Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Access Control and Resource Allocation for Concurrent and Massive Access of M2M Devices

Machine-to-machine (M2M) communications, also known as machine-type communications (MTC) in 3GPP LTE systems, provide autonomous connectivity between machines without human intervention to create new service, e.g., the Internet of Things and the smart grid. M2M communications normally involve a large number of MTC devices (MTCDs) to support a variety of sensor applications. Consequently, concurrent and massive access attempts of MTCDs to radio access networks (RANs) may cause intolerable delay, packet loss, and even service unavailability. In this paper, we propose a joint optimal physical random access channel (PRACH) resource allocation and access control mechanism to address the performance degradation caused by concurrent and massive access attempts of MTCDs in LTE systems. We define the notion of random access efficiency and formulate an optimization problem for maximization of the random access efficiency with random access delay constraint. We also propose a dynamic resource allocation and access control algorithm based on estimation of the number of MTCDs for a system with dynamically varying numbers of massive MTCDs. Then, an analytical model is provided using a discrete-time Markov chain for the proposed mechanism. The effectiveness of the proposed algorithm is demonstrated via analysis and simulations. The proposed algorithm was able to maintain the optimal random access efficiency while satisfying the average random access delay requirement of MTCDs to handle massive and dynamic MTCDs per cell.
Local Relationship between Global-Flash Multifocal Electroretinogram Optic Nerve Head Components and Visual Field Defects in Patients with Glaucoma

Purpose. To investigate the local relationship between quantified global-flash multifocal electroretinogram (mfERG) optic nerve head component (ONHC) and visual field defects in patients with glaucoma. Methods. Thirty-nine patients with glaucoma and 30 normal controls were enrolled. The ONHC amplitude was measured from the baseline to the peak of the second positive deflection of the induced component. The ONHC amplitude was normalized by dividing ONHC amplitude by the average of seven largest ONHC amplitudes. The ONHC amplitude ratio map and ONHC deficiency map were constructed. The local relationship between the ONHC measurements and visual field defects was evaluated by calculating the overlap between the ONHC deficiency maps and visual field defect plots. Results. The mean ONHC amplitude measurements of patients with glaucoma (6.01 ± 1.91 nV/deg2) were significantly lower than those of the normal controls (10.29 ± 0.94 nV/deg2) (P < 0.001). The average overlap between the ONHC deficiency map and visual field defect plot was 71.4%. The highest overlap (75.0%) was between the ONHC ratios less than 0.5 and the total deviations less than 5%. Conclusions. The ONHC amplitude was reduced in patients with glaucoma compared to that in normal controls. Loss of the ONHC amplitude from the global-flash mfERG showed a high local agreement with visual field defects in patients with glaucoma.
Stability analysis of finite-level quantized linear control systems

In this paper we investigate the stability of discrete-time linear time-invariant systems subject to finite-level logarithmic quantized feedback. Both state feedback and output feedback are considered. We develop an LMI approach to estimate, for a given controller and a given finite-level quantizer, a set of admissible initial states and an associated attractor set in a neighborhood of the origin such that all state trajectories starting in the first set will converge to the attractor in a finite time and will never leave it. Furthermore, when these two such sets are a priori specified, we develop sufficient conditions for designing a suitable state or output feedback controller, along with a finite-level logarithmic quantizer.
Changes in Volatile and Non-Volatile Flavor Chemicals of “Valencia” Orange Juice over the Harvest Seasons

Florida “Valencia” oranges have a wide harvest window, covering four months after first reaching the commercial maturity. However, the influence of harvest time on juice flavor chemicals is not well documented, with the exception of sugars and acids. Therefore, we investigated the major flavor chemicals, volatile (aroma), non-volatile (taste) and mouth feel attributes, in the two harvest seasons (March to June in 2007 and February to May in 2012). Bitter limonoid compounds, limonin and nomilin, decreased gradually. Out of a total of 94 volatiles, 32 increased, 47 peaked mid to late season, and 15 decreased. Juice insoluble solids and pectin content increased over the season; however, pectin methylesterase activity remained unchanged. Fruit harvested in the earlier months had lower flavor quality. Juice from later harvests had a higher sugar/acid ratio with less bitterness, while, many important aroma compounds occurred at the highest concentrations in the middle to late season, but occurred at lower concentrations at the end of the season. The results provide information to the orange juice processing industry for selection of optimal harvest time and for setting of precise blending strategy.
A codebook-driven speech enhancement method by exploiting speech harmonicity

In this paper, we present a novel codebook-driven speech enhancement method, which applies speech harmonic structure. Our algorithm is able to remove more residual noise in harmonic bands because we combine speech harmonic structure and codebook. Firstly, we utilize the speech harmonic structure to estimate prior speech presence probability. Then we use it to appraise the noise autoregressive (AR) spectral shapes for speech enhancement application. In addition, the prior speech presence probability is also used to modify the Wiener filter. Finally, by combining the clean speech AR spectral shape codebook, we can build the modified Wiener filter to acquire enhanced speech signal. Comparing with conventional codebook-driven method, the experiments derived from four typical noises show that the proposed algorithm is able to achieve a better performance.
Permanent scatterers in space-surface bistatic SAR using Beidou-2/Compass-2 as illuminators: Preliminary experiment results and analysis

Beidou-2/Compass-2 inclined geosynchronous orbit satellites have short revisit time, which provides possibility for bistatic SAR differential interferometry application. But the orbit is not accurately repeated and the baseline grows with the time, which can result in deteriorating spatial coherence coefficient and may invalidate the tradition differential interferometry method. In order to implement accurate deformation monitoring, permanent scatterer technique could be utilized. However, the existence of permanent scatterers in this kind of system has not been demonstrated so far. In this paper, validation experiments are presented to validate the feasibility for SS-BiSAR differential interferometry using permanent scatterers. The echoes of urban region in 10 different days with time span of 12 days are collected and processed. The coherences coefficients between different days and the amplitude dispersion are analyzed. The result shows that under the amplitude dispersion threshold of 0.25 many pixels can be identified as permanent scatters. Thus the existence of permanent scatterers in this kind of system is demonstrated, showing its potential to implement local area deformation monitoring.
A Lightweight Front-end Tool for Interactive Entity Population

Entity population, a task of collecting entities that belong to a particular category, has attracted attention from vertical domains. There is still a high demand for creating entity dictionaries in vertical domains, which are not covered by existing knowledge bases. We develop a lightweight front-end tool for facilitating interactive entity population. We implement key components necessary for effective interactive entity population: 1) GUI-based dashboards to quickly modify an entity dictionary, and 2) entity highlighting on documents for quickly viewing the current progress. We aim to reduce user cost from beginning to end, including package installation and maintenance. The implementation enables users to use this tool on their web browsers without any additional packages --- users can focus on their missions to create entity dictionaries. Moreover, an entity expansion module is implemented as external APIs. This design makes it easy to continuously improve interactive entity population pipelines. We are making our demo publicly available (this http URL).
Association of severe hypoglycemia with depressive symptoms in patients with type 2 diabetes: the Fukuoka Diabetes Registry

Objective Although many studies have investigated the clinical characteristics of patients with diabetes with depression in Western populations, there is a lack of information regarding other ethnicities. We studied the association between clinical characteristics and depressive symptoms in Japanese patients with type 2 diabetes. Methods A total of 4218 Japanese patients with type 2 diabetes who were not taking antidepressants were divided into four groups according to the Center for Epidemiologic Studies Depression Scale (CES-D) score. The relationship between the severity of depressive symptoms and clinical parameters was examined cross-sectionally. Results After multivariate adjustments, the severity of depressive symptoms was significantly associated with body mass index, leisure-time physical activity, current smoking, sleep duration, sucrose intake, skipping breakfast, insulin use, severe hypoglycemia, dysesthesia of both feet, history of foot ulcer, photocoagulation, ischemic heart disease, and stroke. ORs for severe hypoglycemia increased significantly with the CES-D score in 2756 sulfonylurea and/or insulin-treated patients after multivariate adjustment including age, sex, duration of diabetes, glycated hemoglobin, insulin use, self-monitoring of blood glucose, leisure-time physical activity, skipping breakfast, dysesthesia of both feet, ischemic heart disease, and stroke (CES-D score ≤9, referent; 10–15, OR 1.64; 16–23, OR 2.09; ≥24, OR 3.66; p for trend <0.01). Conclusions Severe hypoglycemia was positively associated with the severity of depressive symptoms in Japanese patients with type 2 diabetes independent of glycemic control, insulin therapy, lifestyle factors, and diabetic complications. As both severe hypoglycemia and depression are known risk factors for morbidity and mortality in patients with diabetes, clinicians should be aware of this association. UMIN Clinical Trial Registry 000002627.
Combining hardware and software instrumentation to classify program executions

Several research efforts have studied ways to infer properties of software systems from program spectra gathered from the running systems, usually with software-level instrumentation. One specific application of this general approach, which is the focus of this paper, is distinguishing failed executions from successful executions. While existing efforts appear to produce accurate classifications, detailed understanding of their costs and potential cost-benefit tradeoffs is lacking. In this work, we present a hybrid instrumentation approach which uses hardware performance counters to gather program spectra at very low cost. This underlying data is further augmented with data captured by minimal amounts of software-level instrumentation. We also evaluate this hybrid approach by comparing it to other existing approaches. We conclude that these hybrid spectra can reliably distinguish failed executions from successful executions at a fraction of the runtime overhead cost of using software-only spectra.
Fine-grained power gating of datapath using FSM

As a fine-grained power gating method for achieving greater power savings, our approach takes advantage of the finite state machine with a datapath (FSMD) characteristic which shows sequential idleness among subcircuits. In an FSMD-based power gating, while only an active subcircuit is expected to be turned on, more subcircuits should be activated due to the power overhead. To reduce the number of missed opportunities for power savings, we deactivated some of the turned-on subcircuits by slowing the FSMD down and predicting its behavior. Our microprocessor experiments showed that the power savings are close to the upper bound.
Collocation analysis: a method for conceptualizing and understanding narrative data

In light of the newly defined field of narrative inquiry, this article examines ways in which narrative functions can be used during the analytical and interpretative phases of qualitative research. Using Mishler’s typology as a starting point, it questions the practice of dividing storied data into small bits and suggests that there is a need for a wider range of grounded analytical practices - practices based on the functions and operations of narrative itself. Specifically, this discussion proposes a series of models for collocating narrative data by examining how one researcher created frameworks that helped ground final conclusions within the broader narrative environment and context. The article concludes by suggesting that the collocation of narrative is essential for establishing validity and for creating holistic and connected research texts.
Local Stereo Matching Based on Support Weight With Motion Flow for Dynamic Scene

Stereo matching is one of the most important and challenging subjects in the field of stereo vision. The disparity obtained in stereo matching can represent depth information in 3-D world to a great extent and shows great importance in stereo field. In general, stereo-matching methods primarily emphasize static image. However, the information provided by dynamic scene can be used fully and effectively to improve the results of stereo matching for dynamic scene, such as video sequences. In this paper, we propose a dynamic scene-based local stereo-matching algorithm which integrates a cost filter with motion flow of dynamic video sequences. In contrast to the existing local approaches, our algorithm puts forward a new computing model which fully considers motion information in dynamic video sequences and adds motion flow to calculate suitable support weight for accurately estimating disparity. Our algorithm can perform as an edge-preserving smoothing operator and shows improved behavior near the moving edges. The experimental results show that the proposed method achieves a better depth map and outperforms other local stereo-matching methods in disparity evaluation.
Phosphoproteome analysis of an early onset mouse model (TgCRND8) of Alzheimer's disease reveals temporal changes in neuronal and glia signaling pathways

Sustained exposure to soluble amyloid β (Aβ42) oligomers is predicted to impair synaptic function in the hippocampal‐entorhinal circuit, signaling synaptic loss and precipitating cognitive impairment in Alzheimer's disease. Regional changes in overall patterns of protein phosphorylation are likely crucial to promote transition from a presymptomatic to a symptomatic state in response to accumulating Aβ42. Here, we used unbiased proteomic approaches to compare the phosphoproteome of presymptomatic and symptomatic TgCRND8 mice and identify network disruptions in signaling pathways implicated in the manifestation of behavioral indices of learning and memory impairment. Phosphopeptide enrichment with triple isotopic dimethylation labeling combined with online multidimensional separation and MS was used to profile phosphoproteome changes in 2‐ and 6‐month‐old TgCRND8 mice and congenic littermate controls. We identified 1026 phosphopeptides representing 1168 phosphorylation sites from 476 unique proteins. Of these, 595 phosphopeptides from 293 unique proteins were reliably quantified and 139 phosphopeptides were found to change significantly in the hippocampus of TgCRND8 mice following conversion from a presymptomatic to a symptomatic state.
Size Distortion of Bootstrap Tests Application to a Unit Root Test

Testing for a unit root in a series obtained by summing a stationary MA(1) process with a parameter close to -1 leads to serious size distortions under the null, on account of the near cancellation of the unit root by the MA component in the driving stationary series. The situation is analysed from the point of view of bootstrap testing, and an exact quantitative account is given of the error in rejection probability of a bootstrap test. A particular method of estimating the MA parameter is recommended, as it leads to very little distortion even when the MA parameter is close to -1. A new bootstrap procedure with still better properties is proposed. While more computationally demanding than the usual bootstrap, it is much less so than the double bootstrap.
High-Risk Infants of Teenage Mothers: Later Candidates for Special Education Placements?

Almost 600,000 infants are bom to teenage mothers each year. These teenage mothers and their infants are at multiple risk to impaired birth and growth—physically, socially, and cognitively. This article reviews some of the more relevant research on the problem of teenage pregnancy, the special educational needs of the infants of these teenage mothers, and the current intervention programs for teenage mothers and infants. Recent research demonstrates that intervention programs for teenage mothers and infants can have an impact on infant mortality, morbidity, and prematurity as well as on the infant's social and cognitive development. In addition, these programs can have an impact on the mother's graduation from high school, employment, repeat pregnancies, and parenting skills. Enhancing parenting skills has been found to affect children's cognitive development. It would seem wise for society to invest more money and effort into preventing untoward pregnancies, preventing birth deficits, and enhancing and supporting parenting skills. Preventable special education placements could be avoided.
Genital identity: A core component of sexual‐ and self‐identity

The self is a social product and consists of self-definitions (Vernon, 1972) and attitudes which are products of interactions with many individuals in many different social settings. Symbolic interaction (SI) theory holds that the physical world as well as the social world takes on meaning through the process of symbolic interaction. Both worlds have to be interpreted and are given meaning through the use and manipulation of symbols that are brought to and are inherent in the various social milieus. In the same manner, humans come to give meaning to the groups with which they identify, to the individuals with whom they interact, to the very interact itself, and to themselves (Plummer, 1982). At the same time, it is generally accepted that it is not the actual responses of others but the interpretation of those responses that is the more important in the formation of the self-concept (Quarantelli & Cooper, 1978). Because of its focus on the symbolic nature of human interaction, SI is especially suited for an examination and analysis of the self as a social product. It is through such an examination that one becomes aware that in order to comprehend the self more fully, we must further refine the concept of self.
NIH 'Research Risks Office' reprimands hospital institutional review board.

IN WHAT is believed to be a unique action, the Office for Protection from Research Risks at the National Institutes of Health (NIH) has reprimanded an institutional review board for failing to ensure that all subjects in a clinical trial were informed. There have been instances when investigators have failed to protect research subjects properly, but none involving institutional review boards, according to Charles R. McCarthy, PhD, director of the research risks office in Bethesda, Md. McCarthy and the deputy director of the office, Alan L. Sandler, DDS, jointly sent a letter to Peter H. Wolff, MD, chair of the committee on clinical investigation at Children's Hospital, Boston, Mass, pointing out that prerandomization to a study is unacceptable when consent is being sought in only one arm of that study. Prerandomization is a procedure in which patients are randomized to receive the treatments that are being tested before being asked
Accelerated Fibrinolysis and Its Propagation on Vascular Endothelial Cells by Secreted and Retained tPA

We successfully visualized the secretory dynamics of tissue-type plasminogen activator (tPA) tagged by green fluorescent protein (tPA-GFP) from cultured vascular endothelial cells (VECs) using total internal reflection fluorescence (TIRF) microscopy and demonstrated that tPA-GFP secreted from VECs was retained on cell surfaces in a heavy-chain-dependent manner. Progressive binding of Alexa568-labeled Glu-plasminogen was also observed on the surface of active tPA-GFP expressing cells via lysine binding sites (LBS), which was not observed on inactive mutant tPA-GFP expressing cells. These results suggest that retained tPA on VECs effectively activated plasminogen to plasmin, which then facilitated the binding of additional plasminogen on the cell surface by proteolytically cleaving surface-associated proteins and exposing their C-terminal lysine residues. Thus prolonged retention of tPA appeared to play an important role in initiating and amplifying plasmin generation on VECs. LBS-dependent binding of plasminogen was also observed as a narrow band at the lytic front of the fibrin mesh formed on active tPA-GFP expressing cells, which expanded outward as the lytic area increased. This binding was not observed on inactive mutant tPA-GFP expressing cells or in the presence of aprotinin. The binding of plasminogen to partially digested fibrin appears to be indispensable for spontaneous fibrinolysis.
Towards Scalable Spectral Clustering via Spectrum-Preserving Sparsification

The eigendeomposition of nearest-neighbor (NN) graph Laplacian matrices is the main computational bottleneck in spectral clustering. In this work, we introduce a highly-scalable, spectrum-preserving graph sparsification algorithm that enables to build ultra-sparse NN (u-NN) graphs with guaranteed preservation of the original graph spectrums, such as the first few eigenvectors of the original graph Laplacian. Our approach can immediately lead to scalable spectral clustering of large data networks without sacrificing solution quality. The proposed method starts from constructing low-stretch spanning trees (LSSTs) from the original graphs, which is followed by iteratively recovering small portions of "spectrally critical" off-tree edges to the LSSTs by leveraging a spectral off-tree embedding scheme. To determine the suitable amount of off-tree edges to be recovered to the LSSTs, an eigenvalue stability checking scheme is proposed, which enables to robustly preserve the first few Laplacian eigenvectors within the sparsified graph. Additionally, an incremental graph densification scheme is proposed for identifying extra edges that have been missing in the original NN graphs but can still play important roles in spectral clustering tasks. Our experimental results for a variety of well-known data sets show that the proposed method can dramatically reduce the complexity of NN graphs, leading to significant speedups in spectral clustering.
Ellipsis Resolution by Controlled Default Unification for Multi-modal and Speech Dialog Systems

We present a default-unification-based approach to ellipsis resolution that is based on experience in long running multimodal dialog projects, where it played an essential role in discourse processing. We extend default unification to non-parallel structures, which is important for speech and multimodal dialog systems. We introduce new control mechanisms for ellipsis resolution by considering dialog structure with respect to specification, variation and results of tasks and combine this with the analysis of relations between the information elements contained in antecedent and elliptic structures.