Miracle-dz/UniFAR-FTUs huggingface dataset:
Synergistic antiproliferative effects of benzyl isothiocyanate in combination with methyl-β-cyclodextrin and MK571 in human colorectal cancer cells

vii CHAPTER 1 . 1 General Introduction . 1 1.1 Cholesterol . 1 1.2 Colorectal cancer . 1 1.3 Cyclodextrins (CDs) . 2 1.4 Benzyl Isothiocyanate (BITC) . 2 1.5 Drug resistance . 3 1.5. 1 Celluar survival pathway . 4 1.5. 2 Drug efflux . 4 1.6 MAPKs involved pathways . 5 1.7 study outlines . 6 CHAPTER 2 . 7 Methyl-β-cyclodextrin potentiates the BITC-induced anti-cancer effect through modulation of the Akt phosphorylation in human colorectal cancer cells . 7 2.
Evaluation of strobe effects on bitmap images

The subject of research is improvement and evaluating of the quality of digital processing of the image for the purpose of removing undesired defects (artifacts) in the form of strobe effects. The article presents new evaluating techniques for strobe effects on images based on defining image boundaries by Canny and Prewitt methods. There have been examined the possibilities of estimating the strobe effects on raster images represented by RGB color model. Evaluation of strobe effects is carried out by comparing the contours selected by Canny and Prewitt methods for individual color components (red, green, blue) and gray scale images. On the selected contours there are determined their minimum and maximum coordinates, by which the boundaries of the contours are differentiated. As shown by experiments, this approach can serve as a numerical metric for strobe effects on raster images. Another proposed approach in assessing strobe effects is to compare the area of circles obtained from previous calculations. The ratio of the smaller area to the larger one can serve as a numerical metric for estimating strobe effects on raster images. The proposed metrics are supplied with numerical calculations and graphic illustrations.
Nonlinear stability and control study of highly maneuverable high performance aircraft, phase 2

This project is intended to research and develop new nonlinear methodologies for the control and stability analysis of high-performance, high angle-of-attack aircraft such as HARV (F18). Past research (reported in our Phase 1, 2, and 3 progress reports) is summarized and more details of final Phase 3 research is provided. While research emphasis is on nonlinear control, other tasks such as associated model development, system identification, stability analysis, and simulation are performed in some detail as well. An overview of various models that were investigated for different purposes such as an approximate model reference for control adaptation, as well as another model for accurate rigid-body longitudinal motion is provided. Only a very cursory analysis was made relative to type 8 (flexible body dynamics). Standard nonlinear longitudinal airframe dynamics (type 7) with the available modified F18 stability derivatives, thrust vectoring, actuator dynamics, and control constraints are utilized for simulated flight evaluation of derived controller performance in all cases studied.
Crop row detection a bioinspired and data analysis approach

The increasing of robotics equipped with machine vision sensors applied to Precision Agriculture is demanding solutions for several problems. The robot navigates and acts over a rough surface, considering specific restrictions. The information to navigate between the crops is supplied by physical sensors and mainly by some imaging detection system to the robot. The vision system for this kind of robots has many challenges, as changes in luminosity, uncontinuous crop row, processing capacity and time, as well as terrain conditions, among others. The aim of this research is to propose a method to develop a vision system for a tractor robot based on: PCA dimensionality reduction algorithm, the second derivative method and a genetic algorithm for crop row detection.
Information Processing & Information Diffusion in Computing Citation Networks

The paper citation network is a traditional social medium for the exchange of ideas and knowledge. In this paper we view citation networks from the perspective of information diffusion. We study the structural features of the information paths through the citation networks of publications in computer science and analyze the impact of various citation choices on the subsequent impact of the article. We find that citing recent papers and papers within the same scholarly community garners a slightly larger number of citations on average. However, this correlation is weaker among well cited papers implying that for high impact work citing within one’s field is of lesser importance. We also study differences in information flow for specific subsets of citation networks: books versus conference and journal articles, different areas of computer science, and different time periods.
Development of a Strategy Model of the Driver in Lane Keeping

SUMMARY Based on vehicle constraints and known human operator characteristics, a strategy model was postulated for describing behavior in the lane keeping task. This model includes nonlinear thresholds operating on vehicle yaw and lateral translation, random input sources to account for spurious driver activity, and smoothing to account for driver response lag. The output of the model is steering wheel position To determine model parameters and model suitability in describing driver behavior, recordings were made for driver-subjects performing a lane-keeping task in a moving base driving simulator having a computer generated display. A procedure involving both analytic and experimental techniques was then developed for determining the model parameters of each driver Statistical comparisons and visual inspections made between driver-vehicle and model-vehicle time histories indicate a high degree of correspondence. Models such as these show promise in obtaining a better understanding of driver behavior and .
The Design of Embedded Wireless Monitoring System Based on ZigBee Technology

The home monitoring system designed in this paper used the combination of a home gateway controller and the ZigBee coordinator, the connection of the ZigBee coordinator and the terminal nodes through wireless mode, to issue the collected real-time data to the Intemet with a web page form, and the dynamic real-time update, so that the intelligent monitoring of the home-based internal environment is achieved. This system overcomes the shortcomings of traditional wired control system, has good currency, high expansibility, and can shorten the intelligent home product development cycle, help to design and develop powerful, cost-effective powerful, cost-effective home products.
Deep active inference for state estimation by learning from demonstration

Traditionally in reinforcement learning, agents are guided towards solving a task in a given environment through a combination of reward signals [1]. For example, in the classic mountain car environment, the agent receives a large positive reward when it reaches the end goal and accumulates negative rewards for every action it performs [2, 3]. In the active inference setting, on the other hand, there is no notion of a direct reward signal; the agent is rather driven towards a particular state configuration that it favours [4]. Traditionally, these preferred states are prescribed by a prior model. Posterior state predictions based on sensor observations are then driven to match these prior states through minimization of their Kullback-Leibler (KL) divergence. At the same time, a likelihood model is trained to explain the agent’s observations based on its state by optimizing the negative log-likelihood (NLL). The sum of both KL and NLL terms leads to the important concept of free energy, which can be minimized by performing actions in the environment that are dictated by a certain policy, thereby solving the task at hand.
A low power injection-locked divider for body sensor network

This paper presents a low power 866 MHz injection-locked frequency divider (ILFD) based on UMC 0.18 μm CMOS process. Unlike high-cost off-chip inductor ILFD, on-chip spiral inductor and MOS varactor are used. The ILFD is based on dual cross-coupled LC voltage controlled oscillator (VCO) with direct injection for tail transistor. Compared to NMOS/PMOS-only oscillators, cross-coupled VCO enables low-power operation by doubling transconductance, the supply voltage of cross-coupled VCO can be also as low as 0.6 V. It is about 0.6 mW (without output buffers) under supply voltage of 0.8 V. When the injected input signal power is 0 dBm, direct ILFD features a wide locking range of 100 MHz. The ILFD still has a locking range of 10 MHz when the injected input signal power is −10 dBm. It can be used as the driver stage of power amplifier in transmitter systems for body sensor network.
The Application of Energy Sensitivity for Practical Dynamic Security Region Calculation

For transient security assessment of in power system, the practical dynamic security region (PDSR) of active generator power is a significant reference for power operators. Based on the trajectory sensitivity, the paper efficiently utilizes the potential energy boundary surface (PEBS) method to iteratively obtain the critical power and PDSR. The result of IEEE 17-generator 162-bus power system validates that the proposed energy trajectory sensitivity is efficient and effective for calculating the critical active power and PDSR.
Architecture of Wideband High-Efficiency Envelope Tracking Power Amplifier for Base Station

This paper proposes new architectures for envelope-tracking power supplies for base stations. (1) The proposed multiphase DC-DC converter circuit uses multiple switching circuits with hysteresis comparators (as well as a voltage follower circuit), each optimized for a different envelope frequency, to realize both high efficiency and wide bandwidth. (2) We also propose the one using interleaved PWM switching circuits. The operation of the circuits, and simulation results, are described.
ULTRA STABLE COHERENT SOURCES BASED ON INJECTION LOCKED DFB FROM A FEMTOSECOND FIBER LASER COMB

We propose ultra stable coherent laser sources based on optical injection locked single mode of the femtosecond fiber laser comb. The source is discretely tunable coherent optical sources by two DFB lasers, which are injection-locked to a respective single mode of the fiber laser comb with the frequency difference of 500 MHz, that is exactly twice of the comb repetition frequency.
Base64, End of File and One Time Pad for Improvement Steganography Security

The aim of the study was to increase the level of security of data hiding and make it harder for cryptanalysts to know the hidden information.Improving confidentiality data is very important and can be performed by using a cryptography algorithm and combining few algorithms. End of File (EOF) were one of steganography method that widely used because it easily implemented in any media, combination of Base64 algorithm and One Time Pad as encoding and encryption process on data or object before embedded process using EOF method will produce a strong ciphertext that is good because one time pad using dynamic key as length message, ciphertext will be embedded in bytecode using EOF method and with the combination already perform it will not be easy to read by irresponsible party.
QoS Provisioning in 60 GHz Communications by Physical and Transport Layer Coordination

In the last decades, technological developments in wireless communications have been coupled with an increasing demand of mobile services. From real-time applications with focus on entertainment (e.g., high quality video streaming, virtual and augmented reality), to industrial automation and security scenarios (e.g., video surveillance), the requirements are constantly pushing the limits of communication hardware and software. Communications at millimeter wave frequencies could provide very high throughput and low latency, thanks to the large chunks of available bandwidth, but operating at such high frequencies introduces new challenges in terms of channel reliability, which eventually impact the overall end-to-end performance. In this paper, we introduce a proxy that coordinates the physical and transport layers to seamlessly adapt to the variable channel conditions and avoid performance degradation (i.e., latency spikes or low throughput). We study the performance of the proposed solution using a simulated IEEE 802.11ad-compliant network, with the integration of input traces generated from measurements from real devices, and show that the proposed proxy-based mechanism reduces the latency by up to 50% with respect to TCP CUBIC on a 60 GHz link.
Investigating the Temperature Distribution of Diatomic Carbon in Comets Using the Swan Bands

We present high spectral resolution observations of comets 122P/de Vico and 153P/Ikeya–Zhang obtained with the Tull Coudé spectrograph on the 2.7 m Harlan J. Smith telescope of McDonald Observatory. We used these data to study the distribution of the lines of the C2 (Swan) bands. We show that the data are best represented with two rotational temperatures, with the lowest energy lines being at a relatively cool temperature and the higher energy lines being at a higher temperature. We discuss the implications of this two-temperature distribution and suggest future work.
Weighing the stellar constituents of the galactic halo with APOGEE red giant stars

The stellar mass in the halo of the Milky Way is notoriously difficult to determine, owing to the paucity of its stars in the solar neighbourhood. With tentative evidence from Gaia that the nearby stellar halo is dominated by a massive accretion event – referred to as Gaia-Enceladus or Sausage – these constraints are now increasingly urgent. We measure the mass in kinematically selected mono-abundance populations (MAPs) of the stellar halo between −3 < [Fe/H] < −1 and 0.0 < [Mg/Fe] < 0.4 using red giant star counts from APOGEE DR14. We find that MAPs are well fit by single power laws on triaxial ellipsoidal surfaces, and we show that that the power-law slope α changes such that high [Mg/Fe] populations have α ∼ 4, whereas low [Mg/Fe] MAPs are more extended with shallow slopes, α ∼ 2. We estimate the total stellar mass to be $M_{*,\mathrm{tot}} = 1.3^{+0.3}_{-0.2}\times 10^{9}\ \mathrm{M_{\odot}}$, of which we estimate ${\sim}0.9^{+0.2}_{-0.1} \times 10^{9}\ \mathrm{M_{\odot}}$ to be accreted. We estimate that the mass of accreted stars with e > 0.7 is M*,accreted, e > 0.7 = 3 ± 1 (stat.) ± 1 (syst.) × 108 M⊙, or ${\sim}30{-}50{{\ \rm per\ cent}}$ of the accreted halo mass. If the majority of these stars are the progeny of a massive accreted dwarf, this places an upper limit on its stellar mass, and implies a halo mass for the progenitor of ∼1010.2 ± 0.2 M⊙. This constraint not only shows that the Gaia-Enceladus/Sausage progenitor may not be as massive as originally suggested, but that the majority of the Milky Way stellar halo was accreted. These measurements are an important step towards fully reconstructing the assembly history of the Milky Way.
Private Protocols for U-Statistics in the Local Model and Beyond

In this paper, we study the problem of computing $U$-statistics of degree $2$, i.e., quantities that come in the form of averages over pairs of data points, in the local model of differential privacy (LDP). The class of $U$-statistics covers many statistical estimates of interest, including Gini mean difference, Kendall's tau coefficient and Area under the ROC Curve (AUC), as well as empirical risk measures for machine learning problems such as ranking, clustering and metric learning. We first introduce an LDP protocol based on quantizing the data into bins and applying randomized response, which guarantees an $\epsilon$-LDP estimate with a Mean Squared Error (MSE) of $O(1/\sqrt{n}\epsilon)$ under regularity assumptions on the $U$-statistic or the data distribution. We then propose a specialized protocol for AUC based on a novel use of hierarchical histograms that achieves MSE of $O(\alpha^3/n\epsilon^2)$ for arbitrary data distribution. We also show that 2-party secure computation allows to design a protocol with MSE of $O(1/n\epsilon^2)$, without any assumption on the kernel function or data distribution and with total communication linear in the number of users $n$. Finally, we evaluate the performance of our protocols through experiments on synthetic and real datasets.
An MDL-Based Classifier for Transactional Datasets with Application in Malware Detection

We design a classifier for transactional datasets with application in malware detection. We build the classifier based on the minimum description length (MDL) principle. This involves selecting a model that best compresses the training dataset for each class considering the MDL criterion. To select a model for a dataset, we first use clustering followed by closed frequent pattern mining to extract a subset of closed frequent patterns (CFPs). We show that this method acts as a pattern summarization method to avoid pattern explosion; this is done by giving priority to longer CFPs, and without requiring to extract all CFPs. We then use the MDL criterion to further summarize extracted patterns, and construct a code table of patterns. This code table is considered as the selected model for the compression of the dataset. We evaluate our classifier for the problem of static malware detection in portable executable (PE) files. We consider API calls of PE files as their distinguishing features. The presence-absence of API calls forms a transactional dataset. Using our proposed method, we construct two code tables, one for the benign training dataset, and one for the malware training dataset. Our dataset consists of 19696 benign, and 19696 malware samples, each a binary sequence of size 22761. We compare our classifier with deep neural networks providing us with the state-of-the-art performance. The comparison shows that our classifier performs very close to deep neural networks. We also discuss that our classifier is an interpretable classifier. This provides the motivation to use this type of classifiers where some degree of explanation is required as to why a sample is classified under one class rather than the other class.
Detection and Identification of Objects and Humans in Thermal Images

Intelligent detecting processing capabilities can be instrumental to improving safety and efficiency for firefighters and victims during firefighting activities. The objective of this research, is to create an automated system that is capable of real-time object detection and recognition that improves the situational awareness of firefighters on the scene. We have explored state of the art Machine Learning (ML) techniques to achieve this objective. The goal for this work is to maximize the situational awareness for fire-fighters by effectively exploiting the information gathered from the infrared camera and use a trained deep Convolutional Neural Network (CNN) system to classify and identify objects of interest in real time. In the midst of such critical circumstances created by a fire, this system is able to accurately inform the decision making process of firefighters in real time by extracting crucial information for processing. It is then able to make inferences about these circumstances to aid firefighters in safely navigating such hazardous and catastrophic environments.
High Mutual Information in Representation Learning with Symmetric Variational Inference

We introduce the Mutual Information Machine (MIM), a novel formulation of representation learning, using a joint distribution over the observations and latent state in an encoder/decoder framework. Our key principles are symmetry and mutual information, where symmetry encourages the encoder and decoder to learn different factorizations of the same underlying distribution, and mutual information, to encourage the learning of useful representations for downstream tasks. Our starting point is the symmetric Jensen-Shannon divergence between the encoding and decoding joint distributions, plus a mutual information encouraging regularizer. We show that this can be bounded by a tractable cross entropy loss function between the true model and a parameterized approximation, and relate this to the maximum likelihood framework. We also relate MIM to variational autoencoders (VAEs) and demonstrate that MIM is capable of learning symmetric factorizations, with high mutual information that avoids posterior collapse.
Halonen The Residual Value Function-Including the Estimated Brand Effects on Residual

ISSN 1976-7307(Print) IJAIM, Vol. 2 (July 2008), 1-14 Abstract Used car value depreciates when the age and kilometers increases. The value of the vehicle at the end of the leasing contract is often called residual value. The depreciation of value of the vehicle is the most significant part of the cost of leasing. There are very large differences in the rate of depreciation between cars based on the characteristics of them. Some cars hold their value much better than the others. It is very important for the leasing business to identify which are the cars that hold their value the best and, more generally, to correctly forecast the car values at the end of the contract. For the manufacturer, it is very important that they can produce cars that hold their value well. This is because the cost of the ownership of the car is then minimized for the customer. Thus buyers try to find cars that hold value well. Using large amount of data and statistical modeling (mixed models) it is possible to estimate the residual value function that relates the characteristics of the car and the expectation value of residual values as percentage of the new car value. This function is not constant in time. Rather, it is changing continuously, and usually rather slowly. There are many characteristics that have statistically significant effect on residual value percentage. One of the most important characteristic is the brand of the car. If we have two technically similar cars having the same new car price, the residual value as percentage of new car value differ very significantly based on the brand. Measuring the effect of the brand gives numerical estimate of the brand value, i.e. how much more or less the consumer is willing to pay for technically similar cars just because the brand is different. These brand values change slowly in time and we can follow the changes. Korean brands are rather new in the car market of Finland. It is very interesting to follow the brand value development of these brands. For any new and rather unknown brand in any market, it is rather difficult to attain high brand value, because it is about trust and people generally don t trust an unknown brand. In the Finnish car market Kia has been able to build valuable brand image quickly, which is very unusual. The secret has probably been a very well known retailer, intensive service program and the longest warranty in the market.
Artemia spp . Model-A Well-Established Method for Rapidly Assessing the Toxicity on an Environmental Perspective

Toxicology is the science that deals with the study of adverse effects chemicals or physical agents may produce in living organisms under specific conditions of exposure. It is a science that attempts to investigate qualitatively all the hazards, for example the organ toxicities that are associated with a substance as well as to quantitatively determine the exposure conditions under which those hazard or toxicities are induced [1, 2]. Toxicology is the science that experimentally investigates the occurrence, nature, incidence, mechanism and risk factors for the adverse effects of toxic substances [2]. ABSTRACT