Miracle-dz/UniFAR-FTUs huggingface dataset:
A MODEL FOR ACCELERATING IDENTIFICATION AND REGULATORY APPROVAL OF EFFECTIVE INVESTIGATIONAL AGENTS

A path for accelerated regulatory review of new drug and biomarker combinations is badly needed to transform the current clinical drug development process into an efficient, effective system that meets current and future healthcare needs. However, this type of radical transformation will not occur by layering regulatory change on existing clinical practice patterns. Here, we summarize a May 2011 I-SPY 2 TRIAL (Investigating Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular AnaLysis) workshop that included key leaders from the Food and Drug Administration (FDA), academia, industry, advocacy, and clinical trial-focused foundations that addressed this critical need. The workshop identified key aspects of the organization, trial design, and regulatory and industry alignment required to rapidly move successful agents from trial to clinical care. The workshop specifically focused on providing input for the development of a new evidence-based regulatory path for review of drugs and biomarkers that derive from neoadjuvant, modular, adaptive phase 2 screening trials achieved through precompetitive collaborations. The workshop participants agreed that new models, exemplified by the I-SPY 2 TRIAL for breast cancer, could address the need to more efficiently review and advance new drug and biomarker combinations. A three-tier model for trial development best describes this process. As practiced in I SPY 2, integrating imaging and biomarker information obtained through adaptive trials in the neoadjuvant breast cancer care setting is the first step. The second tier is replicable structural processes, including real-time data collection. The third is partner alignment achieved through pre-competitive collaboration and the potential to position successful drugs for accelerated regulatory approval in the neoadjuvant setting, where patients are likely to have the greatest benefit. Many elements of this model were incorporated into a new FDA draft guidance document which was released for public comment on May 31, 2012. Categories: Miscellaneous, Oncology
Recognition of Unhealthy Plant Leaves Using Naive Bayes Classifier

India is a country and around 70% of our population relies on development. 33% of our national pay starts from agribusiness. So to identify the plant diseases is a difficult way in the agriculture field. Major part of the plant diseases are brought about by the assault of microorganisms, developments, contamination, etc. In case proper consideration isn’t taken around there, it will cause genuine consequences for plants and unfavorably influences the efficiency and quality. To perceive the plant diseasesa brisk customized way is needed. The main aim of this project is to spot the unhealthy plant leaves. In this task, MATLAB tool is used for measuring affected area of disease and to determine the colour of the diseases. So this task takes less measure of time and get more efficiency. Here, this project is proposed with a thoughtof identifying unhealthy region of plant leaves using classifier technique. The classifier technique can be used to classify the leaves and the classified results result can be seen in simulation.
Immunohistochemical study of claudin-4 expression in relation to P504S (AMACR) and their significance in prostatic adenocarcinoma

Objectives Claudins are a family of ∼24 tight junction proteins that are frequently altered in several human cancers. The aim of this study was to evaluate the expression and the prognostic relevance of claudin-4 in relation to P504S [&agr;-methylacyl-CoA racemase (AMACR)] in benign, preneoplastic, and neoplastic prostate tissue samples. Materials and methods Immunohistochemical analysis was carried out to determine expression levels of claudin-4 and AMACR protein as markers for prostatic adenocarcinoma (PCa; n=40), prostatic intraepithelial neoplasia (PIN; n=26), and benign prostatic hyperplasia (BPH; n=10). The relationship between the expression of these proteins and clinicopathological parameters such as Gleason’s score, pathological stage, prostate-specific antigen value, and patients’ age was studied. Results A significant increase in claudin-4 expression was observed from BPH through low-grade and high-grade PIN to primary PCa (40, 50, 75, and 85%, respectively; P=0.013). PCa had the highest mean staining score for claudin-4 (P=0.0008), which was significant. P504S showed positive expression in 90% of PCas, 87.5% of high-grade PINs, and 70% of low-grade PINs. Ninety percent of the patients with BPH did not show P504S expression. PCa had the highest mean staining score for P504S (P=0.00004), which was significant. Low expression of claudin-4 was associated with a Gleason score of 7 or higher (P=0.035) and a high prostate-specific antigen value (>10 ng/ml; P=0.021). Claudin-4 expression increased with pT-stage but with no significant difference. P504S did not show a significant relationship with any of the clinicopathological parameters. A highly significant association was found between P504S and claudin-4 expression (P<0.001). Conclusion Our results suggest that a combination of AMACR and claudin-4 may be of great value in increasing the diagnostic accuracy for PCa. Moreover, claudin-4 may serve as an important biomarker for and a promising target for antibody-based therapy of prostate cancer.
Hybrid Local Search Polynomial-Expanded Linear Multiuser Detector

This work proposes hybrid multiuser detector based on Polynomial Expansion (PE-MuD) as a way to reduce computational complexity inherent to correlation matrix inversion, followed by the low complexity Local Search (LS) procedure, aiming to obtain a near-optimum multiuser Bit-ErrorRate (BER) performance, however with reduced computational complexity. The proposed hybrid PE-LS-MuD receiver topology is analyzed under realistic wireless mobile channels, as well as useful system operation scenarios. Simulation results indicate a suitable improvement in the performance-complexity trade-off regarding the classical linear multiuser detectors, particularly the MMSE. Keywords— Suboptimum Local Search Algorithms, Polynomial-Expanded Multiuser Detection, Gerschgorin Circles, DS-CDMA.
Gorthaur: A Portfolio Approach for Dynamic Selection of Multi-Armed Bandit Algorithms for Recommendation

Recommendation systems must reach a good global accuracy but also diversify their recommendations. Despite the theoretically grounded guarantees, we observe that multi-armed bandit algorithms obtain different results depending on the nature of the real-world applications or the offline datasets that is used. Thus, before choosing an algorithm, it is necessary to carry out a preliminary offline evaluation on the criteria of global accuracy and, if necessary, diversity. However, recommendation systems are notoriously hard to evaluate due to their interactive and dynamic nature. Hence, we have implemented a portfolio approach, entitled Gorthaur, which uses a heuristic to dynamically select multi-armed bandits algorithms used for recommending. Thus, Gorthaur aims at selecting algorithms by maximising the two criteria of global accuracy and diversity. Following our results, we observe that the advantage of using Gorthaur is twofold: 1) Find a trade-off in cases where there is no prior knowledge about the nature of the dataset or the recommendation application we want to deploy; 2) Rapidly shed light on a set of optimal algorithms.
Pencil Drawing Video Rendering Using Convolutional Networks

Traditional pencil drawing rendering algorithms when applied to video may suffer from temporal inconsistency and shower‐door effect due to the stochastic noise models employed. This paper attempts to resolve these problems with deep learning. Recently, many research endeavors have demonstrated that feed‐forward Convolutional Neural Networks (CNNs) are capable of using a reference image to stylize a whole video sequence while removing the shower‐door effect in video style transfer applications. Compared with video style transfer, pencil drawing video is more sensitive to the inconsistency of texture and requires a stronger expression of pencil hatching. Thus, in this paper we develop an approach by combining a latest Line Integral Convolution (LIC) based method, specializing in realistically simulating pencil drawing images, with a new feed‐forward CNN that can eliminate the shower‐door effect successfully. Taking advantage of optical flow, we adopt a feature‐map‐level temporal loss function and propose a new framework to avoid the temporal inconsistency between consecutive frames, enhancing the visual impression of pencil strokes and tone. Experimental comparisons with the existing feed‐forward CNNs have demonstrated that our method can generate temporally more stable and visually more pleasant pencil drawing video results in a faster manner.
1000 times closer to a continuous atom laser: Steady-state strontium with unity phase-space density

This thesis describes the Strontium Continuous Atom Laser (SrCAL) machine that we built and the many lessons we learned. The path to a steady-state Bose-Einstein Condensate (BEC) is uncharted. To navigate us there we designed and built a unique and flexible strontium quantum gas machine, a machine designed from the beginning to be a steady-state device. Few non-alkali quantum gas machines have ever been built and only a handful of them are for strontium, so making a new quantum gas machine is always a challenge. Learning how to build such a machine is not something you will learn in class, or from the literature. The best resources we have are the theses of those who came before us so an important part of this thesis is to document some of that hard won knowledge, to aid future builders in the construction of both pulsed and steady-state degenerate gas machines. This thesis also describes the technical detail needed for future users of SrCAL to wield this tool with mastery.
Diagnosis and Management of Concomitant Cystic Fibrosis in Patients With Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) and cystic fibrosis share key clinical features such as chronic airway inflammation and obstructive airflow limitation, and both diseases may lead to chronic respiratory failure and death. Cigarette smoke, well-known to be the major cause of COPD, contributes to suppression of the expression and function of cystic fibrosis transmembrane conductance regulator protein. Likewise, there is evidence that cystic fibrosis transmembrane conductance regulator dysfunction may contribute to key molecular pathways involved in the pathogenesis of COPD. We present 3 adult patients with extensive cigarette smoking histories and COPD who were subsequently diagnosed with cystic fibrosis and describe the management strategy implemented.
Research on Information Fusion Method of Millimetre Wave Radar and Monocular Camera for Intelligent Vehicle

The forward traffic environment cannot be fully perceived by a single sensor or multiple homogeneous sensors in the driving condition. Therefore, it is necessary to fuse the heterogeneous sensors to realize the mutual cooperation and compensation. Aiming at the advantages and disadvantages of different vehicle sensors, millimeter wave radar and monocular camera are selected as vehicle sensors to perceive the forward information. Firstly, the coordinate relationship between the sensors is calculated and sensor coordinates are mapped into the same vehicle coordinate system. Secondly, target information from the sensors are processed in parallel. Thirdly, in the case of time and space synchronization of two kinds of signals, the signals are processed by the matching algorithm of Global Nearest Neighbor (GNN) and the two matched targets are combined into one by the weighted average method. Finally, the unmatched targets and matched targets are tracked to determine the final state by Extended Kalman Filter(EKF) algorithm. Results in real car environment reveal that the fusion method can make up the deficiency of single sensor and improve the recognition rate of target.
Combining Lexical Substitutes in Neural Word Sense Induction

Word Sense Induction (WSI) is the task of grouping of occurrences of an ambiguous word according to their meaning. In this work, we improve the approach to WSI proposed by Amrami and Goldberg (2018) based on clustering of lexical substitutes for an ambiguous word in a particular context obtained from neural language models. Namely, we propose methods for combining information from left and right context and similarity to the ambiguous word, which result in generating more accurate substitutes than the original approach. Our simple yet efficient improvement establishes a new state-of-the-art on WSI datasets for two languages. Besides, we show improvements to the original approach on a lexical substitution dataset.
Butterfly-like Algorithms for GASPI Split Phase Allreduce

—Collective communication routines pose a signiﬁcant bottleneck of highly parallel programs. Research on different algorithms for disseminating information among all participating processes in a collective communication has brought forth many different algorithms, some of which have a butterﬂy-like communication scheme. While these algorithms have been abandoned from usage in collective communication routines with larger messages, due to the congestion that arises from their use, these algorithms have ideal properties for split-phase allreduce routines: all processes are involved in the computation of the result in each communication round and they have few communication rounds. This article will present several different algorithms with a butterﬂy-like communication scheme and examine their usability for a G ASPI allreduce library routine. The library routines will be compared to state-of-the-art MPI implementations and also to a tree-based allreduce algorithm.
Navigation behavioural decision-making of MASS based on deep reinforcement learning and artificial potential field

To realize intelligent obstacle avoidance and local path decisions for maritime autonomous surface ships (MASS) in uncertain environments, a navigation behavioural decision-making model based on deep reinforcement learning (DRL) algorithm improved by artificial potential field (APF) is proposed. Based on the analysis of navigation decision system and perception principle, the action space, reward function, motion search strategy and action value function are designed respectively for the purpose of steering to collision avoidance. The navigation behavioural decision-making model for MASS is improved by adding the prior information, the gravitational potential field and the obstacle repulsion potential field to update the initial action state value function and search path. Python and Pygame modules are used to build a simulation chart. Effectiveness of the algorithm is verified, with Tianjin Xingang port as a study case. The simulation results show that the APF-DRL algorithm is better than the DRL algorithm in training iteration time and piloting decision path, which improves the self-learning ability of MASS, and can meet the requirements of MASS path decision and adaptive obstacle avoidance.
Mental Health and Somatic Distress among Ethiopian Migrant Returnees from the Middle East

Objective: This study aimed to examine the mental health and somatic distress among migrant returnee population in Ethiopia. Background: Mental health and psychosocial distress are frequent among people who have faced adversity, such as exposure to abuse, exploitation, loss, displacement, and human trafficking. Returned Migrants are the most vulnerable population for such adversities, especially when they work in under-regulated sectors such as domestic work entered through illegal migration. Addressing the mental health issues of returnees has to be a cross- cutting activity of any migration -crisis intervention. However, knowledge about the frequency, severity, and risk factors as they affect migrants’ mental health in Ethiopian is limited at best. Method: In a sample of 1,035 returnee migrants, data were collected on the mental distress based on SRQ-20 and somatic distress based on PHQ-15. A descriptive statistics correlation, t-test, and factorial MANOVA analysis were run to determine the distress status and the relationship between different variables. Results: Using a cut of point of 8, 26.08% of the sample was considered to be a probable case (n=270), with females endorsing more items than males. Twenty- three percent (11.7%, Mild; 8.2% Moderate; and 3% Sever) of the participants reported somatic manifestation of psychological distress. A significant relationship is found between participants’ mental health distress and somatic psychological distress. Religious affiliation and ethnicity are found to be associated with mental health distress, while gender and education are found to be significantly associated with somatic psychological distress. Conclusion: Ethiopian migrant returnees deal with significant mental health distress and endorsed somatic symptoms in outpatient setting. Migrant returnees should access to a mental health service that is exclusively geared towards their mental health problems instead of clustering them together under the umbrella of general health services. Future studies are needed into the nature and efficacy of mental healthcare intervention in Ethiopia.
Securing CAN-Based Cyber-Physical Systems

With the exponential growth of cyber-physical systems (CPSs), new security challenges have emerged. Various vulnerabilities, threats, attacks, and controls have been introduced for the new generation of CPS. However, there lacks a systematic review of the CPS security literature. In particular, the heterogeneity of CPS components and the diversity of CPS systems have made it difficult to study the problem with one generalized model. As the first component of this dissertation, existing research on CPS security is studied and systematized under a unified framework. Smart cars, as a CPS application, were further explored under the proposed framework and new attacks are identified and addressed. The Control Area Network (CAN bus) is a prevalent serial communication protocol adopted in industrial CPS, especially in small and large vehicles, ships, planes, and even in drones, radar systems, and submarines. Unfortunately, the CAN bus was designed without any security considerations. We then propose and demonstrate a stealthy targeted Denial of Service (DoS) attack against CAN. Experimentation shows that the attack is effective and superior to attacks of the same category due to its stealthiness and ability to avoid detection from current countermeasures. Two controls are proposed to defend against various spoofing and DoS attacks on CAN. The first one aims to minimize the attack using a mechanism called ID-Hopping so that CAN arbitration IDs are randomized so an attacker would not be able to target them. ID-Hopping raises the bar for attackers by randomizing the expected patterns in a CAN network. Such randomization hinders an attacker’s ability to launch targeted DoS attacks. Based on the evaluation on the testbed, the randomization mechanism,
Attribute Based Trust Evaluation for Secure RPL Protocol in IoT Environment

Internet of Things (IoT) is an advanced automation technology and analytics systems which connected physical objects that have access through the Internet and have their unique flexibility and an ability to be suitable for any environment. There are some critical applications like smart health care system, in which the data collection, sharing and routing through IoT has to be handled in sensitive way. The IPv6 Routing Protocol for LL(Low-power and Lossy) networks (RPL) is the routing protocols to ensure reliable data transfer in 6LOWPAN networks. However, RPL is vulnerable to number of security attacks which creates a major impact on energy consumption and memory requirements which is not suitable for energy constraint networks like IoT. This requires secured RPL protocol to be used for critical data transfer. This paper introduces a novel approach of combining a lightweight LBS (Location Based Service) authentication and Attribute Based Trust Evaluation (ABTE). The algorithm has been implemented for smart health care system and analyzed how its perform in the RPL protocol for IoT constrained environments.
A Case Study of PBL for Computer Network Course

Problem-based learning (PBL) is a student-centered teaching and learning model, in which teachers play a guiding and inspiring role, students need to cooperate with each other to solve problems. The computer network course covers a wide range of knowledge and needs to integrate theory with practice. It is very suitable for PBL teaching. In view of the general situation of teaching and learning for teachers and students in China, i.e., the passivity of teaching and learning, this paper attempts to apply PBL to the teaching of computer network course. Finally, the effectiveness of PBL is evaluated through examination and questionnaire for students.
A Low-Area, Energy-Efficient 64-Bit Reconfigurable Carry Select Modified Tree-Based Adder for Media Signal Processing

Allwin, Priscilla Sharon. M.S.E.E. , Department of Electrical Engineering, Wright State University, 2019. A Low-Area, Energy-Efficient 64-Bit Reconfigurable Carry Select Modified Tree Based Adder for Media Signal Processing. Multimedia systems play an essential part in our daily lives and have drastically improved the quality of life over time. Multimedia devices like cellphones, radios, televisions, and computers require low-area and low-power reconfigurable adders to process greedy computation algorithms for the real-time audio/video signal and image processing such as discrete cosine transform, inverse discrete cosine transform, and fast Fourier transform, etc. In this thesis, a novel 64-bit reconfigurable adder is proposed and implemented to reduce the area and power consumption. This adder can be run-time reconfigured to different reconfigurable word lengths, i.e., one 64bit, two 32-bits, four 16-bits or eight 8-bits addition, depending on the partition signal command. A Carry Select Modified Tree (CSMT) based adder is used in the reconfigurable adder to reduce the area by 22 % and the power consumption by 47 % when compared to the conventional design. The proposed adder, implemented in 180 nm CMOS technology at 1.8-volt supply, has a worst-case Delay of 20.67 nanoseconds with an overall area of 36.417 μm and power consumption of 447.93 μW
Optimal crop water allocation coupled with reservoir operation by Genetic Algorithm and Non-Linear Programming (GA-NLP) hybrid approach

This paper presents a Genetic Algorithm and Non-Linear Programming (GA-NLP) hybrid model to derive steady state optimal reservoir operating policies for a reservoir. In the present study, the objective is maximizing the yields of all the crops in the command area considering yield response to water deficit subject to constraints on reservoir water balance, storage bounds, channel capacities and minimum water requirements. Decision variables of the model are fortnight water allocations to each crop grown under left & right main canals and d/s releases. The model developed is applied to the Nagarjunasagar reservoir in Andhra Pradesh, India. Various levels of dependable inflows entering into the reservoir (75%, 80% & 85%) are considered in the present study. Optimal policy obtained by proposed model are validated through simulation and compared with Standard operating policy (SOP). The results obtained using the proposed model gives guidelines to reservoir managers to take decisions. Results reveal that GA-NLP model can be effectively used for optimal allocation of limited available water resources to any reservoir.