Miracle-dz/UniFAR-FTUs huggingface dataset:
Super Resolve Dynamic Scene from Continuous Spike Streams

Recently, a novel retina-inspired camera, namely spike camera, has shown great potential for recording high-speed dynamic scenes. Unlike conventional digital cameras that compact the visual information within an exposure interval into a single snapshot, the spike camera continuously outputs binary spike streams to record the dynamic scenes, yielding a very high temporal resolution. Most of the existing reconstruction methods for spike camera focus on reconstructing images with the same resolution as spike camera. However, as a trade-off of high temporal resolution, the spatial resolution of spike camera is limited, resulting in inferior details of the reconstruction. To address this issue, we develop a spike camera super-resolution framework, aiming to super resolve high-resolution intensity images from the low-resolution binary spike streams. Due to the relative motion between the camera and the objects to capture, the spikes fired by the same sensor pixel no longer describes the same points in the external scene. In this paper, we exploit the relative motion and derive the relationship between light intensity and each spike, so as to recover the external scene with both high temporal and high spatial resolution. Experimental results demonstrate that the proposed method can reconstruct pleasant high-resolution images from low- resolution spike streams.
Sedimentary Processes Influencing Divergent Wetland Evolution in the Hudson River Estuary

iv LIST OF TABLES.ix LIST OF FIGURES.x CHAPTER 1 1: EMERGENT AQUATIC VEGETATION MODULATES SEDIMENT TRANSPORT AND TRAPPING IN FRESHWATER TIDAL WETLANDS.1 1.
Comparative Analysis on Voltage Reference Circuits

Abstract : In the modern world the low power supply voltage circuits are major attractable to the most extent because of the long battery runtime for the portable devices like the mobiles, laptops etc. the reduction of the power supply voltage has became the one of the most challenging issue in the VLSI technology industries. So voltage reference circuits are used to reduce the supply voltage because they can operate in the low supply voltage. The voltage reference circuits play an important role in the analog systems. The voltage reference circuit is a simple device having the one simple functionality that is it generates the exact output voltage without depending on the factors like input supply voltage, load current and the temperature change. The voltage reference provides the recommended output voltage which is needed by the circuit for its required measurements. In this paper the different types of voltage reference circuits are discussed and the literature review of the technologies used in different voltage reference circuits have been performed and comparison of different technologies have been performed.
ADC for IoT Systems

Nowadays, the designers of analog circuits face problems due to the ever-decreasing device sizes, which require the system voltage to decrease. Among other limitations, this leads to a reduced input voltage swings and linearity problems. On the other side of the coin, technology scaling allows the digital cells, such as logic gates and oscillators, to show faster propagation times. Therefore, in the analog design arises a new approach, in which a Time-Domain Quantization replaces the classical Voltage Domain Quantization. There are two diﬀerent ways to implement Time-Domain Quantization: one way uses a Voltage Controlled Oscillator to convert the input signal from the voltage domain to the frequency and then quantify that frequency; the other uses a Voltage-to-Time Converter to produce two digital signals. The time diﬀerence between the digital signals is proportional to the input signal amplitude. Then, a Time to Digital Converter quantiﬁes that time interval. In this dissertation, an Analog-to-Digital Converter was designed, with time interval based Time Domain Quantization, through a time-domain folding approach. It has a 7 bit resolution, operates at 2.5GS/s, and features 6.32 ENOB in typical conditions. This ADC consumes 33.6 mW, leading to a Walden Figure of Merit of FOM W = 168 . fJ/conv − step .
Fish ecology in Lake Banyoles

Lake Banyoles is the second largest lake in the Iberian Peninsula and due to this relative uniqueness, its peculiar geological origin, and its considerable age attracted the early attention of the late Professor Ramon Margalef. One of the first papers by Margalef was on the biota of Lake Banyoles and two of the first four Ph.D. theses that he supervised were about the limnology of this lake. Unfortunately, the uniqueness of this lake also implied that it was the first place of introduction into the Iberian Peninsula of several exotic fish species, early in the XXth Century and nowadays the lake fish assemblage is dominated by invasive species, and some native ones have been extirpated. Although the limnological studies in Lake Banyoles were pioneering within the Iberian Peninsula, studies on fish ecology of the lake did not start until 1989. Thereafter, four Ph.D. theses have been completed on different aspects of the fish assemblages of Lake Banyoles. The aim of this paper is to provide a short overview of this research, largely brought about by the considerable limnological information previously available for this lake, thanks to the insightful, pioneering work of Professor Margalef.
Neural Network Model for Optimization of Cold-Formed Steel Beams

An important advantage of cold-formed steel is the greater flexibility of cross-sectional shapes and sizes available to the structural steel designer. However, the lack of standard optimized shapes makes the selection of the most economical shape very difficult if not impossible. The task is further complicated by the complex and highly nonlinear nature of the rules that govern their design. A general mathematical formulation and computational model is presented for optimization of cold-formed steel beams. The nonlinear optimization problem is solved by adapting the robust neural dynamics model developed recently. The basis of the design can be American Iron and Steel Institute (AIS), allowable stress design (ASD), or load and resistance factor design (LRFD) specifications. The computational model has been applied to three different commonly used types of cross-sectional shapes: hat-, I-, and Z-shapes. The robustness and generality of the approach have been demonstrated by application to three different examples. This research lays the mathematical foundation for automated optimum design of structures make of cold-formed shapes. The result would be more economical use of cold-formed steel.
Supplementary of “FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search”

Figure 4 details the supernet training stage of our approach. In fact, it’s inherently efficient regarding GPU utilization. Even on powerful machines such as Tesla V100, it can make full use of GPU without special optimization. As most of the existing deep learning frameworks allow paralleled execution between data generation and gradient calculation, our algorithm can exploit this parallelism to the extreme since a mini-batch of data is reused by m times of backpropagation. The GPUs are always busy because the data is ready whenever required, which shortens the training time. Moreover, our method works in a single-path way, which is memory friendly. Irregular Search Spaces. Note that SF in the paper can be easily extended by a preprocessing function in case of irregular search spaces (i.e. the number of operations are not the same for each layer). We only need to make a minor modification of Algorithm 1. Say the l-th layer has ml choices. Suppose M = max(ml), we randomly choose M −ml extra operations from ml choices and regard these extra options as different ones from the original search space. Therefore, the input condition of Algorithm 1 still hold and we can use it directly. This procedure can be regarded as an approximated SF. However, perfect SF for irregular cases remains as our future work.
The photon structure in interference processes , quantum entanglement and self-organized cosmos

The standard framework of cosmology requires evaluating the quantum entanglement of primordial Planck bosons at a same quantum state. A thermodynamic feedback becomes evidence trough the Hubble‟s law emanating from the comoving volume, responding to the coupling of the flow energy with the imprinted dimensionality of the space-time. The pair formation in which coherence between same quantum states will manifested in an oscillatory superposition. Therefore, two almost simultaneous positions displace a wavelength value. Thus, a small identical time shapes space-time, separating the event-distance between the interactions of two photons. Hence, the energy flow at the microscopic level aligns the evolution into homogeneous phases, indicating the presence of a regulatory process over expansion connected to the universe age under flatness. The distension-contraction due to the interference of the pair of photons (or electrons) involves a sum of angular moments, at the instant of the vector re-configuration of the electric and magnetic fields. Therefore, the transitory wavelength of the photon pair superposition can be characterized by two exclusive states: constructive (distension of the E field) or destructive (contraction of the E field). Quantum entanglement acts as a dissipative coherence-decoherence oscillatory potential, with momentum conservation between rest and inertial mass. Thus, this moderates the increase in entropy as a function of the bottleneck that is the velocity of light: c, and preserving the balance between open and closed curvatures, that is, flatness of the expansion.
MASS ESTIMATION BASED ON GENERATIVE CLASSIFIER

: The aim to build generative classifiers is to estimate the joint probability p(x, y) indirectly, estimating the conditional likelihood p(x|y) and the prior probability p(y). To predict the likely class that maximizes the posterior probability p(y|x) using Bayes rule. The paper propose a generative classifier which estimates the joint distribution directly through a data modelling mechanism called mass estimation. The generative classifier makes prediction based on decision rule that maximizes mass, better than Bayes rule
A Study on Role of Intelligent Decision Support Systems in Healthcare

: The objective of the paper is to summarize the applications of Intelligent Decision Support Systems (IDSS) in Healthcare Monitoring Systems (HMS), that too mainly in monitoring the heart diseases and providing necessary remedial actions. The Decision Support Systems (DSS) are capable of handling the complex and ambiguous scenarios from the earlier statistics. The objective of DSS lies in accomplishing the evolutionary computation and the ultimate decision making nature even in the uncertainty situations. Integrating the Artificial Intelligence (AI) into DSS transforms the traditional DSS into IDSS, which assists the humans in accomplishing the right decisions in a fast and easy manner with higher accuracies. The extensive study concluded that the IDSS had played an essential role in monitoring and remedial actions in the healthcare domain
“Budget Management Strategies in Repeated Auctions”∗ Technical Report with Supplementary Materials on the Uniqueness and Stability Properties in the Independent Setting

In this technical report, we consider the dynamical systems corresponding to natural implementations of budget management mechanisms in Table 1 of the main paper and characterize the uniqueness and stability properties of system equilibria of these mechanisms in the independent setting. Often in practice, budget management strategies are implemented dynamically by adjusting parameters over time. One simple approach involves adjusting the parameter of each buyer independently until the system converges to an equilibrium. Recall, for all budget management mechanisms under consideration, that a system equilibrium can be alternatively characterized as a solution to the following nonlinear complementarity problem:
Deep Learning Architecture : A Literature Survey

: Deep learning is a sub field of machine learning. Learning can be of supervised, semi-supervised and unsupervised. There are different types of architectures for deep learning . In this paper we are giving an overview of different architectures that are widely used and their application area. Deep learning is applied in many areas such as image processing, speech recognition, data mining, natural language processing, social network filtering, machine translation, bioinformatics and drug design.
Current-mode active compensation techniques

A general class of active compensation techniques based on current-mode analogue signal processing is described. These techniques are applicable to typical amplifier structures embedded in capacitive networks, such as operational amplifiers and integrators. A measure of the magnitude of the amplifier's capacitive embedment is shown to be directly related to an effective capacitance. This is significantly reduced by applying the proposed current-mode active compensation techniques, resulting in considerable improvement in amplifier speed, accuracy and bandwidth.
Dynamic Pricing Model for Exploring Resource Rental Planning in Cloud Computing

: The promising feature of cloud computing model that has abundant resources and elasticity, liberate organizations from the costly infrastructure investment. The main issues arise when the applications generate high volume of data that acquire significant cost on storage and transfer. Hence, the need arises for the service providers to evaluate resource rental options that must be displayed for the consumers. The main objective is to serve both the clients and the service providers. The profitable valuation model is used in order to reduce the pricing of cloud resources. In this model, clients pay for the storage space in which they could post images for the advertisements. Cloud Service Providers reduce the price for storage based on several characteristics such as on advertisement clicks by the users. Clients make profit by the number of advertisement clicks by the users. This model ensures that the profit increases due to the increase in number of clients.
INFLUENTIAL NODE IN ONLINE SOCIAL NETWORKS USING SPARK

: Social Networks play an important role in each and everyone’s life in this world. Every individual spends most of his time in Social Networks each day to gain and provide knowledge. A particular individual is said to be the influential user if he has the maximum number of followers. To find the influential node in each domain dynamically we use the Apache Spark framework for the processing of the data dynamically and the algorithm we use is the UBI (Upper Bound Greedy Interchange) Algorithm. This helps to find the influential node in a domain at the particular instance of time. The performance of the system is improved and the scope of viral marketing is increased. We use Apache Spark, a Real-time stream processing framework of Big Data to process the data in real time. Hence we find the most influential user in the online social networks in each domain dynamically at that particular instance
Let the Needles Do the Talking! Evaluating the New Haven Needle Exchange

New Haven, Connecticut implemented a needle exchange program in November 1990 to combat the spread of the AIDS virus. We developed a syringe tracking and testing system that provided data for mathematical models of HIV transmission. The models suggest that needle exchange reduced the HIV infection rate among program clients by 33 percent. In response, the Connecticut legislature continued funding the program, expanded needle exchange services to Bridgeport and Hartford, and decriminalized syringe possession. New needle exchange programs and legislation have also been developed in New York City, California, and Massachusetts partially as a result.
Dimension, Divergence and Desingularization∗ A Rough Cut on Motifs

The history of modern theoretical physics demonstrates a collective urge on behalf of its practitioners to seek beauty and simplicity in each generation’s search for answers to, what it considers, fundamental questions about the material universe. In this desiderata one should also add the quest for uniqueness of physical laws and an ontological one-to-one correspondence between “law” and “phenomena”. Like the former, the notion of uniqueness of physical law is laden with subjective meaning– an article of faith– that struggles for a consistent mathematical reformulation. At the same time, there is a tension between generality and specificity corresponding to nonuniqueness and uniqueness (see figure 1). In this Essay, I examine this tension between unique-
Neonatal Screening for Congenital Hypothyroidism in Tertiary Care Centers

Material and Methods: The study was conducted in post natal ward of tertiary care centers at Greater Noida (multicentric hospital based study) of Gautam Buddha Nagar districts to find out the prevalence of congenital hypothyroidism. A t o t a l o f 314 newborn babies were included in present study which was conducted during Feb 2014 to Dec 2016. Cord blood / venous blood samples were taken of congenital hypothyroidism (CH) suspected neonates. Thyroid stimulation hormone (TSH) and T4 (free) were used to screen the neonates. Neonates with abnormal hormonal levels TSH ≥ 25μIU/ml, T4(free)≤ 1.0 ng/dl in c o r d b lood and TSH ≥10μIU/ml, T4(free) ≤0.58 ng/dl in venous blood or any doubtful results rechecked with repeat sample. On the initial screening samples were considered to have positive screening test for CH & were classified as transient hypothyroid (TSH level 25-50μIU/ml) & permanent hypothyroid (TSH level > 50μIU/ml). The data was analyzed using standard statistical techniques.
Abstract— This work proposes and tests a simplified coherent uplink architecture for ultra-dense coherent analog mobile Fronthaul with phase noise cancellation based on single sideband modulation of a dual-EML. Results show optical channel spacings

This work proposes and tests a simplified coherent uplink architecture for ultra-dense coherent analog mobile Fronthaul with phase noise cancellation based on single sideband modulation of a dual-EML. Results show optical channel spacings of only 1.5 GHz and error-free signal recovery with Δν Tb = 1.2% and 3% with reaching power as low as -40 dBm for 100 Mbit/s and -37 dBm for 1 Gbit/s using QPSK modulation.
TEXT CATEGORIZATION USING SUPPORT VECTOR MACHINE CLASSIFICATION

: Text categorization is a vital and all around considered territory of example acknowledgment, with an assortment of present day applications. Viable spam email sifting frameworks, mechanized report association and administration, and enhanced data recovery frameworks all advantage from systems inside this field. The issue of highlight determination or picking the most applicable highlights out of what can be an extraordinarily huge arrangement of information, is especially essential for exact Text categorization. The proposed framework (I) utilize understood pre-handling technique doorman and Lancaster for prepare the dataset. (ii) various component choice measurements have been investigated in content arrangement, among which data pick up (IG), chi-square (CHI), Mutual data (MI), Ng-Goh-Low (NGL), Galavotti-Sebastiani-Simi (GSS), Relevancy Score (RS), Multi-Sets of Features (MSF) Document recurrence (DF) and chances proportions (OR) are viewed as best. Pruning strategies are additionally proposed utilizing overlook the component in view of TF and DF to additionally decrease the arrangement of conceivable highlights (normally words) inside a report preceding applying a technique for include determination. (iii) Finally order the chose include in light of two calculation bolster vector machines and Navie bayes. Two benchmark accumulations were picked as the testbeds: Reuters-21578 and little bit of Reuters Corpus Version 1 (RCV1). The two classifiers and the two information accumulations, and that a further increment in execution is acquire by consolidating uncorrelated and high-performing highlight choice techniques
SCALABLE LEARNING OF BEHAVIOURAL FEATURES FOR CLASSIFYING USERS IN COLOSSAL SOCIAL ENVIRONMENTS

: The Framework proposes an efficient approach to the user behaviour clustering in social networking website Latent social dimensions are extracted based on network topology to capture the potential applications of user in social network. These extracted social dimensions characterize how each other is concerned in different application affiliations. The social dimensions can be treated as features of actors for following dictionary learning. Sensor network features, typically support vector machine and forward with select based regression can be engaged. Social dimensions extracted according to soft clustering, ok such as modularity maximization and probabilistic methods. This sparse dictionary learning course of action will determine which social dimension correlates with the targeted behaviour and then assign proper weight. Observation is that actors of the same affiliation tend to connect with each other.
Design And Implementation Of An Efficient Noc Router

—Networks-On-Chip (NoCs) have been proposed as a promising solution for power, performance demands and scalability of next generation Systems-On-Chip (SOCs) to overcome the several challenges of current SoC with conventional architecture. In this article, NoC, its architecture and features are presented. Further the article is extended with research challenges. Major areas of scope for research are addressed briefly with the view that microelectronic field researchers get benefitted. Performance analysing parameters and simulation tools for NoC are also included. Future SoC design needs lot of innovations and creativity to explore its complete features. Research on NoC is mandatory at this critical juncture. This paper provides complete design and implementation of network on chip router architecture. Router is the basic element of NoC which have multiple connections connected to other router and to a local processing element in NoC. This router architecture used for building a NoC with low latency, high speed and high maximum peak performance. Low latency and high speed is achieved by allowing router function for each input port and distributed arbiter which gives high level of parallelism This paper presents an On-chip routers design using First In First out (FIFO) buffer. The concept of virtual channel (VC) is eliminated from the previous design by using an efficient flow control scheme that uses the storage already present in pipelined channels in place of explicit input virtual channel buffer. Through this design power is saving up to 66.66% and delay is reduced by 99.80% as compared to generic and power is saving up to 60.48% and delay is reduced by 90.88% as compared to virtual channel router