash memory is widely used in various systems, ranging from real-time embedded systems to enterprise server systems. Because the flash memory has erase-before-write characteristics, we need flash-memory management methods, i.e., address translation and garbage collection. In particular, garbage collection (GC) incurs long-tail latency, e.g., 100 times higher latency than the average latency at the 99th percentile. Thus, real-time and quality-critical systems fail to meet the given requirements such as deadline and QoS constraints. In this study, we propose a novel method of GC based on reinforcement learning. The objective is to reduce the long-tail latency by exploiting the idle time in the storage system. To improve the efficiency of the reinforcement learning-assisted GC scheme, we present new optimization methods that exploit fine-grained GC to further reduce the long-tail latency. The experimental results with real workloads show that our technique significantly reduces the long-tail latency by 29--36% at the 99.99th percentile compared to state-of-the-art schemes.
Pitching-Motion: Pose-Based Pitch Trajectory Overlay System

This study reviews the current practical use of the electronic strike zone in baseball, which typically features a fixed high-low strike zone. This system deviates from the baseball rules, which dictate that the strike zone should be adjusted according to the batter's shoulder, waist, and knee positions. Consequently, This study proposes a system based on commonly used baseball game camera perspectives. Using YOLO-Pose technology, it automatically detects players' body structures. It combines them with the baseball nine-grid positioning principle to promptly establish a strike zone that complies with baseball rules and player positions. The accuracy of this system, as measured by the Intersection over Union (IoU) metric, is 0.8541, representing a 14.29% improvement over the current electronic strike zone's IoU metric of 0.7473. Considering both viewers' perspective and sports analysis requirements, the system integrates an automated pitch trajectory detection system. It can overlay multiple pitch trajectories, allowing for a visual comparison of their differences. Combined with the strike zone detection system proposed in this study, it provides a more comprehensive view of the overlap between pitch trajectories and the strike zone. This study aims to enhance baseball officiating and analysis by introducing a more accurate and visually informative system for determining the strike zone and analyzing pitch trajectories.
An Improved Hierarchical Approach for Music-to-symbolic Score Alignment

We present an efficient approach for an off-line alignment of a symbolic score to a recording of the same piece, using a statistical model. A hidden state model is built from the score, which allows for the use of two different kinds of features, namely chroma vectors and an onset detection function (spectral flux) with specific production models, in a simple manner. We propose a hierarchical pruning method for an approximate decoding of this statistical model. This strategy reduces the search space in an adaptive way, yielding a better overall efficiency than the tested state-of-the art method. Experiments run on a large database of 94 pop songs show that the resulting system obtains higher recognition rates than the dynamic programming algorithm (DTW), with a significantly lower complexity, even though the rhythmic information is not used for the alignment.
A CNN-LSTM deep neural network with technical indicators and sentiment analysis for stock price forecastings

The utilization of social media platforms, such as Twitter, has experienced a notable surge. Consequently, the sentiments expressed by both influential figures and the general public have come to exert a significant influence on public opinion. This influence extends to various domains, including the stock market, where traders are swayed by the ideas and posts disseminated through these channels. Hence, the present study seeks to improving accuracy in stock price for castings with sentiment analysis and utilization of technical indicators, thus an CNN-LSTM framework is introduced to forecast stock market with sentiment analysis and technical indicators. Three deep learning models along with technical indicators and sentiment analysis are used to forecast stock prices. The impact of time windows of different sizes; 1, 7, 14, 21, 30, 45 and 60 was compared on the performance of the model, and the best time window was selected to forecast stock prices. 4 error-based evaluation criteria have calculated. The best model to forecast the stock price was the CNN-LSTM model. The MSE of the model was 0.0011.
Optimizing fertilizer use within the context of integrated soil fertility management in Rwanda

An agro-ecological zone is a land resource mapping unit, defined in terms of climate, landform and soils, and/or land cover, and having a specific range of potentials and constraints for land use (FAO 1996). An agroecological zones (AEZ) map is an essential tool for agricultural planning. There are three regional classification schemes of AEZ commonly used in Rwanda. These were defined based on differences in soils, altitude and rainfall, and as such also show marked differences in cropping patterns, farm size, livestock ownership and other important household and regional characteristics. The most used in Rwanda is that of Clay and Dejaegher (1987), who defined five AEZ with emphasis on agronomic and socioeconomic homogeneity within AEZ among farmers and their farming systems (Figure 13.1).
Computational Argument as a Diagnostic Tool: The role of reliability

Formal and computational models of argument are ideally suited for education in ill-defined domains such as law, public policy, and science. Open-ended arguments play a central role in these areas but students of the domains may not have been taught an explicit model of argument. Computational models of argument may be ideally suited to act as argument tutors guiding students in the formation of arguments and argument analysis according to an explicit model. In order to achieve this it is important to establish that the models can be understood and evaluated reliably, an empirical question. In this paper we report ongoing work on the diagnostic utility of argument diagrams produced in the LARGO tutoring system.
Real-time estimation and dynamic renegotiation of UPC parameters for arbitrary traffic sources in ATM networks

This paper presents a robust and flexible real-time scheme for determining appropriate parameter values for usage parameter control (UPC) of an arbitrary source in an asynchronous transfer mode network. In our approach, the UPC parameters are chosen as a function of the statistical characteristics of the observed cell stream, the user's tolerance for traffic shaping, and a measure of the network cost. For this purpose, we develop an approximate statistical characterization for an arbitrary cell stream. The statistical characterization is mapped to a UPC descriptor that can be negotiated, with the network. The selected UPC descriptor is optimal in the sense of minimizing a network cost function, subject to meeting user-specified constraints on shaping delay. The UPC estimation scheme is extended to adapt to slow time-scale changes in traffic characteristics via dynamic renegotiation of the UPC parameters. We illustrate the effectiveness of our methodologies with examples taken from MPEG video sequences.
Performance Evaluation of Cryogenically Treated Worn CBN Insert by Turning Process

Machining of materials is to produce desired shape and size with smooth surfaces for the performance. Machining is carried out using various cutting tools starting high speed steel to recently developed tools like CBN and PCBN etc. These tools are used to machine difficult to cut materials like high strength alloy steels, stainless steel, Inconel 718, Titanium etc. The inserts used are thrown out or no longer required for finish machining. It can be used for rough machining where smooth surface is not primary important and subjected to subsequent machining using fresh inserts. The used inserts can be used subsequently by subjecting them cryogenic treatment at – 196◦ C in a closed chamber. It is longer process for more than 30 hours in a liquid nitrogen chamber. This treatment gives additional strength to cutting inserts to improve the cutting ability and wear resistance. The components used in high strength applications like an aerospace, automobile industries are treated with cryogenic process to improve wear strength. The operating parameters are cutting velocity, feed rate and constant depth of cut. In this research, CBN inserts after turning for 750 mm length was cryogenically treated and again used with same operating parameters as previous machining conditions. Each inserts were measured for flank wear by Scanning Electron Microscope (SEM) after treatment and re-used with same turning conditions as before. Performances of all inserts used were producing the same results or results near to same. The treated inserts were acting as fresh cutting edges. The results showed that cryogenically processed CBN inserts performed very close to previous results.