Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Future of Training Spiking Neural Networks

: Spiking Neural Networks have obtained a lot of attention in recent years due to their close depiction of brain functionality as well as their energy efficiency. However, the training of Spiking Neural Networks in order to reach state-of-the-art accuracy in complex tasks remains a challenge. This is caused by the inherent non-linearity and sparsity of spikes. The most promising approaches either train Spiking Neural Networks directly or convert existing artificial neural networks into a spike setting. In this work, we will express our view on the future of Spiking Neural Networks and on which training method is the most promising for recent deep architectures.
A Game Theoretic Approach to Attack Graphs

: An attack graph is a succinct representation of all the paths in an open system that allow an attacker to enter a forbidden state (e.g., a resource), besides any attempt of the system to prevent it. Checking system vulnerability amounts to verifying whether such paths exist. In this paper we reason about attack graphs by means of a game-theoretic approach. Precisely, we introduce a suitable game model to represent the interaction between the system and the attacker and an automata-based solution to show the absence of vulnerability.
An Integrated Assessment of Measured and Modeled Integrated Water Vapor in Switzerland for the Period 2001–03

Abstract In this paper an integrated assessment of the vertically integrated water vapor (IWV) measured by radiosonde, microwave radiometer (MWR), and GPS and modeled by the limited-area mesoscale model of MeteoSwiss is presented. The different IWV measurement techniques are evaluated through intercomparisons of GPS to radiosonde in Payerne, Switzerland, and to the MWR operated at the Institute of Applied Physics at the University of Bern in Switzerland. The validation of the IWV field of the nonhydrostatic mesoscale Alpine Model (aLMo) of MeteoSwiss is performed against 14 GPS sites from the Automated GPS Network of Switzerland (AGNES) in the period of 2001–03. The model forecast and the nudging analysis are evaluated, with special attention paid to the diurnal cycle. The results from the GPS–radiosonde intercomparison are in agreement, but with a bimodal distribution of the day-to-night basis. At 0000 UTC, the bias is negative (−0.4 kg m−2); at 1200 UTC, it is positive (0.9 kg m−2) and the variability i.
How to Write a Proof

Yet, the structure of mathematical proofs has not changed in 300 years. The proofs in Newton's Przncipia differ in style from those of a modern textbook only by being written in Latin. Proofs are still written like essays, in a stilted form of ordinary prose. Formulas written in prose, like (1), are hard to understand and hard to get right. Proofs written in prose are also hard to understand and hard to get right. Anecdotal evidence suggests that as many as a third of all papers published in mathematical journals contain mistakes not just minor errors, but incorrect theorems and proofs. Statement (2) is easier to read than statement (1) for two reasons: variables are given names, and formulas are written in a more structured fashion. The benefits of using names is obvious. The benefit of structure is less obvious; we are so used to formulas like xn + yn = zn that we tend to take their structure for granted, and to think they are easy to read just because they are short. Although the brevity of the formula helps, it is primarily its structure that makes it easier to understand than a prose version. The expression
A simple motion tracking backprojection for a class of affine transformation

Image reconstruction of dynamically deforming objects from projections and known time-dependent motion field is of interest for x-ray computed tomography. Recently, three analytical exact methods have been developed based on DFBP or DBPF algorithms which compensates for time-dependent standard affine or relaxed affine transformation. In contrast, an empirical algorithm has been proposed by Schafer, et al., which merely "trace" the motion of each voxel during the backprojection process. The method is known to be an approximation; however, it has not been discussed how good or bad the level of approximation is. In this paper, we present that a slightly modified Schafer's method (FBPx) is exact if the motion of the object can be described by a class of time-dependent affine transformation - isotropic scaling (contraction and expansion), rotation, and translation. We show mathematically and experimentally that Schafer's method is a good approximation.
A novel numerical integrator for velocity and position estimation

Velocity and position are the most important signals used in industrial controllers such as proportional–integral–derivative controllers. While in some real-time applications like structural control, acceleration measurements are easily accessible via accelerometers. The velocity and position have to be estimated from the measured acceleration. In this paper, offset cancellation and high-pass filtering techniques are combined effectively to solve common problems in numerical integration of acceleration signals in real-time applications. The integration accuracy is improved compared with other numerical integrators. Experimental results on a linear servo actuator and a shake table illustrate the effectiveness of the proposed method.
Outlier detection and ambiguity detection for microarray data in probabilistic discriminant partial least squares regression

The reject option plays an important role in the classification of microarray data. In this work, a reject option is implemented in the probabilistic discriminant partial least squares (p‐DPLS) method in order to reject to classify both outliers and ambiguous samples. Microarray data are highly susceptible to present outliers because of the many steps involved in the experimental process. During the development of the classifier, outliers in the training data may strongly influence the model and degrade its performance. Some future samples to be classified may also be outliers that will most probably be misclassified. Ambiguous samples are samples that cannot be clearly assigned to any of the classes with a high confidence. In this work, outlier detection and ambiguity detection are implemented taking into account the x‐residuals, the leverage and the predicted ŷ. The method was applied to oligonucleotide microarray data and cDNA microarray data. For the first dataset (prostate cancer data set), the outlier detection criteria allowed us to remove nine samples from the training set. The model without those samples had better classification ability, with a decrease in the classification cost per sample from 0.10 to 0.07. The method was also used in a second dataset (small round blue cell tumours of childhood dataset) to detect prediction outliers so that most of the outliers were rejected to classify and misclassifications were reduced from 100 to 5%. Copyright © 2010 John Wiley & Sons, Ltd.
Video-Based Sign Language Digit Recognition for the Thai Language: A New Dataset and Method Comparisons

: Video-based sign language recognition aims to support deaf people, so they can communicate with others by assisting them to recognise signs from video input. Unfortunately, most existing sign language datasets are limited to a small vocabulary, especially in low-resource languages such as Thai. Recent research in the Thai community has mostly paid attention to building recognisers from static input with limited datasets, making it difﬁcult to train machine learning models for practical applications. To overcome this limitation, this paper originally introduces a new video database for automatic sign language recognition for Thai sign language digits. Our dataset has about 63 videos for each of the nine digits and is performed by 21 signers. Preliminary baseline results for this new dataset are presented under extensive experiments. Indeed, we implement four deep-learning-based architectures: CNN-Mode, CNN-LSTM, VGG-Mode, and VGG-LSTM, and compare their performances under two scenarios: (1) the whole body pose with backgrounds, and (2) hand-cropped images only as pre-processing. The results show that VGG-LSTM with pre-processing has the best accuracy for our in-sample and out-of-sample test datasets.
Total lithography system based on a new application software platform enabling smart scanner management

Along with device shrinkage, higher accuracy will continuously be required from photo-lithography tools in order to enhance on-product yield. In order to achieve higher yield, the advanced photo-lithography tools must be equipped with sophisticated tuning knobs on the tool and with software that is flexible enough to be applied per layer. This means photo-lithography tools must be capable of handling many types of sub-recipes and parameters simultaneously. To enable managing such a large amount of data easily and to setup lithography tools smoothly, we have developed a total lithography system called Litho Turnkey Solution based on a new software application platform, which we call Plug and Play Manager (PPM). PPM has its own graphical user interface, which enables total management of various data. Here various data means recipes, sub-recipes, tuning-parameters, measurement results, and so on. Through PPM, parameter making by intelligent applications such as CDU/Overlay tuning tools can easily be implemented. In addition, PPM is also linked to metrology tools and the customer’s host computer, which enables data flow automation. Based on measurement data received from the metrology tools, PPM calculates correction parameters and sends them to the scanners automatically. This scheme can make calibration feedback loops possible. It should be noted that the abovementioned functions are running on the same platform through a user-friendly interface. This leads to smart scanner management and usability improvement. In this paper, we will demonstrate the latest development status of Nikon’s total lithography solution based on PPM; describe details of each application; and provide supporting data for the accuracy and usability of the system. Keywords: exposure
ORYZALIN-INDUCED TARO (COLOCASIA ESCULENTA L.) TETRAPLOID AND DIPLOID ASSESSMENT FOR GROWTH AND AGRONOMIC TRAITS

Taro (Colocasia esculenta L. cv. Pontianak) is a popular and widely cultivated cultivar in the Indonesian farming community. In taro Pontianak, manipulating the ploidy level by chromosome doubling can enhance its genetic diversity. The latest study aimed to evaluate the oryzalin-induced polyploid taro cv. Pontianak compared with its diploid in terms of growth, anatomical, cytological, morphological, and agronomic traits in the greenhouse and the field. In vitro shoots of taro cv. Pontianak were soaked using a liquid medium supplemented with oryzalin with a concentration of 30 and 60 μM for one day. After five times of subcultures, flow cytometry analysis proceeded to determine their ploidy levels. The diploid (2n = 2x) and tetraploid (2n = 4x) plantlets were then acclimatized in the greenhouse for six to eight weeks and hardened for two months before planting in the field for 10 months. Squashing the root tip to ensure the ploidy levels of taro seedlings helped measure the chromosome numbers. Observing the growth, anatomical, and agronomic traits of diploid and tetraploid plants occurred during the hardening process until harvest time. The 30 μM oryzalin treatment produced tetraploid and mixoploid shoots; none from the 60 μM treatment. Oryzalininduced tetraploid taro exhibited some morphological characteristics of tetraploid plants, such as, longer stomata size and low density. Tubers of tetraploid plants tended to have increased protein, ash, and water content compared with their diploid counterpart. However, tetraploid taro showed a slower growth rate both in the greenhouse and the field. Additionally, it gave lower yields than diploids in some agronomic characteristics, including delayed flowering time, lower plant fresh weight, and smaller tubers. This study provides information that oryzalin-induced tetraploid taro could exhibit increasing and decreasing nutritional-agronomic traits (as mentioned above) compared with its diploid.
RECENT THEORETICAL AND METHODOLOGICAL DEVELOPMENTS IN MULTIDIMENSIONAL SCALING AND ITS RELATED METHODS IN JAPAN

The present paper is a review of recent theoretical and methodological developments in multidimensional scaling and its related methods in Japan. The term multidimensional scaling (MDS) is used in two senses. One is a narrow sense, and the other is a wider sense. The former is a technical term which is divided into metric MDS and non-metric MDS. The latter has many variations in multidimensional data analysis. In this review, MDS in a narrow sense and its closely related methods, which are a part of MDS in a wider sense, are taken into consideration. And Japanese works are introduced.
Phase errors elimination in compact digital holoscope (CDH) based on a reasonable mathematical model

In the compact digital holoscope (CDH) measurement process, theoretically, we need to ensure the distances between the reference wave and object wave to the hologram plane exactly match. However, it is not easy to realize in practice due to the human factors. This can lead to a phase error in the reconstruction result. In this paper, the strict theoretical analysis of the wavefront interference is performed to demonstrate the mathematical model of the phase error and then a phase errors elimination method is proposed based on the advanced mathematical model, which has a more explicit physical meaning. Experiments are carried out to verify the performance of the presented method and the results indicate that it is effective and allows the operator can make operation more flexible.
FakeRevealer: A Multimodal Framework for Revealing the Falsity of Online Tweets Using Transformer-Based Architectures

: As the Internet has evolved, the exposure and widespread adoption of social media concepts have altered the way news is formed and published. With the help of social media, getting news is cheaper, faster, and easier. However, this has also led to an increase in the number of fake news articles, either by manipulating the text or morphing the images. The spread of fake news has become a serious issue all over the world. In one case, at least 20 people were killed just because of false information that was circulated over a social media platform. This makes it clear that social media sites need a system that uses more than one method to spot fake news stories. To solve this problem, we’ve come up with FakeRevealer, a single-configuration fake news detection system that works on transfer learning based techniques. Our multi-modal archutecture understands the textual features using a language transformer model called DistilRoBERTa and image features are extracted using the Vision Transformer (ViTs) that is pre-trained on ImageNet 21K. After feature extraction, a cosine similarity measure is used to fuse both the features. The evaluation of our proposed framework is done over publicly available twitter dataset and results shows that it outperforms current state-of-art on twitter dataset with an accuracy of 80.00% which is 2.23%more, that than the current state-of-art on twitter dataset.
Thermocapillary and centrifugal-buoyancy-driven motion in a rapidly rotating liquid cylinder

The thermocapillary flow field in a uniformly rotating liquid cylinder heated from above is calculated using linear boundary-layer theory appropriate for small values of the Ekman number. The results show that the thermocapillary flow is confined to a thin layer at the liquid-gas interface if the temperature difference across the cylinder is sufficiently small. The interior flow is a uniform rotation with the endplates. The flow due to centrifugal buoyancy is also analysed using the same theory. The magnitude of this flow compared with the thermocapillary motion is small in typical circumstances. However, it does influence the temperature field in the interior of the cylinder, whereas the thermocapillary motion does not. Full details of these flows and the first-order corrections to the interface shape are presented.
Can Pupillary Responses while Listening to Short Sentences Containing Emotion Induction Words Explain the Effects on Sentence Memory?

: In content viewing activities, such as movies and paintings, it is important to retain and utilize the viewing experience in memory. We have been studying the effect of the content of visual and auditory information provided during viewing activities and presentation timing on content memory. We have clarified the appropriate timing of presenting visual information that should be supplemented by auditory information. We have also found that the inclusion of emotion induction words in the auditory information is effective in forming content memory. In this study, we present a framework for examining the effects of emotion-evoking characteristics of short sentences while taking into account individual differences in memory. Subjects were presented with a short sentence with an emotion-inducing word at the beginning of the sentence, in which the impression of the entire short sentence would appear at the end of the sentence. We designed an experimental system to clarify the relationship between subject-specific pupillary responses to the emotion induction words and memory for short sentences. Our findings indicate a scheme that relates the pupillary response to short sentence memory.
SENSITIVITY ANALYSIS AND IMPACT OF AN IMPERFECT VACCINE OF TWO STRAINS OF HEPATITIS B VIRUS INFECTION

A mathematical model considering two strains of hepatitis B virus (HBV) chronic carriers, to assess the impact of dose-structured imperfect vaccine, in a population, is designed and analyzed. The model is shown to have a locally and globally asymptotically stable disease-free equilibrium (DFE) whenever its associated reproduction number is numerically less than unity. Numerical analysis of the model shows that with the expected [Formula: see text] minimum efficacy of the first vaccine dose, vaccinating [Formula: see text] of the susceptible population with the first vaccine dose will be sufficient to effectively control the spread of hepatitis B infection. Such effective control can also be achieved if [Formula: see text] of the first vaccine dose recipients take the second dose. Threshold analysis reveals that an imperfect HBV vaccine should have positive or negative population-level effect. Latin hypercube sampling–PRCC analysis illustrates that disease transmission rate, birth rate, natural death rate and proportion of children born with maternal immunity are most influential parameters in the disease dynamics. In this paper, the sensitivity analysis based on mathematical and in addition statistical techniques have been performed to determine the significance of the model parameters. It is observed that a number of the parameters play an important role to determine the magnitude of the basic reproduction number. Sensitivity analysis is achieved to determine model parameters’ importance in disease dynamics. It is observed that the reproduction number is the most responsive quantity to the potent transmission rate of HBV and in addition also vital to control the spread of the disease.
CT-Based Automatic Spine Segmentation Using Patch-Based Deep Learning

CT vertebral segmentation plays an essential role in various clinical applications, such as computer-assisted surgical interventions, assessment of spinal abnormalities, and vertebral compression fractures. Automatic CT vertebral segmentation is challenging due to the overlapping shadows of thoracoabdominal structures such as the lungs, bony structures such as the ribs, and other issues such as ambiguous object borders, complicated spine architecture, patient variability, and fluctuations in image contrast. Deep learning is an emerging technique for disease diagnosis in the medical field. This study proposes a patch-based deep learning approach to extract the discriminative features from unlabeled data using a stacked sparse autoencoder (SSAE). 2D slices from a CT volume are divided into overlapping patches fed into the model for training. A random under sampling (RUS)-module is applied to balance the training data by selecting a subset of the majority class. SSAE uses pixel intensities alone to learn high-level features to recognize distinctive features from image patches. Each image is subjected to a sliding window operation to express image patches using autoencoder high-level features, which are then fed into a sigmoid layer to classify whether each patch is a vertebra or not. We validate our approach on three diverse publicly available datasets: VerSe, CSI-Seg, and the Lumbar CT dataset. Our proposed method outperformed other models after configuration optimization by achieving 89.9% in precision, 90.2% in recall, 98.9% in accuracy, 90.4% in F-score, 82.6% in intersection over union (IoU), and 90.2% in Dice coefficient (DC). The results of this study demonstrate that our model’s performance consistency using a variety of validation strategies is flexible, fast, and generalizable, making it suited for clinical application.