Miracle-dz/UniFAR-FTUs huggingface dataset:
The stat of the art of terrestrial eco-hydrological mechanisms and numerical simulation

Eco-hydrology is a science related to the overlapping parts of ecology and hydrology, studying the relationship between vegetation and water cycle in terrestrial and aquatic ecosystems. Climate change has significant effects on carbon and water cycles in terrestrial ecosystem, which have received extensive attentions in recent years. This paper provides a detailed analysis to the major parts of carbon and water cycles, and the relationship between these processes and environmental elements under climate change circumstances. This paper also summarized the classification and characteristics of eco-hydrological simulation models. Eco-hydrological simulation is an important approach to study the interaction between ecological and hydrological processes. The interaction mechanism and coupling of vegetation and hydrology at different scales is still a challenge in eco-hydrological research. Besides, researches on the response of eco-hydrological processes to climate change and human activities, and their feedback effects on climate are the most vigorous in this field.
Simplification of Complex Physiologically Based Pharmacokinetic Models of Monoclonal Antibodies

Monoclonal antibodies (mAbs) exhibit biexponential profiles in plasma that are commonly described with a standard two-compartment model with elimination from the central compartment. These models adequately describe mAb plasma PK. However, these models ignore elimination from the peripheral compartment. This may lead to underestimation of the volume of distribution of the peripheral compartment and thus over-predicts concentration in the peripheral compartment. We developed a simple and physiologically relevant model that incorporates information on binding and dissociation rates between mAb and FcRn receptor, mAb uptake, reflection, and catabolic degradation. We employed a previously published PBPK model and, with assumptions regarding rates of processes controlling mAb disposition, reduced the complex PBPK model to a simpler circular model with central, peripheral, and lymph compartments specifying elimination from both central and peripheral. We successfully applied the model to describe the PK of an investigational mAb. Our model presents an improvement over standard two-compartmental models in predicting whole-body average tissue concentrations while adequately describing plasma PK with minimal complexity and physiologically more meaningful parameters.
Lessons from Cognitive Science for Cognitive Neuropsychology

This article presents a sobering view of the discipline of cognitive neuropsychology as practised over the last three or four decades. Our judgement is that, although the study of abnormal cognition resulting from brain injury or disease in previously normal adults has produced a catalogue of fascinating and highly selective deficits, it has yielded relatively little advance in understanding how the brain accomplishes its cognitive business. We question the wisdom of the following three ‘choices’ in mainstream cognitive neuropsychology: (a) single-case methodology, (b) dissociation between functions as the most important source of evidence, and (c) a central goal of diagramming the functional architecture of cognition rather than specifying how its components work. These choices may all stem from an excessive commitment to strict and fine-grained modularity. Although different brain regions are undoubtedly specialised for different functions, we argue that parallel and interactive processing is a better assumption about cognitive processing. The essential goal of specifying representations and processes can, we claim, be significantly assisted by computational modelling which, by its very nature, requires such specification.
Broca's Area and Language Processing: Evidence for the Cognitive Control Connection

A long-standing pursuit in cognitive neuropsychology has been to understand the role of Broca’s area in language processing. Although a prevailing view has been to equate this region with grammatical abilities in both production and comprehension, a host of recent evidence from brain imaging and patient research has revealed a rather general role for this patch of cortex in complex cognition, even when grammatical performance is untapped—namely, that it regulates mental activity when there is need to resolve among competing representations. In this light, a recent proposal hypothesizes that this broad ‘cognitive control’ function of Broca’s area similarly serves language processing: Broca’s area is responsible for biasing production and comprehension processes when there are strong demands to resolve competition among incompatible characterizations of linguistic stimuli. Some questions that have been asked within this framework are as follows: Does Broca’s area help speakers produce an appropriate word when many alternatives are equally plausible? Does it permit readers and listeners to successfully understand words and sentences, even when the input is ripe for misinterpretation? In the current article, we review new empirical evidence from various fields that supports such an account. A central piece to this discussion is how careful scrutiny of language performance, under varying degrees of cognitive control demands, may shed light on how to suitably describe the idiosyncratic language traits of patients with focal Broca’s area damage, who are decidedly not agrammatic. When explaining how he drives home every weekend, Patient A says:
Capturing Conformational States in Proteins Using Sparse Paramagnetic NMR Data

Capturing conformational changes in proteins or protein-protein complexes is a challenge for both experimentalists and computational biologists. Solution nuclear magnetic resonance (NMR) is unique in that it permits structural studies of proteins under greatly varying conditions, and thus allows us to monitor induced structural changes. Paramagnetic effects are increasingly used to study protein structures as they give ready access to rich structural information of orientation and long-range distance restraints from the NMR signals of backbone amides, and reliable methods have become available to tag proteins with paramagnetic metal ions site-specifically and at multiple sites. In this study, we show how sparse pseudocontact shift (PCS) data can be used to computationally model conformational states in a protein system, by first identifying core structural elements that are not affected by the environmental change, and then computationally completing the remaining structure based on experimental restraints from PCS. The approach is demonstrated on a 27 kDa two-domain NS2B-NS3 protease system of the dengue virus serotype 2, for which distinct closed and open conformational states have been observed in crystal structures. By changing the input PCS data, the observed conformational states in the dengue virus protease are reproduced without modifying the computational procedure. This data driven Rosetta protocol enables identification of conformational states of a protein system, which are otherwise difficult to obtain either experimentally or computationally.
Scene text recognition with high performance CNN classifier and efficient word inference

The recognition of text in natural scene images is a practical yet challenging task due to the large variations in backgrounds, textures, fonts, and illumination conditions. In this paper, we propose a highly accurate character recognition model by utilizing the representational power of a specially designed Convolutional Neural Network (CNN). Based on the recognition model, we also develop an efficient post processing approach for error correction and hypothesis re-verification. Character and word image recognition experiments on two public datasets, namely the ICDAR 2003 Robust Reading dataset and the Street View Text (SVT) dataset both show that the proposed approach provides superior or comparable results to the state-of-the-art techniques.
A Fast Algorithm of Computing Word Similarity

Computing distributional similarity is an effective strategy for finding synonyms. The time complexity of the naive nearest-neighbor approach of computing distributional word similarity is O(n*n*m), it is inefficient for accurately representing synonymy using large corpus. We find a parse property of triple that the growth rate of average triples number of each word leveled off as corpus's size increases. Using this property we design a fast algorithm for computing word similarity whose time complexity is O(n*n). We demonstrate the efficiency of this algorithm based on the English Gig word corpus.
Repurposing Clinical Data for Cancer Research With Formal Privacy Protections President ’ s Cancer Panel December 14 , 2010 Bethesda , Maryland

A Movement Toward Clinical Information Reuse The healthcare community is in the midst of an information technology (IT) revolution. Over the past several decades, advances in health IT, such as electronic medical recor d (EMR) systems, have facilitated the collection of large quantities of finely detailed personal data. At the sam time, the field of personalized medicine has been fueled by the aid of a number of ventures, such as the Personalized Health Care Initiative of the U.S. Department of Health and Human Service s. In turn, there has been a magnification in the desire to utilize information embedded in EMRs to t ailor a patient’s preventative care and treatment regimen to their specific evidence, which is crucial fo r the management of cancer. Given the detail of information in EMRs, and their increasing integration wit h biorepositories, EMRs are poised to become a critical component of biomedical research endeavors. More importantly, EMR systems can amass data on sizable populations, whic h can be automatically mined for a substantial range of clinical phenomena, on a massive scale. The opportunities are awesome, and demonstration projects, such as those undertaken by the NIH-sponsored Electr onic Medical Record and Genomics Network (eMERGE) consortium, have shown that EMRs can be repurpos d for large-scale clinical phenotyping and subsequent genome-wide association studies (GWAS). Though EMRs contain noise and error, eMERGE investigations indicate that highly-specific clini al phenotype models can be specified for such systems in a manner that is reusable acros s diverse systems. Already, models for over ten clinical phenotypes have been developed and utilized in GWAS with over 17,000 patients. The EMERGE network currently consists of five primary sites, including the Group Health Cooperative of Puget Sound, Marshfield Clinic, Mayo Clinic, Northwestern University, and Vanderbilt University, and a recent RFA from the NIH suggests that eMERGE will expand in size. Moreov e , ther initiatives, such as the Pharmacogenomics Research Network, are broadening in scope and reuse of EMR systems as well. Much more could be said about how phenotyping from EMRs can be achieved for cancer research purposes, but the goal of this testimony is to focus on the privacy issues, and so I refer you to the eMERGE website for further references on this topic (http://ww w.g as.net).
A survey of attitudes toward computers

What do people really think about computers and their impact? In 1970, a study of people's attitudes in North America showed computers to be regarded as either “beneficial tools of mankind” or as “awesome thinking machines. ” A recent survey taken in Australia and reported in this article, though, suggests there may have been a change in attitudes over the past decade. The Australians expressed much concern over the computer's possible disemploying and dehumanizing effects—as well as disquiet over the control computers could exercise over their lives. If these attitudes are typical beyond the shores of Australia, they could create a barrier to the widespread acceptance and application of computers around the world.
Adaptive backstepping tracking control of the Stewart platform

This paper presents an adaptive backstepping control approach for the motion control of a Stewart platform. The control scheme is proposed given that the overall system parameters are subject to uncertainties while only the positions and velocities of links are measurable. To achieve high performance tracking control of a 6 DOF Stewart platform normally requires the full knowledge of the system dynamics. In this paper, some important properties of the dynamics of the Stewart platform have been derived and exploited to develop an adaptive backstepping controller which can drive the motion tracking error to zero asymptotically. Stability analysis based on Lyapunov theory is performed to guarantee that the controller design is stable. Finally, the experimental results confirm the effectiveness of our control design.
Adjustment of home posture of a biped humanoid robot using an inertial sensor and force torque sensors

In the walking control of a biped humanoid robot, the walking performance is seriously affected by the initial home posture. An important thing is that the initial home posture of real robot is slightly different at every setting because a zero position of joint is not exactly the same. Accurate and consistent initial home posture is essential when we compare and analyze walking control algorithms. In general, the conventional method for electric motor uses an incremental encoder with a limit switch or an absolute encoder such as potentiometer to make a zero position. However, this method for the multi-axis humanoid robot is difficult, time-consuming, and inaccurate. Furthermore, it has the disadvantage that additional limit switch or absolute encoder can interfere with the design objective of compactness. This paper describes a novel adjustment method of home posture for a humanoid robot utilizing incremental encoders, an inertial sensor and force torque sensors. Four kinds of controllers are proposed and adjusted offsets are measured when outputs of the controllers are converged. The experimental results from KHR-2 show the effectiveness of the proposed algorithm.
Self-reported patient safety competence among Canadian medical students and postgraduate trainees: a cross-sectional survey

Background Quality and patient safety (PS) are critical components of medical education. This study reports on the self-reported PS competence of medical students and postgraduate trainees. Methods The Health Professional Education in Patient Safety Survey was administered to medical students and postgraduate trainees in January 2012. PS dimension scores were compared across learning settings (classroom and clinical) and year in programme. Results Sixty-three percent (255/406) of medical students and 32% (141/436) of postgraduate trainees responded. In general, both groups were most confident in their learning of clinical safety skills (eg, hand hygiene) and least confident in learning about sociocultural aspects of safety (eg, understanding human factors). Medical students’ confidence in most aspects of safety improved with years of training. For some of the more intangible dimensions (teamwork and culture), medical students in their final year had lower scores than students in earlier years. Thirty-eight percent of medical students felt they could approach someone engaging in unsafe practice, and the majority of medical students (85%) and postgraduate trainees (78%) agreed it was difficult to question authority. Conclusions Our results suggest the need to improve the overall content, structure and integration of PS concepts in both classroom and clinical learning environments. Decreased confidence in sociocultural aspects of PS among medical students in the final year of training may indicate that culture in clinical settings negatively affects students’ perceived PS competence. Alternatively, as medical students spend more time in the clinical setting, they may develop a clearer sense of what they do not know.
Evaluation of Permeable Reactive Barrier Performance

Federal Remediation Technologies Roundtable <www.frtr. gov> ii ACKNOWLEDGMENTS This report on the performance of permeable reactive barriers (PRB) for groundwater remediation was prepared under the auspices of the Federal Remediation Technologies Roundtable, a collaborative effort among federal agencies involved in hazardous waste site cleanup. Three United States (U.S.) government agencies, the Department of Defense (DoD), Department of Energy (DOE), and United States Environmental Protection Agency (EPA), as well as the Interstate Technologies and Regulatory Council (ITRC) contributed to the report, which is a concise summary of the conclusions and recommendations of the three agencies' individual studies at different sites. are the coordinating partners for the DoD study. Battelle coordinated the preparation of this report for the Tri-Agency PRB Initiative, a consortium that was formed to leverage the resources, expertise, and site experience of the three agencies. The Tri-Agency PRB Initiative operates under the umbrella of the Federal Remediation Technologies Roundtable. Charles Reeter is the project officer at NFESC for this study. Matt Turner with New Jersey Department of Environmental Protection (NJDEP) coordinated the ITRC contribution and played an important role in disseminating the study results through other ITRC members and through the ITRC website. Other participants who contributed strongly (to the DoD study, in particular) include Steve White with U.S. Army Corps of Engineers (USACE), Timothy McHale with Air Force Research Laboratory (AFRL), and Robert Edwards with Waste Policy Institute (representative for the Air Force Center for Environmental Excellence). The DoD study was funded by the Environmental Security Technology Certification Program (ESTCP) and the Strategic Environmental Research and Development Program (SERDP). Other individuals who made significant contributions to the EPA study include
Out-of-Band mmWave Beamforming and Communications to Achieve Low Latency and High Energy Efficiency in 5G Systems

We propose a hybrid RF/millimeter wave (mmWave) architecture for $5$G cellular systems. Communication in the mmWave band faces significant challenges due to variable channels, intermittent connectivity, and high energy usage. Moreover, speeds for electronic processing of data is of the same order as typical rates for mmWave interface. Therefore, the use of complex algorithms for tracking channel variations and adjusting resources accordingly is practically out-of-reach. To alleviate the challenges associated with mmWave communications, our proposed architecture integrates the RF and mmWave interfaces for beamforming and data transfer, and exploits the spatio-temporal correlations between the interfaces. Based on extensive experimentation in indoor and outdoor settings, we demonstrate that an integrated RF/mmWave signaling and channel estimation scheme can remedy the problem of high energy usage and delay associated with digital and analog beamforming, respectively. In addition, cooperation between two interfaces at the higher layers effectively addresses the high delays caused by highly intermittent connectivity in mmWave channels. Subsequently, we formulate an optimal scheduling problem over the RF and mmWave interfaces where the goal is to maximize the delay-constrained throughput of the mmWave interface. We prove using subadditivity analysis that the optimal scheduling policy is based on a single threshold that can be easily adopted despite high link variations. We design an optimal scheduler that opportunistically schedules the packets over the mmWave interface, while the RF link acts as a fallback mechanism to prevent high delay.
On screen randomized blank keyboard

Online banking has become increasingly popular globally because it is so easy and convenient for internet users to manage their bank accounts from anywhere of the world at any time. The internet enhanced the user experience of banking activities dramatically. Online banking users' faces a wide range of security risks such brute force attacks, key logging attacks, shoulder surfing attacks, etc. The design of secure authentication protocols is quite challenging, considering that various kinds of root kits reside in PCs to observe the users behavior and to make PCs untrusted devices. Involving human in authentication protocols is not easy because of their limited capability of computation and memorization. Therefore relying on users to enhance security will degrades the usability. On the other hand relaxing assumptions and vigorous security design to improve the user experience can lead to security breaches that can harm the users trust. In this paper, a password based visual authentication protocol using random blank keyboard with some special symbols associated with each character is proposed. Using this new keyboard, users can enter their credentials. This is effective against the key logging attacks and shoulder surfing attacks by increasing the amount of short term memory required in an attack.
Big Data Clustering using Data Streams Approach

In this paper we propose to process big data using a data streams approach. The data set is divided into subsets, each subsets is considered as a time window from a data stream. Our approach uses a neighborhood-based clustering. Instead of processing each new element one by one, we propose to process each group of new elements simultaneously. A clustering is applied on each new group using neighborhood graphs. The obtained clusters are then used to incrementally construct a representative graph of the data. The data graph is visualized in real time with specific visualizations that reflect the processing algorithm. To validate the approach, we apply it to different data streams and we compare it with known data stream clustering approaches.
Silicene-based DNA nucleobase sensing

We propose a DNA sequencing scheme based on silicene nanopores. Using first principles theory, we compute the electrical properties of such pores in the absence and presence of nucleobases. Within a two-terminal geometry, we analyze the current-voltage relation in the presence of nucleobases with various orientations. We demonstrate that when nucleobases pass through a pore, even after sampling over many orientations, changes in the electrical properties of the ribbon can be used to discriminate between bases.
Applications of SDR exact-ML criterion to tree-searching detection for MIMO systems

In previous work, we proposed the positive diagonal values (PDV) criterion, which is an exact-ML criterion of semidefinite relaxation (SDR) optimality condition. In this paper, we apply the PDV criterion to the tree-searching based MIMO detection by two ways. The first application is node-pruning algorithm for depth first search such as sphere decoding (SD). The proposed node-pruning algorithm using PDV criterion is not based on the Euclidean distance mostly used for node-pruning algorithm, instead, it uses an absolute test in each node so that it can be worked independently with many existing node-pruning algorithms. Furthermore, the proposed node-pruning algorithm can guarantee the exact-ML performance and reduce the number of nodes visited significantly. The second application is K-best algorithm of breadth first search. The proposed K-best algorithm takes K candidates at each stage based on PDV criterion. As a result, the proposed K-best algorithm can achieve near-ML performance.
Draft Genome Sequence of a Biocontrol Rhizobacterium, Chryseobacterium kwangjuense Strain KJ1R5, Isolated from Pepper (Capsicum annuum)

ABSTRACT Strain KJ1R5 of the rhizobacterium Chryseobacterium kwangjuense is an effective biocontrol agent against Phytophthora blight of pepper caused by a destructive soilborne oomycete, Phytophthora capsici. Here, we present the draft genome sequence of strain KJ1R5, which contains genes related to biocontrol, plant growth promotion, and environmental stress adaptation.