Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional difference between the two variants of rising-falling intonation in spontaneous Japanese monologue

Penultimate non-lexical prominence, or PNLP, is a variant of rising-falling boundary pitch movement (L%HL%) in Tokyo Japanese. Analysis of Corpus of Spontaneous Japanese revealed hitherto unknown function of the PNLP. It occurred basically only once near the end of an utterance flanked by deep clause boundaries. This finding suggests strongly t he interpretation that native speakers of Japanese use PNLP to predict the end of a long utterance. Pilot analysis was also conducted to examine the factors governing the occurrence of PNLP.
Restoration of Prairie Hydrology at the Watershed Scale: Two Decades of Progress at Neal Smith National Wildlife Refuge, Iowa

Tallgrass prairie once occupied 67.6 million hectares in the North American Midwest but less than 0.1% remains today. Consisting of more than 2200 ha, Neal Smith National Wildlife Refuge (NSNWR) was established by the US Fish and Wildlife Service in the 5217 ha Walnut Creek watershed in Jasper County, Iowa. Large tracts of land are being converted from row crop agriculture to native prairie and savanna with the goal to restore the landscape to a semblance of the condition that existed prior to Euro-American settlement. Understanding hydrologic processes at the watershed scale has been a focus of research at NSNWR for nearly two decades and the purpose of this paper is to integrate research results from monitoring projects to assess the progress made towards restoring five key hydrologic components: the water balance, stream network, streamflow hydrograph, groundwater levels and water quality. Restoration of hydrology is severely challenged by the history of hydrologic changes that occurred in the basin during a century of intensive agricultural activity. We document measurable progress in restoring key hydrologic processes in some areas, particularly in upland catchments compared to the larger watershed scale and discuss the timeframe needed to observe changes at short- and long-term scales.
Lagrangian relaxation-based multi-threaded discrete gate sizer

vii CHAPTER 1. OVERVIEW . 1 1.1 Previous Work . 2 1.1.1 ISPD Gate Sizing Contests . 3 1.1.2 Major Drawbacks With Existing LR Gate Sizers . 4 1.2 Contributions . 5 1.3 Thesis Outline . 6 CHAPTER 2. FAST LAGRANGIAN RELAXATION BASED GATE SIZING USING MULTITHREADING . 7 2.
An Articulatory Silicon Vocal Tract for Speech and Hearing Prostheses

We describe the concept of a bioinspired feedback loop that combines a cochlear processor with an integrated-circuit vocal tract to create what we call a speech-locked loop. We discuss how the speech-locked loop can be applied in hearing prostheses, such as cochlear implants, to help improve speech recognition in noise. We also investigate speech-coding strategies for brain-machine-interface-based speech prostheses and present an articulatory speech-synthesis system by using an integrated-circuit vocal tract that models the human vocal tract. Our articulatory silicon vocal tract makes the transmission of low bit-rate speech-coding parameters feasible over a bandwidth-constrained body sensor network. To the best of our knowledge, this is the first articulatory speech-prosthesis system reported to date. We also present a speech-prosthesis simulator as a means to generate realistic articulatory parameter sequences.
Arctic mass, freshwater and heat fluxes: methods and modelled seasonal variability

Considering the Arctic Ocean (including sea ice) as a defined volume, we develop equations describing the time-varying fluxes of mass, heat and freshwater (FW) into, and storage of those quantities within, that volume. The seasonal cycles of fluxes and storage of mass, heat and FW are quantified and illustrated using output from a numerical model. The meanings of ‘reference values’ and FW fluxes are discussed, and the potential for error through the use of arbitrary reference values is examined.
A Spectral-change-aware Loss Function for DNN-based Speech Separation

Speech separation can be treated as a mask estimation problem where supervised learning is employed to construct the mapping from acoustic features to a mask. Interference can be reduced by applying the estimated mask on a time-frequency (T-F) representation of noisy speech, resulting in improved speech intelligibility. Most of existing learning networks for speech separation aim to minimize the Mean Square Error (MSE) over the training set, where the loss from each T-F representation is equally weighted. In this paper, we proposed a spectral-change-aware loss function, where loss from the T-F units with large spectral changes over time were assigned higher weights compared to the T-F units with minor spectral changes. Such spectral-change-aware loss function was evaluated on speech separation performance in terms of mask estimation accuracy, short-time objective intelligibility (STOI) and SNR gain of unvoiced segments. The results indicated that the proposed loss function could further improve the speech intelligibility and increase SNR gain of unvoiced segments even in the cost of increased error rate of estimated mask.
Corticospinal excitability and sleep: a motor threshold assessment by transcranial magnetic stimulation after awakenings from REM and NREM sleep

Transcranial magnetic stimulation (TMS) is a recently established technique in the neurosciences that allows the non‐invasive assessment, among other parameters, of the excitability of motor cortex. Up to now, its application to sleep research has been very scarce and because of technical problems it provided contrasting results. In fact delivering one single suprathreshold magnetic stimulus easily awakes subjects, or lightens their sleep. For this reason, in the present study we assessed motor thresholds (MTs) upon rapid eye movement (REM) and non‐rapid eye movement (NREM) sleep awakenings, both in the first and in the last part of the night. Taking into account that a full re‐establishment of wake regional brain activity patterns upon awakening from sleep needs up to 20–30 min, it is possible to make inferences about the neurophysiological characteristics of the different sleep stages by analyzing the variables of interest immediately after provoked awakenings. Ten female volunteers slept in the lab for four consecutive nights. During the first night the MTs were collected, following a standardized procedure: 5 min before lights off, upon stage 2 awakening (second NREM period), upon REM sleep awakening (second REM period), upon the final morning awakening (always from stage 2). Results showed that MTs increased linearly from presleep wakefulness to REM sleep awakenings, and from the latter to stage 2 awakenings. There was also a time‐of‐night effect on MTs upon awakening from stage 2, indicating that MTs decreased from the first to the second part of the night. The increase in corticospinal excitability across the night, which parallels the fulfillment of sleep need, is consistent with the linear decrease of auditory arousal thresholds during the night. The maximal reduction of corticospinal excitability during early NREM sleep can be related to the hyperpolarization of thalamocortical neurons, and is in line with the decreased metabolic activity of motor cortices during this sleep stage. On the contrary, the increase of MTs upon REM sleep awakenings should reflect peripheral factors. We conclude that our findings legitimate the introduction of the TMS technique as a new proper tool in sleep research.
Semantic segmentation of artery-venous retinal vessel using simple convolutional neural network

Semantic segmentation is how to categorize objects in an image based on pixel color intensity. There is an implementation in the medical imaging. This article discusses semantic segmentation in retinal blood vessels. Retinal blood vessels consist of artery and vein. Arteryvenous segmentation is needed to detect diabetic retinopathy, hypertension, and artherosclerosis. The data for the experiment is Retinal Image vessel Tree Extraction (RITE). Data consists of 20 patches with a dimension of 128 × 128 × 3. The process for performing semantic segmentation consists of 3 method, create a Conventional Neural Network (CNN) model, pre-trained network, and training the network. The CNN model consists of 10 layers, 1 input layer image, 3 convolution layers, 2 Rectified Linear Units (ReLU), 1 Max pooling, 1 transposed convolution layer, 1 softmax and 1 pixel classification layer. The pre-trained network uses the optimization algorithm Stochastic Gradient Descent with Momentum (SGDM), Root Mean Square Propagation (RMSProp) and Adaptive Moment optimization (Adam). Various scenarios were tested to get optimal accuracy. The learning rate is 1e-3 and 1e-2. Minibatch size are 4,8,16,32,64, and 128. The maximum value of epoch is set to 100. The results show the highest accuracy of up to 98.35%
Hearing Impairment Is Associated with Smaller Brain Volume in Aging

Although recent studies show that age-related hearing impairment is associated with cerebral changes, data from a population perspective are still lacking. Therefore, we studied the relation between hearing impairment and brain volume in a large elderly cohort. From the population-based Rotterdam Study, 2,908 participants (mean age 65 years, 56% female) underwent a pure-tone audiogram to quantify hearing impairment. By performing MR imaging of the brain we quantified global and regional brain tissue volumes (total brain volume, gray matter volume, white matter (WM) volume, and lobe-specific volumes). We used multiple linear regression models, adjusting for age, sex, head size, time between hearing test and MR imaging, and relevant cognitive and cardiovascular covariates. Furthermore, we performed voxel-based morphometry to explore sub-regional differences. We found that a higher pure-tone threshold was associated with a smaller total brain volume [difference in standardized brain volume per decibel increase in hearing threshold in the age-sex adjusted model: -0.003 (95% confidence interval -0.004; -0.001)]. Specifically, WM volume was associated. Both associations were more pronounced in the lower frequencies. All associations were consistently present in all brain lobes in the lower frequencies and in most lobes in the higher frequencies, and were independent of cognitive function and cardiovascular risk factors. In voxel-based analyses we found associations of hearing impairment with smaller white volumes and some smaller and larger gray volumes, yet these were statistically non-significant. Our findings demonstrate that hearing impairment in elderly is related to smaller total brain volume, independent of cognition and cardiovascular risk factors. This mainly seems to be driven by smaller WM volume, throughout the brain.
A New Separation Method for Galaxy Spectra Based on Data Fusion between Two Grism Orders in Slitless Spectroscopy

We consider the problem of decontaminating galaxy spectra in the context of the EUCLID space mission. The spectra of neighboring astronomical objects being spatially mixed, a source separation method should be used to separate them. Here, we propose a new method based on the fusion of information between first and second-order spectra generated by a grism. Using the optical properties, we propose a regularized criterion and a gradient algorithm to optimize it. The tests using noisy realistic simulated data show that our method leads to better results than a method only based on second-order information.
Empirical equations for some soil hydraulic properties

The soil moisture characteristic may be modeled as a power curve combined with a short parabolic section near saturation to represent gradual air entry. This two-part function—together with a power function relating soil moisture and hydraulic conductivity—is used to derive a formula for the wetting front suction required by the Green-Ampt equation. Representative parameters for the moisture characteristic, the wetting front suction, and the sorptivity, a parameter in the infiltration equation derived by Philip (1957), are computed by using the desorption data of Holtan et al. (1968). Average values of the parameters, and associated standard deviations, are calculated for 11 soil textural classes. The results of this study indicate that the exponent of the moisture characteristic power curve can be predicted reasonably well from soil texture and that gradual air entry may have a considerable effect on a soil's wetting front suction.
A Machine Learning Method for Identification of Key Body Poses in Cyclic Physical Exercises

Motion segmentation plays an important role in human motion analysis. Understanding the intrinsic features of human activities represents a challenge for modern science. Current solutions usually involve computationally demanding processing and achieve the best results using expensive, intrusive motion capture devices. In this paper, a simple, affordable and effective method for human motion segmentation and alignment is presented. The approach follows a two-step process: first, the most salient principal components are calculated in order to reduce the dimension of the input motion data. Then, candidates of key body poses (landmarks) are inferred using multi-class, supervised machine learning techniques from a set of training samples. Finally, cluster analysis is used to refine the result. Predictions are guaranteed to be invariant to the repetitiveness and symmetry of the performance. Results show the effectiveness of the proposed approach by comparing it against the Dynamic Time Warping algorithm and Hierarchical Aligned Cluster Analysis.
Regulation of CTP Synthase Filament Formation During DNA Endoreplication in Drosophila

CTP synthase (CTPsyn) plays an essential role in DNA, RNA, and lipid synthesis. Recent studies in bacteria, yeast, and Drosophila all reveal a polymeric CTPsyn structure, which dynamically regulates its enzymatic activity. However, the molecular mechanism underlying the formation of CTPsyn polymers is not completely understood. In this study, we found that reversible ubiquitination regulates the dynamic assembly of the filamentous structures of Drosophila CTPsyn. We further determined that the proto-oncogene Cbl, an E3 ubiquitin ligase, controls CTPsyn filament formation in endocycles. While the E3 ligase activity of Cbl is required for CTPsyn filament formation, Cbl does not affect the protein levels of CTPsyn. It remains unclear whether the regulation of CTPsyn filaments by Cbl is through direct ubiquitination of CTPsyn. In the absence of Cbl or with knockdown of CTPsyn, the progression of the endocycle-associated S phase was impaired. Furthermore, overexpression of wild-type, but not enzymatically inactive CTPsyn, rescued the endocycle defect in Cbl mutant cells. Together, these results suggest that Cbl influences the nucleotide pool balance and controls CTPsyn filament formation in endocycles. This study links Cbl-mediated ubiquitination to the polymerization of a metabolic enzyme and reveals a role for Cbl in endocycles during Drosophila development.
A Novel Probabilistic Passive Attack on the Protocols HB and HB+

We present a very simple probabilistic, passive attack against the protocols HB and HB+. Our attack presents some interesting features: it requires less captured transcripts of protocol executions when compared to previous results; It makes possible to trade the amount of required transcripts for computational complexity; the value of noise used in the protocols HB and HB+ need not be known.
Recognition Conﬁdence Scoring for Use in Speech Understanding Systems (cid:1)

In this paper we present an approach to recognition conﬁdence scoring and a method for integrating conﬁdence scores into the understanding and dialogue components of a speech understanding system. The system uses a multi-tiered approach where con-ﬁdence scores are computed at the phonetic, word, and utterance levels. The scores are produced by extracting conﬁdence features from the computation of the recognition hypotheses and processing these features using an accept/reject classiﬁer for word and utter-ance hypotheses. The output of the conﬁdence classiﬁers can then be incorporated into the parsing mechanism of the language understanding component. To evaluate the system, experiments were conducted using the JUPITER weather information system. Evaluation was performed at the understanding level using key-value pair concept error rate as the evaluation metric. When conﬁdence scores were integrated into the understanding component of the system, the concept error rate was reduced by over 35%.
Hierarchical layer-based class extensions in Squeak/Smalltalk

Class extensions are frequently used in programming languages such as Ruby and Smalltalk to add or change methods of a class that is defined in the same application or in a different one. They suffer from modularity issues if globally visible: Other applications using the same classes are then affected by the modifications. This paper presents a hierarchical approach for dynamically scoping class extensions in dynamically-typed, class-based programming languages supporting class nesting. Our mechanism allows programmers to define the scope of class extensions and to reuse class extensions in other programs. Class extensions can be scoped according to a nested class hierarchy or based on whether programmers regard a potentially affected class as a black box or not. Class extensions are organized in layers, where multiple layers targeting the same class can be active at the same time.
Global output feedback stabilization for uncertain nonlinear systems with output dependent incremental rate

The solvability of global robust stabilization problem of nonlinear systems by output feedback has been given under various growth conditions on the vector fields of the systems. One of the most common cases is that the vector field is bounded by a linear growth with constant incremental rate. Recently, the problem was solved for some systems with output dependent incremental rate, without considering the uncertainties. This paper further considers the uncertain systems and gives the robust result.
Task migration for fault-tolerance in mixed-criticality embedded systems

In this paper we are interested in mixed-criticality embedded applications implemented on distributed architectures. Depending on their time-criticality, tasks can be hard or soft real-time and regarding safety-criticality, tasks can be fault-tolerant to transient faults, permanent faults, or have no dependability requirements. We use Earliest Deadline First (EDF) scheduling for the hard tasks and the Constant Bandwidth Server (CBS) for the soft tasks. The CBS parameters determine the quality of service (QoS) of soft tasks. Transient faults are tolerated using checkpointing with rollback recovery. For tolerating permanent faults in processors, we use task migration, i.e., restarting the safety-critical tasks on other processors. We propose a Greedy-based online heuristic for the migration of safety-critical tasks, in response to permanent faults, and the adjustment of CBS parameters on the target processors, such that the faults are tolerated, the deadlines for the hard real-time tasks are satisfied and the QoS for soft tasks is maximized. The proposed online adaptive approach has been evaluated using several synthetic benchmarks and a real-life case study.
Lateral capture steps for bipedal walking

Bipedal walkers are difficult to control, inherently unstable systems. Besides the complexity of the walking motion itself, the balance of the robot constantly has to be maintained with good foot placements and other disturbance-rejection strategies. In this work, we are presenting a new, closed-loop control approach that addresses both, the problem of complexity and the challenge of maintaining balance during walking. We decouple walking motion from balance and combine them in a hierarchical framework allowing a foot placement-based balance regulator to control the timing and footstep coordinates of central pattern-generated stepping motions. Furthermore, we decompose the balance controller into three simple, independent modules that compute suitable estimates of timing and sagittal and lateral coordinates for the next footstep to maintain a nominal center of mass trajectory. We implemented the timing and the lateral step size components using the equations of a parameterized version of the linear inverted pendulum model that we fit to data collected from a walking robot. The parameter optimization has a significant impact on the accuracy of our predictions. We demonstrate the efficiency of our approach by performing experiments on a real biped. Results show that the robot is able to reliably recover from any lateral push in only a few steps as long as it does not tip over the current support leg.
Competence of the Deltoid Ligament in Bimalleolar Ankle Fractures After Medial Malleolar Fixation*

Background: The stability of the ankle joint is provided by the medial and lateral malleoli and ligaments. Recent studies of cadaveric ankles have demonstrated that injury to the medial structures of the ankle is necessary to allow lateral subluxation of the talus after fracture. However, cadaveric models are limited by the fracture pattern chosen for the model. We sought to investigate the competency of the deltoid ligament in vivo in patients with an operatively treated bimalleolar ankle fracture. Methods: Twenty-seven patients with a bimalleolar ankle fracture were evaluated. In each patient, the medial malleolus was anatomically reduced and fixed. A radiograph of the ankle was then made with application of an external rotation load to the joint. All lateral malleolar injuries were then reduced and fixed. The radiographs were evaluated for restoration of the competence of the deltoid ligament according to established criteria. Results: Seven (26 percent) of the twenty-seven patients had radiographically evident incompetence of the deltoid ligament after medial malleolar fixation. This finding was associated with a small medial malleolar fragment. Conclusions: In bimalleolar fractures, the medial injury may be an osseous avulsion, leaving the deltoid intact on the displaced fragment, or it may be a combination of ligamentous and osseous injury with disruption of the deep portion of the deltoid ligament.
Depression as a Risk Factor for Coronary Artery Disease: Evidence, Mechanisms, and Treatment

Objective: The present paper reviews the evidence that depression is a risk factor for the development and progression of coronary artery disease (CAD). Methods: MEDLINE searches and reviews of bibliographies were used to identify relevant articles. Articles were clustered by theme: depression as a risk factor, biobehavioral mechanisms, and treatment outcome studies. Results: Depression confers a relative risk between 1.5 and 2.0 for the onset of CAD in healthy individuals, whereas depression in patients with existing CAD confers a relative risk between 1.5 and 2.5 for cardiac morbidity and mortality. A number of plausible biobehavioral mechanisms linking depression and CAD have been identified, including treatment adherence, lifestyle factors, traditional risk factors, alterations in autonomic nervous system (ANS) and hypothalamic pituitary adrenal (HPA) axis functioning, platelet activation, and inflammation. Conclusion: There is substantial evidence for a relationship between depression and adverse clinical outcomes. However, despite the availability of effective therapies for depression, there is a paucity of data to support the efficacy of these interventions to improve clinical outcomes for depressed CAD patients. Randomized clinical trials are needed to further evaluate the value of treating depression in CAD patients to improve survival and reduce morbidity.