Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Brain Tumor Detection using Classification Techniques

A brain tumor is a cancerous growth of irregular cells inside the brain. In the beginning, the tumor grows in the tissues of the brain. These kinds of cancers metastasizes the brain tissues inside the body from one place to another place. There is no limit or range in terms of human age it can occur to anyone. Bio-medical images are developed and used in various techniques such as X-ray, MRI, and CT-experiments. In this paper, we present various procedures used for mechanically identifying brain tumors. The distinctive pathological kinds with five primary steps used in every method are as pre-processing, segmentation, area detection, function extraction, and class. three one-of-a-kind strategies are used which might be based totally on classification inclusive of FA-MB-PNN, FFBNN-PNN, and MBAT-PNN. Within the proposed strategies are used in conjunction with a hard and fast of pictures that can be used for analyzing the outcomes for the proposed brain tumor classification machine. As a result, the proposed brain tumor type system gives a tremendous tempo of accuracy, sensitivity, and specificity
HUMAN ACTIVITY PROFILE TRACKING USING STATIC ANALYSIS OF BINARY CODE ACCESS PATTERNS FOR FREEZE-SAFE IOT SYSTEMS

IoT-driven systems perform human-interactive services based on communications that is constructed with irregular links between heterogeneous things. These links are major causes of propagating unwanted errors or states in entire systems. This paper focuses on reducing the number of possible cases of unwanted system halts by quickly propagating abnormal freezing statuses. Our approach is based on the early detection and mitigation of the uncontrollable error injections that cause these potential errors. Because human activity is always tightly coupled with internal software, the binary access patterns in software execution, which is triggered by human activity, can be abstracted with user-defined profile data. The expected profile, which is recorded in the pre-simulation step, will be compared with the runtime monitoring results of the state transitions. The proposed method is applied to the next-generation prototype of commercial devices, showing capability as case study toward freeze-safe IoT applications.
Current Policy Options to Promote Economic Transition in China

From the perspective of economic development history for most countries, it is a common phenomenon that industrialization starts from extensive mode. Under the background of current new urbanization in China, economic transition faces more severe challenges from the population, resources, regional development and other aspects. How to rapidly shift from extensive mode to resource-saving mode is the urgent task of present economic works. Unfortunately, considering China’s actual situation, it is unlikely to be achieved in the short term. Based on the analysis of difficulties and obstacles in economic transition, this paper puts forward strategy options to accelerate economic transition and reasonable suggestions on institutional innovation.
A novel coenzyme-Q approach for the prevention of postsurgical adhesion.

Objectives Postoperative intraperitoneal adhesions are an unsolved and important problem in abdominal surgery. In the present study, the probable preventive role of coenzyme-Q in the development of peritoneal adhesions was investigated. Material and Methods Sixteen Wistar Hannover male rats weighing 300-350 g were randomly separated into two groups of 8 rats each. The cecum was abraded with a sterile gauze until sub-serosal hemorrhage developed. A patch of peritoneum located opposite to the cecal abrasion was completely dissected. No treatment was given to Group 1. Group 2 received 30 mg/kg coenzyme-Q, which was injected 2 mL intraperitoneally. All the rats were sacrificed on the postoperative 21st day, and after adhesions were scored macroscopically, tissue specimens of the peritoneum and bowel were subjected to histopathological investigation. Tissue and blood specimens were also taken for biochemical analysis to investigate antioxidant efficiency. Results Adhesion scores were significantly different between the control group and the coenzyme-Q group (p= 0.001). According to the tissue levels of GSH-Px, MDA, and SOD levels, there was no significant difference between the study groups (p= 0.074, p= 0.208, p= 0.526). According to the plasma GSH-Px and SOD levels, there was significant difference between the groups (p= 0.002, p= 0.001), but the difference was not significant at MDA levels (p= 0.793). The differences between the pathological scores of the control and coenzyme-Q (p= 0.028 for fibrosis; p= 0.025 for inflammation) groups were statistically significant. Conclusion This study confirms that coenzyme-Q is the potential application in the prevention of early postoperative adhesions.
post-retrieval inhibition in sequential memory search 1 Running head : post-retrieval inhibition in sequential memory search Post-retrieval Inhibition in Sequential Memory Search

Sequential behavior is observed in various domains of cognitive psychology, including free recall paradigms. In this chapter, within a neurocomputational framework resampling (RS) mechanisms are compared with competitive queuing (CQ) mechanisms. While both types of implementations select the most active representation, the subsequent inhibition is at the level of selection for RS models and at the level of (re)activation for CQ models. It is shown that despite the overwhelming success of CQ models in serial recall (with regard to types of sequencing error), RS models outperform CQ models with regard to inter-response times in a free recall task. Additional analyses show that decay of response suppression reduces the difference between the models. The RS model is sensitive to the size of the search set and accounts for memory selection performance in patients with Alzheimer’s dementia or Huntington’s disease.
Improved Method for License Plate Recognition for Indian Road Conditions

The paper is presenting and evaluating the proposed methods used in LPR system against existing methods in order to justify the efficiency of proposed methods. These methods later may be used for the applications like traffic control; vehicle owner identification etc. This proposed approach split the LPR applications into three major categories: access control (AC), law enforcement (LE), and road patrol (RP). Each category is characterized by variables of different variation scopes. This proposed method consists of three modules for plate detection, character segmentation, and recognition. In this paper an additional null class is introduced for improving character recognition rate.In this papar, this proposed solution will targeted the Indian datasets. In this paper the samples from India, from various locations, time, traffic, and weather condition. During the practical evaluation measure the performance of each module and compare it against the existing technique in order to claim efficiency of proposed solution
Simulation Comparisons of Vehicle-Based and Movement-Based Traffic Control for Autonomous Vehicles at Isolated Intersections

With the advent of autonomous driving technologies, traffic control at intersections is expected to experience revolutionary changes. Various novel intersection control methods have been proposed in the existing literature, and they can be roughly divided into two categories: vehicle-based traffic control and movement-based traffic control. Movement-based traffic control can be treated as updated versions of the current intersection signal control with the incorporation of the performance of autonomous vehicle functions. Meanwhile, vehicle-based traffic control utilizes some brand-new methods, mostly in real-time fashion, to organize traffic at intersections for safe and efficient vehicle passages. However, to date, no systematic comparison between these two control categories has been performed to suggest their advantages and disadvantages. This paper conducts a series of numerical simulations under various traffic scenarios to perform a fair comparison of their performances. Specifically, we allow trajectory adjustments of incoming vehicles under movement-based traffic control, while for its vehicle-based counterpart, we implement two strategies, i.e., the first-come-first-serve strategy and the optimization-based strategy. Overall, the simulation results show that vehicle-based traffic control generally incurs a negligible delay when traffic demand is low but lead to an excessive queuing time as the traffic volume becomes high. We also discover that the comparison results can be influenced by other factors such as intersection layouts, traffic distribution and maturity of the autonomous driving technologies.
Parameter Sharing Exploration and Hetero-Center Triplet Loss for Visible-Thermal Person Re-Identification

This paper focuses on the visible-thermal cross-modality person re-identification (VT Re-ID) task, whose goal is to match person images between the daytime visible modality and the nighttime thermal modality. The two-stream network is usually adopted to address the cross-modality discrepancy, the most challenging problem for VT Re-ID, by learning the multi-modality person features. In this paper, we explore how many parameters a two-stream network should share, which is still not well investigated in the existing literature. By splitting the ResNet50 model to construct the modality-specific feature extraction network and modality-sharing feature embedding network, we experimentally demonstrate the effect of parameter sharing of two-stream network for VT Re-ID. Moreover, in the framework of part-level person feature learning, we propose the hetero-center triplet loss to relax the strict constraint of traditional triplet loss by replacing the comparison of the anchor to all the other samples by the anchor center to all the other centers. With extremely simple means, the proposed method can significantly improve the VT Re-ID performance. The experimental results on two datasets show that our proposed method distinctly outperforms the state-of-the-art methods by large margins, especially on the RegDB dataset achieving superior performance, rank1/mAP/mINP 91.05%/83.28%/68.84%. It can be a new baseline for VT Re-ID, with a simple but effective strategy.
Effects of cattle trampling and mechanical seedbed preparation on grass seedling emergence.

Cattle trampling has been recommended to bury seeds and encourage seedling establishment but has not been compared with traditional seedbed preparation techniques. We compared seedling emergence of broadcast-seeded 'Vaughn' sideoats grams [Bouteloua curtipendula (Michx.) Torr], 'A-130' blue panic (Panicum antidotale Retz.), 'A-68' Lehmann lovegrass (Eragrostis lehmanniana Nees), and 'Cochise' atherstone lovegrass (Eragrostis lehmanniana Nees X E. tricophera Coss and Dur.) on lightly and heavily trampled seedbeds with that on undisturbed, land imprinted, and root-plowed or ripped seedbeds. We seeded and applied the treatments prior to summer rains on a sandy loam soil in southern Arizona for 3 years. In a wet year (1987) when surface soil water was estimated to be available for at least 24 consecutive days, heavy trampling and land imprinting increased emergence of blue panic and land imprinting increased emergence of Cochise lovegrass. In that year, lovegrass emergence was high even on undisturbed plots. In a moderately wet year (1988), surface soil water was available for periods of 6-9 days during seedling emergence and greater disturbance, either by heavy trampling, land imprinting and/or root plowing or ripping produced higher emergence than light trampling and nondisturbance. In a dry year (1989), surface soil water was available for periods of 2-3 days and seedling emergence was low and generally similar for all treatments. Sideoats grama emergence was low all 3 years, but was highest in 1988 when initial thunderstorms were followed closely by subsequent storms. Seedbed disturbance by cattle and mechanical methods may enhance revegetation in the Southwest in years of moderate precipitation but may be unnecessary in wet years or futile in dry years, depending on species and soils.
Divergence-free Scott-Vogelius elements on curved domains

We construct and analyze an isoparametric finite element pair for the Stokes problem in two dimensions. The pair is defined by mapping the Scott-Vogelius finite element space via a Piola transform. The velocity space has the same degrees of freedom as the quadratic Lagrange finite element space, and therefore the proposed spaces reduce to the Scott-Vogelius pair in the interior of the domain. We prove that the resulting method converges with optimal order, is divergence--free, and is pressure robust. Numerical examples are provided which support the theoretical results.
Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems

Task-oriented dialogue systems use four connected modules, namely, Natural Language Understanding (NLU), a Dialogue State Tracking (DST), Dialogue Policy (DP) and Natural Language Generation (NLG). A research challenge is to learn each module with the least amount of samples (i.e., few-shots) given the high cost related to the data collection. The most common and effective technique to solve this problem is transfer learning, where large language models, either pre-trained on text or task-specific data, are fine-tuned on the few samples. These methods require fine-tuning steps and a set of parameters for each task. Differently, language models, such as GPT-2 (Radford et al., 2019) and GPT-3 (Brown et al., 2020), allow few-shot learning by priming the model with few examples. In this paper, we evaluate the priming few-shot ability of language models in the NLU, DST, DP and NLG tasks. Importantly, we highlight the current limitations of this approach, and we discuss the possible implication for future work.
Predicting Event Time by Classifying Sub-Level Temporal Relations Induced from a Unified Representation of Time Anchors

Extracting event time from news articles is a challenging but attractive task. In contrast to the most existing pair-wised temporal link annotation, Reimers et al.(2016) proposed to annotate the time anchor (a.k.a. the exact time) of each event. Their work represents time anchors with discrete representations of Single-Day/Multi-Day and Certain/Uncertain. This increases the complexity of modeling the temporal relations between two time anchors, which cannot be categorized into the relations of Allen's interval algebra (Allen, 1990). In this paper, we propose an effective method to decompose such complex temporal relations into sub-level relations by introducing a unified quadruple representation for both Single-Day/Multi-Day and Certain/Uncertain time anchors. The temporal relation classifiers are trained in a multi-label classification manner. The system structure of our approach is much simpler than the existing decision tree model (Reimers et al., 2018), which is composed by a dozen of node classifiers. Another contribution of this work is to construct a larger event time corpus (256 news documents) with a reasonable Inter-Annotator Agreement (IAA), for the purpose of overcoming the data shortage of the existing event time corpus (36 news documents). The empirical results show our approach outperforms the state-of-the-art decision tree model and the increase of data size obtained a significant improvement of performance.
Visualizing conical intersection passages via vibronic coherence maps generated by stimulated ultrafast X-ray Raman signals

Significance The capabilities of modern X-ray sources have substantially increased the temporal and spectral resolutions for the observation of elementary molecular events. Theoretically, it has been shown that these unique features can be exploited to probe vibronic coherences that emerge when molecules pass through conical intersections. Here, we demonstrate this technique for the photorelaxation of the RNA-nucleobase uracil after UV excitation. The generated coherence maps reveal the path of the nuclear wavepacket on the excited-state surface in the vicinity of the conical intersection. This information is directly accessible from the spectroscopically recorded signal. Being transferrable to other molecules, these coherence maps will provide detailed visualizations of other nonadiabatic passages in the future. The rates and outcomes of virtually all photophysical and photochemical processes are determined by conical intersections. These are regions of degeneracy between electronic states on the nuclear landscape of molecules where electrons and nuclei evolve on comparable timescales and thus become strongly coupled, enabling radiationless relaxation channels upon optical excitation. Due to their ultrafast nature and vast complexity, monitoring conical intersections experimentally is an open challenge. We present a simulation study on the ultrafast photorelaxation of uracil, based on a quantum description of the nuclei. We demonstrate an additional window into conical intersections obtained by recording the transient wavepacket coherence during this passage with an X-ray free-electron laser pulse. Two major findings are reported. First, we find that the vibronic coherence at the conical intersection lives for several hundred femtoseconds and can be measured during this entire time. Second, the time-dependent energy-splitting landscape of the participating vibrational and electronic states is directly extracted from Wigner spectrograms of the signal. These offer a physical picture of the quantum conical intersection pathways through visualizing their transient vibronic coherence distributions. The path of a nuclear wavepacket in the vicinity of the conical intersection is directly mapped by the proposed experiment.
ConsNet: Learning Consistency Graph for Zero-Shot Human-Object Interaction Detection

We consider the problem of Human-Object Interaction (HOI) Detection, which aims to locate and recognize HOI instances in the form of in images. Most existing works treat HOIs as individual interaction categories, thus can not handle the problem of long-tail distribution and polysemy of action labels. We argue that multi-level consistencies among objects, actions and interactions are strong cues for generating semantic representations of rare or previously unseen HOIs. Leveraging the compositional and relational peculiarities of HOI labels, we propose ConsNet, a knowledge-aware framework that explicitly encodes the relations among objects, actions and interactions into an undirected graph called consistency graph, and exploits Graph Attention Networks (GATs) to propagate knowledge among HOI categories as well as their constituents. Our model takes visual features of candidate human-object pairs and word embeddings of HOI labels as inputs, maps them into visual-semantic joint embedding space and obtains detection results by measuring their similarities. We extensively evaluate our model on the challenging V-COCO and HICO-DET datasets, and results validate that our approach outperforms state-of-the-arts under both fully-supervised and zero-shot settings.
Analytical bounds on the local Lipschitz constants of affine-ReLU functions

In this paper, we determine analytical bounds on the local Lipschitz constants of of affine functions composed with rectified linear units (ReLUs). Affine-ReLU functions represent a widely used layer in deep neural networks, due to the fact that convolution, fully-connected, and normalization functions are all affine, and are often followed by a ReLU activation function. Using an analytical approach, we mathematically determine upper bounds on the local Lipschitz constant of an affine-ReLU function, show how these bounds can be combined to determine a bound on an entire network, and discuss how the bounds can be efficiently computed, even for larger layers and networks. We show several examples by applying our results to AlexNet, as well as several smaller networks based on the MNIST and CIFAR-10 datasets. The results show that our method produces tighter bounds than the standard conservative bound (i.e. the product of the spectral norms of the layers' linear matrices), especially for small perturbations.
An empirical model on the dynamics of Covid-19 spread in human population

We propose a mathematical model to analyze the time evolution of the total number of infected population with Covid-19 disease at a region in the ongoing pandemic. Using the available data of Covid-19 infected population on various countries we formulate a model which can successfully track the time evolution from early days to the saturation period in a given wave of this infectious disease. It involves a set of effective parameters which can be extracted from the available data. Using those parameters the future trajectories of the disease spread can also be projected. A set of differential equations is also proposed whose solutions are these time evolution trajectories. Using such a formalism we project the future time evolution trajectories of infection spread for a number of countries where the Covid-19 infection is still rapidly rising.
On the second Gegenbauer moment of $\rho$-meson distribution amplitude

Using the soft pion theorem, crossing, and the dispersion relations for the two pion distribution amplitude ($2\pi$DA) we argue that the second Gegenbauer moment the $\rho$-meson DA ($a_2^{(\rho)}$) most probably is negative. This result is at variance with the majority of the model calculations for $a_2^{(\rho)}$. Using the instanton theory of the QCD vacuum, we computed $a_2^{(\rho)}$ at a low normalisation point and obtain for the ratio $ a_2^{(\rho)}/M_3^{(\pi)}$ definitely negative value in the range of $a_2^{(\rho)}/M_3^{(\pi)}\in [-2, -1]$. The range of values corresponds to a generous variation of the parameters of the instanton vacuum. The value of the second Gegenbauer moment of pion DA is positive in the whole range and is compatible with its the most advanced lattice measurement. It seems that the topologically non-trivial field configurations in the QCD vacuum (instantons) lead to qualitatively different shapes of the pion and the $\rho$-meson DAs.
Time in Blockchain-Based Process Execution

The traceable execution of business processes and choreographies using smart contracts is one prominent application of blockchain technology in Business Process Management (BPM). Existing approaches support a large set of patterns, modeling languages, and blockchain architectures, which cover a wide range of practical scenarios. However, they largely neglect the important aspect of time, a crucial part of process and choreography models manifested in deadlines, delays, and other temporal constraints. We argue that this deficit is due to inherent limitations of smart contracts—in particular the absence of a natural notion of measuring time—on popular blockchain platforms used in research and practice. We introduce a set of time measures available on blockchain platforms to alleviate these issues, and systematically compare their properties. We also give hints as to their suitability for facilitating various temporal constraints commonly found in process models.