bluuebunny/crossref_metadata_2025_split huggingface dataset:
A more special use of the third person singular pronoun in Shanghainese
Authors: Zhu Jialei 
Published: March 2023

Abstract
This paper investigates the syntax and semantics of the third person singular pronoun ɦi⁶ in
Shanghainese in its non-referential use. Evidence from its phonological dependence and syntactic integration with the preceding
host verb is presented to show that the non-referential ɦi⁶ is a clitic. It is further observed that this use of
ɦi⁶ requires licensing from the [+subjunctive] feature, echoing Qian
(2004) and Jin (2016). However, my analysis diverges from theirs in
treating the ɦi⁶ in question as an expletive licensed by the [+subjunctive] feature, rather than a subjunctive
marker itself on grounds that it is not obligatory in a subjunctive sentence and that the subjunctivity must always be encoded by
other elements in the sentence. ɦi⁶ in this use serves as an emphatic, strengthening the effect of deontic or
imperative force in subjunctive contexts. Furthermore, it is shown that the licensing must obey a locality condition, whereby the
expletive ɦi⁶ must be licensed by a c-commanding licenser in its local domain. From the perspective of
grammaticalization, I propose that ɦi⁶ is going through an intermediate stage of becoming a purely modal
functional marker. A final stage of this “upward” grammaticalization can be found in the expletive use of the third person pronoun
keoi in Cantonese, which has become a more grammaticalized marker that has scope over the sentence. It is
pointed out that the development of the expletive pronoun suggests that being at the right-periphery is a crucial factor in
motivating and facilitating grammaticalization in Chinese. Last, a comparison between the non-referential ɦi⁶ and
the Mandarin non-referential third person pronoun ta in the V-ta-ge-NP/AP construction is
presented, showing that both cases manifest interactions between VP-internal projections and higher functional categories.
Syntactic functions of nonmanuals in Russian Sign Language
Authors: Burkova Svetlana , Khristoforova Evgeniia , Kimmelman Vadim 
Published: March 2023

Abstract
This chapter presents the Russian Sign Language (RSL) Corpus and demonstrates its capabilities as a research tool by summarizing three corpus-based studies primarily focused on syntactic functions of nonmanual markers. The first study considers question marking in regular wh-questions and in question-answer pairs. It shows that the two constructions have very different nonmanual markers. The second study analyzes marking of topics in RSL, and shows that nonmanual markers of topics are typologically common, but are infrequent in naturalistic corpus data. The third study investigates conditional and concessive constructions in RSL. It demonstrates that these constructions make extensive and frequent use of nonmanual markers, but that no single marker is specialized for the function of expressing conditional or concessive meaning. Instead, complex combinations of multiple markers are employed in these constructions. All three studies also contribute to sign language typology by providing novel descriptions of syntactic and discourse phenomena in RSL.
Verb classes in South African Sign Language
Authors: Wehrmeyer Ella 
Published: March 2023

Abstract
This chapter presents a pioneer exploration of verb categories in South African Sign Language (SASL), using a corpus of authentic naturalistic narrative data. The paucity of linguistic descriptions of SASL meant that a theoretical basis for the study was derived using models developed for other sign languages, in particular those of Padden (1986, 1988, 1990), Johnston (1991), and De Beuzeville et al. (2009). The study also sought to explore whether verb classes can be distinguished in terms of gaze, head turns, body turns, location shift, nature of movement, emotive facial expressions, and explicitation of arguments, and how these parameters change during role-shift. The study finds that De Beuzeville et al.’s (2009) categorization provides the closest description for SASL verbs, but with a split in their ‘directional verbs’ category into agreement and motion verbs. A typology for SASL verbs is proposed and reflected upon in terms of teaching objectives.
Exotic marginally outer trapped surfaces in rotating spacetimes of any dimension
Authors: Booth Ivan , Chan Kam To Billy , Hennigar Robie A , Kunduri Hari , Muth Sarah 
Published: March 2023

Abstract
The recently developed MOTSodesic method for locating marginally outer trapped surfaces (MOTSs) was effectively restricted to non-rotating spacetimes. In this paper we extend the method to include (multi-)axisymmetric time slices of (multi-)axisymmetric spacetimes of any dimension. We then apply this method to study MOTSs in the BTZ, Kerr and Myers–Perry black holes. While there are many similarities between the MOTSs observed in these spacetimes and those seen in Schwarzschild and Reissner-Nordström, details of the more complicated geometries also introduce some new, previously unseen, behaviours.
Uniformly convex neural networks and non-stationary iterated network Tikhonov (iNETT) method
Authors: Bianchi Davide , Lai Guanghao , Li Wenbin 
Published: March 2023

Abstract
We propose a non-stationary iterated network Tikhonov (iNETT) method for the solution of ill-posed inverse problems. The iNETT employs deep neural networks to build a data-driven regularizer, and it avoids the difficult task of estimating the optimal regularization parameter. To achieve the theoretical convergence of iNETT, we introduce uniformly convex neural networks to build the data-driven regularizer. Rigorous theories and detailed algorithms are proposed for the construction of convex and uniformly convex neural networks. In particular, given a general neural network architecture, we prescribe sufficient conditions to achieve a trained neural network which is component-wise convex or uniformly convex; moreover, we provide concrete examples of realizing convexity and uniform convexity in the modern U-net architecture. With the tools of convex and uniformly convex neural networks, the iNETT algorithm is developed and a rigorous convergence analysis is provided. Lastly, we show applications of the iNETT algorithm in 2D computerized tomography, where numerical examples illustrate the efficacy of the proposed algorithm.
Collective excitations in a melt of fast phase change material GeCu2Te3
Authors: Inui M , Hosokawa S , Tsutsui S , Nakajima Y , Matsuda K , Maruyama K , Baron A Q R 
Published: March 2023

Abstract
Inelastic x-ray scattering measurements have been carried out to investigate atomic dynamics in a melt of fast phase change material GeCu2Te3. The dynamic structure factor was analysed using the model function with three damped harmonic oscillator components. By investigating the correlation between the excitation energy and the linewidth, and that between the excitation energy and the intensity on contour maps of a relative approximate probability distribution function proportional to

exp
(
−

χ
2

/
N
)

, we could judge the reliability of each inelastic excitation in the dynamic structure factor. The results indicate that there are two inelastic excitation modes besides the longitudinal acoustic one in the liquid. The lower energy excitation could be assigned to the transverse acoustic one whereas the higher energy one disperses like fast sound. The latter result may imply that the liquid ternary alloy exhibits a microscopic phase separation tendency.
Uncertainty-aware temperature interpolation for measurement rooms using ordinary Kriging
Authors: Vedurmudi Anupam Prasad , Janzen Katharina , Nagler Markus , Eichstaedt Sascha 
Published: March 2023

Abstract
Accurate measurements in dimensional metrology necessitate strict controls on spatial and temporal variations in the measurement room temperature. Due to limitations in the number of sensors that can be placed in a given room, interpolation methods that leverage information from multiple sensors are necessary to assess conditions at unsampled locations. In this contribution, Kriging is used to spatially interpolate room temperatures from a limited number of sensors with different measurement uncertainties in a temperature controlled room housing two coordinate measurement machines. A novel method to propagate sensor uncertainties to the interpolated values using a Monte Carlo simulation is also demonstrated. The uncertainty propagation is considered for the explicitly heteroskedastic, i.e. a constituent network of sensors with different measurement uncertainties. The influence of a localized disturbance in the form of a movable heating element in the room is also investigated.
Multiscale cascade recurrent dilation convolution network for fault diagnosis of rolling bearing under cross-load conditions
Authors: Xu Zhenli , Tang Guiji , Pang Bin 
Published: March 2023

Abstract
Recently, deep learning (DL) models based on convolutional neural networks have achieved satisfactory results in rolling bearing fault diagnosis. However, the bearings usually work in variable loading conditions, and their feature distribution could vary with load. The important features cannot be effectively captured in the convolution process using the existing diagnosis models, resulting in poor generalization performance. In this paper, a novel DL model, named multiscale cascade recurrent dilation convolution network, is proposed by introducing the dilated convolution and global average pooling (GAP) layer. Firstly, a new multiscale cascade structure with different convolution kernel sizes is introduced to extract multiscale features contained in the vibration signal. Secondly, a recurrent dilation convolution strategy is designed in each branch of the multiscale cascade structure to extract abundant feature information. Finally, the GAP is employed to reduce redundant feature vectors and output them, while a classifier of multilayer perceptron is used to automatically identify the fault types. The effectiveness of the proposed algorithm is evaluated by two experimental cases. The results show that the proposed method can successfully identify the labels of fault samples under unknown load conditions using the fault samples with labels under existing load conditions. Compared with other methods, this method exhibits excellent robustness and generalization performance for bearing fault diagnosis under cross-load conditions.
Enhanced resistive switching performance of TiO2 based RRAM device with graphene oxide inserting layer
Authors: Hu Lifang , Zheng Zhi , Xiao Ming , Meng Qingsen 
Published: March 2023

Abstract
In this work, graphene oxide (GO)/TiO2 heterostructures for resistive random access memory devices were fabricated, and the composition and microstructure of TiO2 and GO were characterized by x-ray diffraction, Raman spectroscopy, scanning electronic microscopy, and transmission electron microscopy. The resistive characteristics of the fabricated devices were investigated, and the remarkable improvement in cycle-to-cycle uniformity and high ON/OFF ratio of the TiO2 thin film-based memory device were realized by introducing a thin GO layer. The formation/rupture of the conductive filament through the migration of oxygen vacancies in the TiO2 substrate was responsible for the resistive switching. Owing to the different activation energies of reduction and oxidation of the GO, the set voltage became larger than the reset voltage. According to the linear fitting of double logarithm I–V plots, the conduction mechanism in low and high resistance states was governed by the ohmic mechanism and trap-controlled space charge limited current, respectively. The oxygen migration-induced oxidation/reduction in GO rendered it a good oxygen vacancy reservoir, which is responsible for the enhanced cycle-to-cycle uniformity and high ON/OFF ratio.
Crystallographic structural variations in nano-crystalline Sc2O3 under pressure
Authors: Yadav Deepa , Bura Neha , Bhoriya Ankit , Singh Jasveer , Srihari Velaga , Poswal Himanshu K , Sharma Nita Dilawar 
Published: March 2023

Abstract
The present manuscript reports high-pressure investigations on nano-crystalline Scandium Oxide (Sc2O3) through synchrotron angle dispersive x-ray diffraction technique (ADXRD) up to a pressure of ∼35.4 GPa using a diamond-anvil cell (DAC). The sample, having a cubic bixbyite structure at ambient conditions, underwent substantial changes in crystallographic parameters under the application of external pressure, while exhibiting phase stability i.e. does not undergo any structural phase transition up to 35.4 GPa. The lattice parameters and the unit cell volume decrease significantly with an increase in pressure, revealing the compressible nature of the crystal structure. The pressure-volume data is fitted using the third-order Birch-Murnaghan equation of state to calculate the bulk modulus and its first derivative, which yields zero pressure bulk modulus of 217(35) GPa, with

B

0

′

= 12(4) and the corresponding ambient volume per unit molecule comes out to be 59.18(18) Å3.
Simple single-field inflation models with arbitrarily small tensor/scalar ratio
Authors: Stein Nina K. , Kinney William H. 
Published: March 2023

Abstract
We construct a family of simple single-field inflation models consistent with Planck / BICEP Keck bounds which have a parametrically small tensor amplitude and no running of the scalar spectral index. The construction consists of a constant-roll hilltop inflaton potential with the end of inflation left as a free parameter induced by higher-order operators which become dominant late in inflation. This construction directly demonstrates that there is no lower bound on the tensor/scalar ratio for simple single-field inflation models.
Microsecond pulsed yellow emission by intracavity doubled optically pumped two-chip VECSEL
Authors: Liu Hua-Yu , Ma Hao-Da , Bian Qi , Bo Yong , Cui Da-Fu , Peng Qin-Jun 
Published: March 2023

Abstract
A quasi-continues-wave (QCW) 100 μs pulsed yellow laser at 589 nm, based on intracavity frequency doubling (ICFD) of identical two-chip vertical-external-cavity surface-emitting laser (VECSEL) with bow-tie configuration, is demonstrated. Each VECSEL chip is optically QCW-pumped by an 808 nm laser diode, emitting a fundamental wavelength around 1178 nm. A birefringence filter is inserted in the laser cavity to select the linear p-polarized oscillation and tune the operation wavelength. Using a lithium-triborate (LiB3O5) as the frequency doubled crystal, a total frequency doubled output power of 264 mW at 589 nm is achieved, operating at a pulse duration of 194 μs and a pulse repetition rate of 1 kHz. A good beam quality factor is measured to be M
2 = 2.15. These results represent a novel scheme for the QCW multiple gain chips VECSEL ICFD system, opening up a new perspective for the realization of impact long pulse 589 nm laser for sodium guide-star.
Intensive land-use is associated with development status in port cities of Southeast Asia
Authors: Dong Yuqi , Zhou Yu , Zhang Li , Gu Yu , Sutrisno Dewayany 
Published: March 2023

Abstract
Port cities in Southeast Asia (SEA) have been undergoing intensive land use and land cover change (LUCC) because of rapid socio-economic development in recent decades. However, various development statuses among these cities could result in divergent LUCC transitions and therefore affect the local ecosystem at different levels. This study investigated the historic land-use transition in the SEA port cities by classifying Landsat images from 1990 to 2020 with the random forest algorithm, and further integrated these patterns with land-use modeling to inform the future land uses under the current developing mode. DIVISON was used to assess land-use fragmentation and investigated its relationship with development levels represented by the average nighttime light index (ANLI). The results showed an intensive LUCC in SEA port cities in 1990–2020, with artificial surface increasing by 9.2% (175.9 km2) of the total area, mainly converted from cultivated land which decreased by 136.9 km2. An inverted V-shaped relationship between fragmentation and development level was found at the landscape level and for cultivated land, in which both turning points (TPs) occurred in the mid-developed stage (ANLI = 41.1 and 20.0, respectively). Artificial surfaces tended to be more aggregated in later developed stages, showing a TP of ANLI around 53.2. Under the current developing mode, the aggregation trend will be generally continued for the next three decades, mainly resulting from the increased connection in artificial surfaces. The findings also highlighted a tradeoff between city development and agricultural production in SEA port cities, leading to increased food insecurity in more developed stages. Recent developments also negatively affected aqua ecosystems, such as aquaculture, mangroves, and natural waterbodies in several cities. This study underscored the importance of the sustainable LUCC strategy in SEA port cities and provides insights into rationalizing land-use policies for other port cities worldwide.
Exact solution of weighted partially directed walks crossing a square
Authors: Beaton Nicholas R , Owczarek Aleksander L 
Published: March 2023

Abstract
We consider partially directed walks crossing a L × L square weighted according to their length by a fugacity t. The exact solution of this model is computed in three different ways, depending on whether t is less than, equal to or greater than 1. In all cases a complete expression for the dominant asymptotic behaviour of the partition function is calculated. The model admits a dilute to dense phase transition, where for

0
<
t
<
1

the partition function scales exponentially in L whereas for t > 1 the partition function scales exponentially in L
2, and when t = 1 there is an intermediate scaling which is exponential in

L
log
L

. As such we provide an exact solution of a model of the dilute to dense polymeric phase transition in two dimensions.
Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review
Authors: Cui Can , Yang Haichun , Wang Yaohong , Zhao Shilin , Asad Zuhayr , Coburn Lori A , Wilson Keith T , Landman Bennett A , Huo Yuankai 
Published: March 2023

Abstract
The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions.
Identification of significant gene expression changes in multiple perturbation experiments using knockoffs
Authors: Zhao Tingting , Zhu Guangyu , Dubey Harsh Vardhan , Flaherty Patrick 
Published: March 2023

AbstractLarge-scale multiple perturbation experiments have the potential to reveal a more detailed understanding of the molecular pathways that respond to genetic and environmental changes. A key question in these studies is which gene expression changes are important for the response to the perturbation. This problem is challenging because (i) the functional form of the nonlinear relationship between gene expression and the perturbation is unknown and (ii) identification of the most important genes is a high-dimensional variable selection problem. To deal with these challenges, we present here a method based on the model-X knockoffs framework and Deep Neural Networks to identify significant gene expression changes in multiple perturbation experiments. This approach makes no assumptions on the functional form of the dependence between the responses and the perturbations and it enjoys finite sample false discovery rate control for the selected set of important gene expression responses. We apply this approach to the Library of Integrated Network-Based Cellular Signature data sets which is a National Institutes of Health Common Fund program that catalogs how human cells globally respond to chemical, genetic and disease perturbations. We identified important genes whose expression is directly modulated in response to perturbation with anthracycline, vorinostat, trichostatin-a, geldanamycin and sirolimus. We compare the set of important genes that respond to these small molecules to identify co-responsive pathways. Identification of which genes respond to specific perturbation stressors can provide better understanding of the underlying mechanisms of disease and advance the identification of new drug targets.
Historical Co-offending Networks: A Social Network Analysis Approach
Authors: Di Méo Grace 
Published: March 2023

AbstractRecent decades have witnessed growing use of social network analysis (SNA) to study criminal activities, including that of co-offending. However, few studies have examined co-offending networks within a historical context. This paper focuses on group-based crime in a large English town during the Victorian period, employing SNA methods to examine the prevalence, structure and composition of co-offending relationships. Networks for property, violent and victimless crimes were partitioned to compare co-offending across crime categories. Results indicate that co-offending groups were typically segregated, although there was a loosely-organised community of property crime offenders connected by ‘brokers’ who collaborated with multiple groups. Evidence also suggests that co-offending was largely characterised by assortative mixing in regard to sex, age and marital status.
Disassociation between fluctuation shift over frequency and glucose metabolism in motor cortex of Parkinson’s disease: a hybrid PET/fMRI study
Authors: Zang Zhenxiang , Song Tianbin , Li Jiping , Qu Hongying , Nie Binbin , Mei Shanshan , Wu Tao , Zhang Yuqing , Lu Jie 
Published: March 2023

Abstract
Increased glucose metabolism and decreased low-frequency fluctuation have been consistently reported in the motor area of Parkinson’s disease (PD). The reason for such seeming paradox is unclear. Here, we enrolled 34 PD patients and 25 healthy controls (HCs) for hybrid PET/fMRI scan (PET/fMRI(discovery) dataset). In addition, 2 replication datasets, namely fMRI(validation-1) and fMRI(validation-2) dataset, were also included. We computed ratio of standard uptake value (SUVr) to measure FDG-uptake. The amplitude of low-frequency fluctuations (ALFF) for the following 4 frequency bands was calculated: slow-5, slow-4, slow-3, and slow-2. We obtained a significant group-by-frequency interaction effect of ALFF in the paracentral lobule/supplementary motor area (PFWE = 0.003) and the right sensorimotor area (PFWE &lt; 0.001) in the PET/fMRI(discovery) dataset, which could be replicated using fMRI(validation-1) and fMRI(validation-2) datasets (PFWE &lt; 0.05). In detail, HCs exhibited power law-like fluctuation pattern, but PD patients did not. Correlation analyses further revealed significant associations between ALFF and FDG-uptake in HCs (P-values &lt; 0.031), but not in PD (P-values &gt; 0.28). Taken together, this study identified a fluctuation shift over frequency effect in PD patients, which further disassociated with glucose metabolism in the motor cortex.