Miracle-dz/UniFAR-FTUs huggingface dataset:
Rheology and Collective Behavior in Living Tissue

Recent experiments and simulations have indicated that confluent epithelial layers, where there are no gaps or overlaps between the cells, can transition from a soft fluid-like state to a solid-like state, with dynamics that share many features with glass transitions. While a coherent picture has begun to form connecting the microscopic mechanisms that drive this transition with macroscopic observables, much less is known of its consequences in biological processes. Do tissues tune themselves to a fluid state in order to promote collective motion? Has evolution made use of the ability of tissues to tune themselves between fluid and solid states in programming the complex steps leading from the embryo to the organism? Here we describe our recent e↵orts to answer such questions using continuum and mesoscopic models. Employing the biophysical vertex model, active cells in confluent tissue are described as polygons with shape-based energies. Recent work has shown that this class of models yields a solid-liquid transition of tissue with evidence of glassy dynamics near the transition line. Here, we extend one such model to include the influence of cell division and cell death. With careful numerical studies, we refute a recent claim that the presence of such division and death will always fluidify the tissue. In the second part of the thesis, we develop a novel hydrodynamic model of confluent motile tissues that couples a structural order parameter for tissue rigidity to cell polarization. Using this continuum model we identify a new mechanism for pattern formation in confluent tissues via rigidity sensing that we name “morphotaxis”. We find that a single “morphotactic” parameter controls whether a tissue will remain homogeneous or will develop patterns such as asters and bands. Rheology and Collective Behavior in Living Tissue by Michael D. Czajkowski B.A., SUNY Geneseo, 2011 Dissertation Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Physics. Syracuse University August 2018 Copyright c Michael D. Czajkowski 2018 All Rights Reserved
A procedure for determination of the exponential stability of certain differential-difference equations

Certain continuity properties of the spectra of linear autonomous differential-difference equations which depend on a parameter are developed. These results are used to obtain a practical criterion for determination of the exponential stability of these systems. Introduction. In this paper a problem arising from the study of linear differential difference equations is considered. Suppose we are given an ^-dimensional family of differential difference equations of the type d_ dt x(t) S Bjx(t — ahj) = £ Ajx(t ~ ahj), a > 0, (0.1) 7=1 where 0 = h0 < h, < h2 < • • • < hm are fixed constants. We ask the questions: "In what manner do the stability properties of (0.1) depend on a change in a? In particular, if (0.1) is asymptotically stable for a = 0, how large can we take a and still preserve this property? " In the case where Bj = 0 for all j, i.e. the retarded case, the answer to this last question is quite easily given. In this case the roots of the characteristic equation (see e.g. [5]), although infinite in number, possess a continuity property which allows one to determine or at least approximate the size of the largest q for which a e [0, q) guarantees that the corresponding system (0.1) is asymptotically stable. If Bj ^ 0 for some j, the above questions become more difficult. The procedure used in this paper to study these questions may be outlined as follows. For each a in [0, °o) we define a(a) = sup Re{s: ^ is in the point spectrum of (0.1)}. Then for a > 0 we can prove that a (a) is continuous. In the retarded case a(a) is also continuous at a = 0. In the general case a(a) is continuous at a = 0 for a particular class of systems which contains the uniformly exponentially stable systems. Thus classes of systems (0.1) for which c(a) is continuous on [0, °°) provide us with a method of determining whether for a given a„ the system is uniformly asymptotically stable. For if <r(0) 0 and q? o [0, q\ where q is the smallest number which satisfies (t(cv) = 0, then a(a0) < 0. But from the general theory of retarded and neutral functional equations (see e.g. [6] and [7] we know that <r(a0) < guarantees that for the value a0 (0.1) is uniformly exponentially stable. The idea of studying stability though examination of the manner in which the point spectrum of (0.1) varies as a varies is similar to the method of D-partitions used to study the stability of retarded linear systems. A good outline of this method can be found in [5, Received September 8. 1977.
Early Stage Flame Segmentation with DeepLabv3+ and Weighted Cross-Entropy

The main focus of this study is early-stage flame detection, where the number of flame pixels in the image is very scarce. To address this challenge, a custom-made dataset was created specifically for early-stage flame detection, encom-passing challenging environmental conditions. The DeepLabv3+ architecture with ResNet-50 backbone was employed for training and weighted cross-entropy was used to effectively handle the imbalanced nature of the dataset. As a result, the model achieved a mean Intersection over Union (mIoU) value of 0.7519, demonstrating robust performance in challenging conditions. The model exhibited accurate flame pixel detection and flame shape identification in images with low flame content but high smoke levels. Additionally, the model performed well in night-time conditions, accurately identifying flame regions and shapes. An important aspect of the model's performance was its ability to correctly identify images with no flames, thereby reducing false alarms and making it suitable for UAV-based flame detection tasks.
A Matrix Approach to the Analysis and Control of Networked Evolutionary Games with Bankruptcy Mechanism

This paper investigates the evolutionary dynamic and control problem for a kind of networked evolutionary games with bankruptcy mechanism by using semi‐tensor product of matrices, and presents a number of new results. First, this kind of games are expressed as logical dynamic networks and converted into their algebraic forms, based on which, the evolutionary dynamics of the given games can be discussed. Second, the control problem is considered, and a control sequence is designed to guarantee that none of players goes bankrupt as the control target requires. Finally, an illustrative example is given to show the effectiveness of our main results.
DETERMINANT FACTORS OF TECHNICAL EFFICIENCY IN YAM PRODUCTION IN MORO LGA, KWARA STATE, NIGERIA

This paper investigates the determinants factors of technical efficiency using the stochastic frontier production function which incorporates the inefficiency model. A sample of 150 yam farmers were randomly selected from 17 wards of Moro LGA in Kwara State, Nigeria. Data was collected from the respondents with the aid of a structured interview schedule. The empirical result showed that land size, fertilizer, seedling, and use of herbicides were the main factors determining technical efficiency while age, experience, household size and contact with extension agent were the main determinants of the technical inefficiency of yam farmers. The mean technical efficiency was 0.636 which means that 36.4% of the technical output was not achieved. The result also indicates that land size, fertilizer, seedling, and herbicides are significant in yam production. And a significant relationship between age, household size, extension contact and years of experience and the output of the yam producers as it influences their technical efficiencies. The study concludes that land size, fertilizer, seedling, and use of herbicides were the main factors determining technical efficiency while age, experience, household size and contact with extension agent were the main the main determinants of inefficiency and thus recommends youths be encouraged to form cooperative organization and into yam production with attractive incentives like easy access to farm inputs, cooperate financing, easy exposure to markets.
CUSTOMER EXPERIENCE AND THE INTENTION TO REUSE DIGITAL SERVICES IN A FINANCIAL INSTITUTION IN CHICLAYO: ROLE MEDIATOR OF SATISFACTION

: The purpose of this study is to ensure that a client meets their expectations in terms of perceiving satisfaction in relation to a product or service in order to make them want to purchase it again in terms of perceiving a pleasant experience, which aimed to determine the mediating effect of the satisfaction perceived in the customer's experience in the face of the intention to reuse digital services of a financial institution, in the city of Chiclayo. The methodology used had a quantitative approach, an explanatory level, and a non-experimental and cross-sectional design. 300 clients of a Chiclayan financial institution were considered as an analysis sample.
LLD: A Low Latency Detection Solution to Thwart Cryptocurrency Pump & Dumps

Pump and Dump schemes represent a threat to any market. While this issue has long been regulated in mature markets, in unregulated markets, such as crypto exchanges, this plague is very present, and even exacerbated by the low capitalizaton of many cryptocurrencies that represent the perfect target for such a fraudulent scheme. In this paper, we detail a Low Latency Detection solution (LLD) based on deep learning to automatically detect pump and dump activities on centralized cryptocurrency exchanges. We train a LSTM-based auto-encoder on BTC valuations, which can reliably be considered a proxy for regular trading-due to their larger capitalization. We use this auto-encoder to predict valuations on alt coins and use thresholding on a Gaussian tail condition to trigger detection. We argue that low latency detection is paramount for the practicality of such approaches. Unlike previous methods, our solution (LLD) detects the majority of pumps in less than five minutes (2.2 minutes on average) when using OHLCV data at one-minute resolution. In addition, we use social media data only to generate ground truths during testing. We show that in many cases a significant amount of the trade volume could have been saved had LLD been used to trigger trade suspension mechanisms. The idiosyncratic approach of our scheme, its sound rationale and viability, combined with the quality of achieved results-tested over an extensive experimental campaign-and the insights discussed in the paper also pave the way for further research in the field.
Automating Code Generation for MDE using Machine Learning

The overall aim of our research is to improve the techniques for synthesizing code generators in the Model-Driven Engineering (MDE) context. Code generation is one of the main elements of Model-Driven Engineering, involving transformation from specification models to produce executable code. A code generator is designed to reduce the manual program construction work used to implement a software system, but building a code generator itself still currently needs much manual effort. Meanwhile, existing code generators are typically not flexible to adjust for changing development requirements and are hard to reuse for different target languages. Therefore, we aim to provide techniques to improve the process of building code generators, and let them be more reusable. Currently, we researched the related new and traditional approaches for generating code and projects using AI for program translation, code completion or program generation. Based on this research we decided to focus on a symbolic machine learning method related to the programming-by-example concept to build code generators. We use this “Code Generation By Example” (CGBE) concept with tree-to-tree structure mappings as the information format. CGBE has good performance in terms of training dataset size and time when applied to learning a UML-to-Java code generator, but further work is needed to extend it to generate different programming languages and to evaluate these cases, and to handle the optimisation of generated code.
Image Test Libraries for the on-line self-test of functional units in GPUs running CNNs

The widespread use of artificial intelligence (AI)-based systems has raised several concerns about their deployment in safety-critical systems. Industry standards, such as ISO26262 for automotive, require detecting hardware faults during the mission of the device. Similarly, new standards are being released concerning the functional safety of AI systems (e.g., ISO/IEC CD TR 5469). Hardware solutions have been proposed for the infield testing of the hardware executing AI applications; however, when used in applications such as Convolutional Neural Networks (CNNs) in image processing tasks, their usage may increase the hardware cost and affect the application performances. In this paper, for the very first time, a methodology to develop high-quality test images, to be interleaved with the normal inference process of the CNN application is proposed. An Image Test Library (ITL) is developed targeting the on-line test of GPU functional units. The proposed approach does not require changing the actual CNN (thus incurring in costly memory loading operations) since it is able to exploit the actual CNN structure. Experimental results show that a 6-image ITL is able to achieve about 95% of stuck-at test coverage on the floating-point multipliers in a GPU. The obtained ITL requires a very low test application time, as well as a very low memory space for storing the test images and the golden test responses.
Forecasting Electricity and District Heating Consumption: A Case Study in Schools in Sweden

The growing population and demand for new public buildings contribute to increased energy consumption and greenhouse emissions. In Sweden, the largest amount of energy is consumed in school buildings, i.e., where schools form the highest number of public properties (30 million m2). In total, schools consumed 4 222 GWh of district heating and about 3 GWh of electricity for heating and other purposes in 2020. These figures lead to the realization of the need to apply effective measures to meet the European Green Deal target for 2030. Accurately forecasting energy usage is important for all stakeholders to conduct economic analysis and optimize decision-making. It is equally important in maintenance operations to allocate resources and enable the staff and students to adjust their behaviours and address the issues in buildings where peak forecasts occur. This paper develops and evaluates a power and district heating consumption for a single day and multiple days forecasting using Multivariate Recurrent Neural Network (RNN) -Long-Short term memory (LSTM) and convolutional neural networks (CNNs) and Autoencoders (AE), using daily real consumption data of six public schools provided by Skelefteå municipality in Sweden. The experimental results demonstrate that the hybrid model CNN-LSTM has achieved good accuracy compared to others, with RMSE and nRMSE error between 18%-25% and 5%-6% for electricity, respectively, and between 20%-30% RMSE and 5% nRMSE for district heating.
Evidence for Griffiths-Type Dynamics near the Many-Body Localization Transition in Quasi-Periodic Systems

In the presence of sufficiently strong disorder an interacting many-body system can fail to thermalize and become many-body localized. However, already below the many-body localization transition, thermalization can be strongly impeded by rare, already localized regions. In this work, we experimentally and numerically observe signatures of these so-called Griffiths regions in a quasi-periodic system. Here, unlike in systems with true-random disorder, rare localized regions cannot result from the disorder pattern alone, but instead emerge due to randomness in the initial state. Specifically, we observe a power-law decay of an initial charge-density wave pattern on intermediate timescales, which has been predicted for the Griffiths regime. From this decay, we are able to access the dynamical exponent and find evidence for an extended sub-diffusive regime.
A DECOMPOSITION OF 7~~ (S*'+'; Z/pZ)

as announced in[5]. The restriction p > 3 is no longer necessary as subsequent work has eliminated this hypothesis in the theorems of Cohen et al. (See [4].) A corollary of the proof is that C(p)(,) is a double loop space. I wish to thank John Moore and Fred Cohen for their contributions and to give special thanks to Joe Neisendorfer for pointing out Theorem 3 to me. In addition, I would like to take this opportunity to apologize for an error in the introduction to[5]. Although James showed that 2*“, is an exponent for the 2-torsion in n*(S*“+‘) he did not show that this is the best possible exponent. In fact, Neisendorfer has shown me a proof, which I believe is originally due to Mahowald, that r*(S5) has exponent 8 at the prime 2. The problem of finding the best possible exponent for r*(S*“+‘) at the prime 2 is still open.
Evolution of the Practice of Software Testing in Java Projects

Software testing helps developers minimize bugs and errors in their code, improving the overall software quality. In 2013, Kochhar et al. analyzed 20,817 software projects in order to study how prevalent the practice of software testing is in open-source projects. They found that projects with more lines of code (LOC) and projects with more developers tend to have more test cases. Additionally, they found a weak positive correlation between the number of test cases and the number of bugs. Since the conclusions of a study might become irrelevant over time because of the latest practices in the relevant fields, in this paper, we investigate if these conclusions remain valid if we re-evaluate Kochhar et al.’s findings on the Java projects that were developed from 2012 to 2021. For evaluation, we use a random sample of 20,000 open-source Java projects each year. Our results show that Kochhar et al.’s conclusions regarding the projects with test cases having more LOC, the weak positive correlation between the number of test cases and authors, and the weak positive correlation between the number of test cases and bugs remain stable until 2021. Our study corroborates Kochhar et al.’s conclusions and helps developers refocus in light of the latest findings regarding the practice of software testing.
Towards Supporting Emotion Awareness in Retrospective Meetings

Emotion awareness is a key antecedent to team effectiveness and the use of biometrics can help software developers in gaining awareness of emotions at the individual and team level. In this paper, we propose an approach to include emotional feedback in agile retrospective meetings as a proxy to identify developers’ feelings in association with the activity performed by the team. As a proof of concept, we developed an emotion visualization tool that provides an integrated visualization of self-reported emotions, activities, and biometrics. We run a pilot study to evaluate our approach with the agile retrospective meetings of a software engineering capstone project. The preliminary findings suggest that integrated emotion visualization can be useful to inform discussion and reflection around the potential causes of unhappiness, thus triggering actionable insights that could enhance team productivity and improve collaboration.
Exponential stabilization of a structure with interfacial slip

Two exponential stabilization results are proved for a vibrating structure subject to an interfacial slip. More precisely, the structure consists of two identical beams of Timoshenko type and clamped together but allowing for a longitudinal movement between the layers. We will stabilize the system through a transverse friction and also through a viscoelastic damping.
Achieving Last-Mile Functional Coverage in Testing Chip Design Software Implementations

Defective chips may cause huge losses (even disasters), and thus ensuring the reliability of chips is fundamentally important. To ensure the functional correctness of chips, adequate testing is essential on the chip design implementation (CDI), which is the software implementation of the chip under design in hardware description languages, before putting on fabrication. Over the years, while some techniques targeting CDI functional testing have been proposed, there are still a number of hard-to-cover functionality points due to huge input space and complex constraints among variables in a test input. We call the coverage of these points last-mile functional coverage.Here, we propose the first technique targeting the significant challenge of improving last-mile functional coverage in CDI functional testing, called LMT, which does not rely on domain knowledge and CDI internal information. LMT first identifies the relevant variables in test inputs to the coverage of last-mile functionality points inspired by the idea of feature selection in machine learning, so as to largely reduce the search space. It then incorporates Generative Adversarial Network (GAN) to learn to generate valid test inputs (that satisfy complex constraints among variables) with a larger possibility. We conducted a practical study on two industrial CDIs in Huawei to evaluate LMT. The results show that LMT achieves at least 49.27% and 75.09% higher last-mile functional coverage than the state-of-the-art CDI test input generation techniques under the same number of test inputs, and saves at least 94.24% and 84.45% testing time to achieve the same functional coverage.
Understanding the Impact of Fingerprinting in Android Hybrid Apps

Numerous studies demonstrate that browser fingerprinting is detrimental to users’ security and privacy. However, little is known about the effects of browser fingerprinting on Android hybrid apps – where a stripped-down Chromium browser is integrated into an app. These apps expand the attack surface by permitting two-way communication between native apps and the web. This paper studies the impact of browser fingerprinting on these embedded browsers. To this end, we instrument the Android framework to record and extract information leveraged for fingerprinting. We study over 60,000 apps, including the most popular apps from the Google play store. We exemplify security flaws and severe information leaks in popular apps like Instagram. Our study reveals that fingerprints in hybrid apps potentially contain account-specific and device-specific information that identifies users across multiple devices uniquely. Besides, our results show that the hybrid app browser does not always adhere to standard browser-specific privacy policies.
Recursive estimation in piecewise affine systems using parameter identifiers and concurrent learning

ABSTRACT Piecewise affine systems constitute a popular framework for the approximation of non-linear systems and the modelling of hybrid systems. This paper addresses the recursive subsystem estimation in continuous-time piecewise affine systems. Parameter identifiers are extended from continuous-time state-space models to piecewise linear and piecewise affine systems. The convergence rate of the presented identifiers is improved further using concurrent learning, which makes concurrent use of current and recorded measurements. In concurrent learning, assumptions on persistence of excitation are replaced by the less restrictive linear independence of the recorded data. The introduction of memory, however, reduces the tracking ability of concurrent learning because errors in the recorded measurements prevent convergence to the true parameters. In order to overcome this limitation, an algorithm is proposed to detect and remove erroneous measurements at run-time and thereby restore the tracking ability. Detailed examples are included to validate the proposed methods numerically.