Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Purposive Legal Argumentation and Case Outcome Prediction using Argument Schemesin the Value Judgment Formalism

Artificial Intelligence and Law studies how legal reasoning can be formalized in order to eventually be able to develop systems that assist lawyers in the task of researching, drafting and evaluating arguments in a professional setting. To further this goal, researchers have been developing systems, which, to a limited extent, autonomously engage in legal reasoning, and argumentation on closed domains. This dissertation presents the Value Judgment Formalism and its experimental implementation in the VJAP system, which is capable of arguing about, and predicting outcomes of, a set of trade secret misappropriation cases. VJAP argues about cases by creating an argument graph for each case using a set of argument schemes. These schemes use a representation of values underlying trade secret law and effects of facts on these values. VJAP argumentatively balances effects in the given case and analogizes it to individual precedents and the value tradeoffs in those precedents. It predicts case outcomes using a confidence measure computed from the argument graph and generates textual legal arguments justifying its predictions. The confidence propagation uses quantitative weights assigned to effects of facts on values. VJAP automatically learns these weights from past cases using an iterative optimization method. The experimental evaluation shows that VJAP generates case-based legal arguments that make plausible and intelligent-appearing use of precedents to reason about a case in terms of differences and similarities to a precedent and the value tradeoffs that both contain. VJAP’s prediction performance is promising when compared to machine learning algorithms, which do not generate legal arguments. Due to the small case base, however, the assessment of prediction performance was not statistically rigorous. VJAP exhibits argumentation and prediction behavior that, to some extent, resembles phenomena in real case-based legal reasoning, such as realistically appearing citation graphs. The VJAP system and experiment demonstrate that it is possible to effectively combine symbolic knowledge and inference with quantitative confidence propagation. In AI\&Law, such systems can embrace the structure of legal reasoning and learn quantitative information about the domain from prior cases, as well as apply this information in a structurally realistic way in the context of new cases.
Surgeries in Hospital-Based Ambulatory Surgery and Hospital Inpatient Settings, 2014: Statistical Brief #223

Ambulatory surgery (AS), or outpatient surgery, is a planned operation for which the patient is not expected to be admitted to the hospital. Comparison of ambulatory surgery with inpatient surgery is essential for understanding utilization patterns for specific surgical procedures, including changing trends and estimates of total surgical volumes. As the number of surgical procedures capable of being safely performed in an ambulatory setting increases, comparisons of ambulatory surgery with inpatient surgery can also be useful in the evaluation of postsurgical complications, hospital cost savings, and patient experience of care surveys.
Determining the scale at which variation in WUE traits changes population yields

Functional variation is known to influence population yield, but the scale at which this happens is still unknown. Relevant signals might only reach immediate neighbors of a phenotypically diverse plant (neighbor-scale) or conversely may distribute across the population (population-scale). We use Nicotiana attenuata silenced in mitogen-activated protein kinase 4 (irMPK4), plants with low water-use efficiency (WUE), to study the scale at which water-use traits alter intraspecific population yields. In the field and glasshouse, populations with low percentages of irMPK4 plants planted among isogenic control plants produced maximum overall growth and yield. Through paired-plant and local-plant-configuration analyses, we determined that this occurred at the population scale. However, we find that this effect was not due to irMPK4’s WUE phenotype. With micro-grafting, we additionally show that MPK4-deficiency may mediate the response at the population-scale: shoot-expressed MPK4 is required for N. attenuata to change yield in response to a neighbor.
the Cardio-respiratory Fitness in Medical Students by Queen’s College Step Test: A Cross-sectional Study

Introduction: Cardio-respiratory fitness indicates the ability of circulatory system to supply oxygen toworking muscles during continuous physical activity. Maximum oxygen uptake (VO2max) is a single bestmeasure of cardio-respiratory fitness and is considered gold standard to quantify aerobic capacity. Methods:Eighty students of age group 18-25 years were taken by simple random sampling. VO2max was estimatedindirectly by following the protocol of Queen’s College Step Test (QCST) method. Results: Mean value ofVO2max for male (51.61±6.26 ml/Kg/min) and female (36.02±3.71 ml/Kg/min) was compared, which wasfound significantly higher in males than in females (p<0.001). There was negative correlation of gender(r = -0.838), body mass index (BMI) (r = -0.339), obesity category (r = -0.275), obese vs non-obese (r =-0.264) and basal pulse rate (r = -0.456) with VO2max and positive correlation of height (r = 0.592) and hoursof study(r = 0.309) with VO2max. Conclusion: This study showed that increased BMI is associated withdecreased level of VO2max in young adults. One can improve VO2max by maintaining BMI within normallimits.
Implementation of MVC Model Used on Web Development——AWDF

The paper introduces a new Web development framework based on MVC model 2, named as agile Web development framework (AWDF). Comparing with struts framework, it presents several advantages of AWDF: the possibility to reduce the difficulty and cost of system developed by AWDF; the high reusage of the system developed by AWDF,by adopting reusable data validation mechanism; the possibility of using AWDF to develop large system. Finally, the paper describes the AWDF design and the definition and responsibility of all components of the AWDF .
Transcriptional Regulation of the Acetone Carboxylase Operon via Two-Component Signal Transduction in Helicobacter pylori

PAGE The bacterial pathogen Helicobacter pylori exclusively colonizes the human gastric epithelium, causing chronic inflammation which leads to gastritis in most of those who are infected. The long-term presence of H. pylori also promotes the development of more severe conditions such as ulcers, and is a significant risk factor for gastric cancers such as adenocarcinoma. In addition to being an important human pathogen, H. pylori is also an ideal system for basic study of bacterial gene regulation. This species controls expression of a wide array of target genes in response to environmental signals using only three twocomponent signal transduction (TCST) systems: ArsRS, FlgRS, and CrdRS. Evidence of co-regulation of some genes by more than one of these pathways suggests that H. pylori compensates for this apparent deficiency in regulatory ability through a complex network of cross-regulation interactions. The acetone carboxylase operon, acxABC, may be regulated by all three of these signal transduction pathways. This extensive regulation of acetone metabolism suggests that this capability is important for H. pylori's ability to infect and survive in the gastric mucosa, and may reveal promising targets for drug development. In order to characterize the activity of transcription factors that regulate this operon, I have cloned, expressed, and purified the response regulator component of each of H. pylori's TCST systems along with the orphan response regulator HP1021. Electrophoretic mobility shift assays (EMSAs) were performed with these response regulators and DNA probes amplified from the putative upstream regulatory region of acxABC. Results confirm the direct binding of ArsR and HP1021 to the acxA promoter observed in previous studies, and suggest that as many as four ArsR binding sites may be involved in regulation of acxABC. Results also suggest that regulation of acxA by CrdR is direct and involves multiple binding sites or conformations. FlgR bound to the acxA promoter region in a nonspecific manner, suggesting that FlgR must interact with another DNA-binding protein to activate acxA transcription, or that regulation of acxA by FlgR is indirect. These results establish acxA as a model target gene for future study of how H. pylori can finely modulate transcription of a single operon via multiple direct regulatory systems and mechanisms. Furthermore, the optimization of methods and protocols in the course of this study has made additional tools for investigation of TCST networking available to our research group. Table of
Power Amplifier Effects on Frequency Localized 5G Candidate Waveforms

One important direction in advanced communication waveform studies for future wireless communications is improved spectrum localization, i.e., minimization of the power leakage outside very narrow guardbands around the useful signal band. This helps to improve the spectrum efficiency, especially in difficult radio access scenarios, like cognitive radio and dynamic spectrum access. In contrast to basic OFDM based waveforms, this allows dense frequency-division multiple access without need for precise synchronization of users. Various multicarrier and single-carrier waveforms with effective spectrum localization are available, but their spectral characteristics have so far been investigated mostly in the digital processing domain. For practical deployment, it is necessary to study the effects of practical transmitter power amplifiers (PAs) on the waveform characteristics, together with power amplifier linearization and peak-to-average power ratio (PAPR) reduction methods. This paper focuses on these issues, deriving PA backoff requirements using the soft envelope limiter model, corresponding to an ideally linearized PA, together with a generic PAPR reduction method based on windowed clipping. It is demonstrated that this method, when applied with well-linearized PA, helps to significantly reduce the required power amplifier backoff for both single-carrier and multicarrier waveforms, with the potential of reaching extremely low out-of-band radiation with realistic backoff values. The results are generally applicable to all spectrally localized waveforms.
Parallel Transfer Learning: Accelerating Reinforcement Learning in Multi-Agent Systems

Learning-based approaches to autonomic systems allow systems to adapt their behaviour to best suit their operating environment. One of the more widely used learning methods is Reinforcement Learning (RL). RL agents learn by repeatedly executing actions and observing their results, and over time a representation of how to behave well is developed. A significant issue with this approach is that it takes a long time to reach its best performance. Every action has to be experienced several times in each particular circumstance for its value to be representative of its converged value. The presence of multiple agents increases the number of experiences required. Multi-Agent Systems (MAS) are systems in which multiple agents affect a shared environment. MAS are inherently large-scale as they are composed of many interacting agents. In MAS, the cumulative effects of agents’ actions make the environment more variable. Greater variability in the outcomes of actions requires more learning as a single sample becomes less representative of the converged value. An RL system’s performance is necessarily sub-optimal while it is learning. Each learning experience is expensive, taking time and affecting the system that is being controlled. Experiences should be used as efficiently as possible to reduce the time spent learning, improving overall performance. The less time needed to learn, the better the performance of a system over its lifetime. Transfer Learning (TL) is a method of using additional knowledge to accelerate learning. It operates by taking knowledge from a source task (a process that supplies information) and reusing it in a target problem, with the aim of reducing the amount of
Tumefactive demyelination in a patient with relapsing-remitting MS on ocrelizumab

Ocrelizumab is an intravenously infused humanized monoclonal antibody that induces antibody-dependent cellular cytolysis and complement-mediated lysis of CD20-expressing B cells. Ocrelizumab reduced gadolinium-enhancing activity 94% and 95% compared with subcutaneous interferon beta-1a in relapsing-remitting MS (RRMS). 1 We report a case of tumefactive demyelination (TD) in an ocrelizumab-treated patient with RRMS.
Survey of task allocation in multi Agent systems

Task allocation is one of the most important research issues in multi Agent systems(MAS). Task allocation algorithms assign the appropriate agents to the tasks in order to achieve the global optimization.Based on the analysis of typical control architectures of MAS,the characteristics of task allocation problem in centralized MAS are compared with distributed one,and the centralized and distributed task allocation algorithms in MAS are surveyed.Finally,the future research direction of task allocation in MAS from theoretical and applied aspect is provided.
Interactive comment on "The von Kármán constant retrieved from CASE-97 dataset using a variational method" by Y. Zhang et al.

The flux-gradient similarity according to the Monin-Obukhov similarity theory depends on parameters which must be determined experimentally. These are the von-Karmanconstant and the coefficients of the universal function and, in the case of the sensible and latent heat flux, the turbulent Prandtl and Schmidt numbers, respectively. The normal way to determine these parameters is firstly to use near neutral cases of the momentum flux to determine the von-Karman-constant, and secondly to use near neutral cases of the sensible and latent heat flux to determine the turbulent Prandtl and Schmidt numbers. The third step is to use data of all stratifications to determine the coefficients of the universal function. The authors have done this the opposite way. They assumed correct coefficients of the universal function and determined errors in the
Modeling Trolling in Social Media Conversations

Social media websites, electronic newspapers and Internet forums allow visitors to leave comments for others to read and interact. This exchange is not free from participants with malicious intentions, who troll others by positing messages that are intended to be provocative, offensive, or menacing. With the goal of facilitating the computational modeling of trolling, we propose a trolling categorization that is novel in the sense that it allows comment-based analysis from both the trolls’ and the responders’ perspectives, characterizing these two perspectives using four aspects, namely, the troll’s intention and his intention disclosure, as well as the responder’s interpretation of the troll’s intention and her response strategy. Using this categorization, we annotate and release a dataset containing excerpts of Reddit conversations involving suspected trolls and their interactions with other users. For the ﬁnal version of the paper, we plan to identify the difﬁcult-to-classify cases in our corpus and suggest potential solutions for them.
Tracking Control of Vertical Pneumatic Artificial Muscle System Using PID

The advantages of pneumatic system such as compactness, high power to weight ratio, ease of maintenance, cleanliness and inherent safety led to the development of McKibben muscle and pneumatic artificial muscle (PAM). However, the air compressibility and the lack of damping ability of PAM bring dynamic delay to the pressure response and causes oscillatory motion to occur. It is not easy to realize the motion with high accuracy and high speed due to all the non-linear characteristics of pneumatic system. In this paper, we present a vertical PAM system with a simple PID controller to control the motion of the PAM. The experiment setup is explained and Ziegler Nichols tuning method is used in getting the approximation PID parameters. The effectiveness of the proposed control algorithm is demonstrated through experiments.
Conjunct model of cloud & client computing based on multi-agent

In order to utilize the resources of all nodes in networks fully,a novel conjunct model of cloud client computing based on multi-agent was proposed,dividing the computing environment into three layers in accordance with the type of node and adopting agent to represent the behavior and resource of node to realize more efficient resource sharing and collaboration.The model takes full account of available resources owned by terminal nodes and deploys tasks in an orderly manner to the core nodes,servers and terminal nodes in order to maximize the efficiency of the resource utilization.To assure that tasks could be completed efficiently and reliably,a multi-agent-based collaborative multi-ring management mechanism was also presented,which is particularly suitable for the cloud client conjunct computing environment and propitious to enhance the stability of resources sharing and nodes cooperation and relieve the load of core nodes.Finally,the construction method of experimental system and application demonstration based on the model was provided for reference.
Research on IR and Visual Image Registration Based on Mutual Information and Ant Colony Algorithm

IR sensor and visual sensor are widely used in image system.The information and robustness of the image can be enhanced with effective fusion of images from the two kinds of sensors.And it can increase the ability of analysis and recognition for image information.Fast and accurate image registration is the key premise of image fusion.An algorithm of IR and visual image registration based on mutual information and ant colony algorithm is presented.
The role of p90 ribosomal S6 kinases (RSKs) in Steroid signalling

The p90 ribosomal S6 kinases (RSKs) are a family of serine/threonine kinases consisting of four isoforms (RSK1-4), which regulate key cellular processes including cell cycle, proliferation, motility and survival. Among the several transcription factors targeted by RSKs, several studies have identified the Steroid Receptors (SRs) as substrates of the RSKs. SRs are a subfamily of the nuclear receptor superfamily consisting of five proteins (androgen, glucocorticoid, estrogen, progesterone and mineralocorticoid receptors). These proteins regulate gene expression thus are involved in crucial biological processes, including organ development and maintenance, the immune system, neuroprotection and metabolic homeostasis. Importantly, SRs are the main drivers of hormone driven cancers. Therefore, we hypothesised that the RSKs could play an important part in SR signalling within hormone driven cancer development and progression. This was investigated using recombinant DNA techniques to incorporate the RSKs genes into the mammalian expression vector PCDNA 3.1(+) and to produce phospho-mimetic mutants. Luciferase assays were used to determine optimal hormone and DNA concentrations, the role of RSKs on SR activity, the activity of the endogenous MAPK pathway and the significance of phosphorylation state. The PMA experiments indicated that in most cases PMA does not induce a significant change to RSK activity, thus the endogenous MAPK pathway is sufficiently active. The mutant experiments suggested that the phospho-mimetic mutation used does not cause constitutive activation of the RSKs. Together considering all experiments it was illustrated that the RSKs have differential effects on SR signalling. All the RSKs increased AR and GR activity despite some contradictions within the data. RSK4 significantly decreased ERα activity and RSK3 significantly increased PR activity.
Bisifusarium Delphinoides, an Emerging Opportunistic Pathogen in a Burn Patient with Diabetes Mellitus

Abstract An 82-year-old man with diabetes was admitted to the emergency department with a third-degree burn on his left leg. The deep swab specimen from his left leg was cultured on Sabouraud dextrose agar without cycloheximide and incubated at 25 °C for 5 days. On the basis of morphological characteristics and multigene phylogenetic analyses of the internal transcribed spacer region of ribosomal DNA and partial fragments of beta-tubulin and translation elongation factor 1-alpha, the causal agent of fungal skin infection was identified as Bisifusarium delphinoides, which was newly introduced by accommodating a Fusarium dimerum species complex. Thus, we describe here the first case of skin infection caused by B. delphinoides on a burn patient with diabetes mellitus based on morphological observation and molecular analysis.
A method of mining adaptively the pattern between disease and pathogeny of dam

Diagnosing dam disease is a process of discovering knowledge on the pattern between of disease and pathogeny. The information on dam safety needs to be handled and analyzed comprehensively. Based on reduct theory and information system of rough sets theory, this paper presented the mechanism and algorithm for mining the pattern between of dam disease and pathogeny. The genesis on cracks of a concrete arch dam is found by using this method.
Foretell: aggregating distributed, heterogeneous information from diverse sources using market-based techniques

Predicting the outcome of uncertain events that will happen in the future is a frequently indulged task by humans while making critical decisions. The process underlying this prediction and decision making is called information aggregation, which deals with collating the opinions of different people, over time, about the future event's possible outcome. The information aggregation problem is non-trivial as the information related to future events is distributed spatially and temporally, the information gets changed dynamically as related events happen, and, finally, people's opinions about events' outcomes depends on the information they have access to and the mechanism they use to form opinions from that information. This thesis addresses the problem of distributed information aggregation by building computational models and algorithms for different aspects of information aggregation so that the most likely outcome of future events can be predicted with utmost accuracy. We have employed a commonly-used market-based framework called a prediction market to formally analyze the process of information aggregation. The behavior of humans performing information aggregation within a prediction market is implemented using software agents which employ sophisticated algorithms to perform complex calculations on behalf of the humans, to aggregate information efficiently. We have considered five different yet crucial problems related to information aggregation, which include: (i) the effect of variations in the parameters of the information being aggregated, such as its reliability, availability, accessibility, etc., on the predicted outcome of the event, (ii) improving the prediction accuracy by having each human (software-agent) build a more accurate model of other humans' behavior in the prediction market, (iii) identifying how various market parameters effect its dynamics and accuracy, (iv) applying information aggregation to the domain of distributed sensor information fusion, and, (v) aggregating information on an event while considering dissimilar, but closely-related events in different prediction markets. We have verified all of our proposed techniques through analytical results and experiments while using commercially available data from real prediction markets within a simulated, multi-agent based prediction market. Our results show that our proposed techniques for information aggregation perform more efficiently or comparably with existing techniques for information aggregation using prediction markets.