Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of 2n prime-sequence codes for optical code division multiple access

The construction of a special family of 2n codes, so-called '2n prime-sequence' codes, for optical code division multiple access (CDMA) has recently been investigated. Since the codes pose the algebraic properties of the prime-sequence and 2n codes, those optical coding architectures, which are especially designed for either of the two codes, can be jointly employed to optimally cope with various constraints (e.g. power budget and cost) imposed by the systems using optical CDMA. As a result, an optical coding architecture, which is particularly attractive for ultrafast optical signal processing and waveguide implementation for the future, high capacity all-optical CDMA networks with potentially 3 dB ideal power ideal, has been proposed. However, the cardinality of the codes is very limited and shown to approach zero when the code weight is sufficiently large. A generalised family of 2n prime-sequence codes, which has the same cardinality as the original prime-sequence codes, is constructed and supported with complete mathematical proofs.
Refined moderation analysis with binary outcomes in precision medicine research

Moderation analysis for evaluating differential treatment effects serves as the bedrock of precision medicine, which is of growing interest in many fields. In the analysis of data with binary outcomes, we observe an interesting symmetry property concerning the ratio of odds ratios, which suggests that heterogeneous treatment effects could be equivalently estimated via a role exchange between the outcome and treatment variable in logistic regression models. We then obtain refined inference on moderating effects by rearranging data and combining two models into one via a generalized estimating equation approach. The improved efficiency is helpful in addressing the lack-of-power problem that is common in the search for important moderators. We investigate the proposed method by simulation and provide an illustration with data from a randomized trial on wart treatment.
Taphonomy of the teleost Tselfatia formosa Arambourg, 1943 from Vallecillo, NE Mexico

The platy limestone deposit of Vallecillo in northeastern Mexico is dated to the early-middle Turonian (Late Cretaceous) and known to contain a variety of well-preserved vertebrate fossils. One of the most common fish species is the teleost Tselfatia formosa. A review of 149 individuals reveals the presence of two types of body shapes (diamond-shaped and torpedo-shaped individuals) which is interpreted as sexual shape dimorphism (SSD). A unimodal size distribution illustrates a dominance of diamond-shaped specimens, but both body shape types are present in small (young) and big sized (old) individuals. The abundance of well-articulated and complete specimens suggests that T. formosa populated deep levels of the water column, which excluded buoyancy and flotation as well as carcass disintegration near the surface. The reconstruction of the dorsal and anal fins suggests the presence of a membrane between each fin ray and allows for ecological comparison of T. formosa with modern fan fishes.
Comparison of new biomarkers in the diagnosis of perinatal asphyxia

Objectives Precise and early diagnosis of neonatal asphyxia may improve outcomes. Recent studies aim to identify diagnostic biomarkers in neonates at risk for brain damage. The current study was designed to evaluate the diagnostic value of new biomarkers for neonatal asphyxia. Materials & Methods This prospective study was conducted with an available sampling of infants upper 35 weeks of gestational age, including neonates with asphyxia (case group) and healthy controls, 2014-2022, in Ghaem Hospital, Mashhad, Iran. Data collection was performed utilizing a researcher-made questionnaire, including maternal and neonatal characteristics, as well as clinical and laboratory evaluation. Serum umbilical cord levels of interleukin-6 (IL6), interleukin-1-beta (IL- 1β), pro-oxidant-antioxidant balance (PAB), and heat shock protein-70 (HSP70), as well as nucleated red blood cells count (NRBC), were determined. Data were analyzed by t-test, Chi-square, receiver operating characteristic (ROC), and regression models. Results The differences in variables IL6, IL1β, PAB, NRBC/100WBC, and HSP70 were statistically significant between the two groups (in all cases, P<0.0001). In the diagnosis of asphyxia, the most sensitive marker (89%) was IL1β more than 2.39 pg/ml and HSP 70 upper than 0.23 ng/ml, while IL6 was higher than 9pg/ml, determined as the most specific marker (85%). Furthermore, a combination of HSP + PAB and IL6 + lL1b + PAB + NRBC/100WBC possesses the prediction power of 93.2% and 87.3%, respectively, for diagnosing asphyxia. Conclusion According to data analysis, the combination of new biochemical markers (NRBC count, IL6, IL1β, PAB, and HSP 70) could be a reliable marker for diagnosing infants with asphyxia.
Regional weather and climates of the British Isles – Part 3: The Midlands

In this article, the Midlands comprise Shropshire, Herefordshire, Worcestershire, Warwickshire, West Midlands, Staffordshire, Derbyshire (apart from the High Peak), Nottinghamshire, Lincolnshire, Leices tershire, Rutland and Northamptonshire (Figure 1). Kings and Giles (1997) likened the Midlands to a shallow bowl with a subdued central dome. The bowl’s edges are the Welsh Marches, the Cotswolds, the Northamptonshire Jurassic escarpment, the Derbyshire Peak and the Staffordshire Moorlands. The subdued central dome is the Birmingham Plateau (>120m), which acts as the watershed between rivers that drain southwest into the Severn and Avon and those that drain northeast into the Trent. In the west, isolated ranges of hills such as the Malverns and the Long Mynd rise out of the Midland Plain. Stations within the region with long records include Raunds (Northamptonshire), where records began in the 1850s, Ross-onWye and, from 1793, Birmingham. The reopening of the climatological station at Ross-on-Wye in 1985 is a particularly welcome addition to the observing network, as it extends a record that originally commenced at a site elsewhere in the town in 1859 and sustained observations at the current site from 1914 until 1975 (Parsons, 1975). The well-known Central England Temperature (CET) series has always included one station from the Midlands, as well as extending more widely into northwest and southeast England. It is the world’s longest instrumental record, starting in 1659 as a monthly series, and is currently defined by the Met Office’s Hadley Centre as the average of Rothamsted (Hertfordshire), Pershore College (Worcestershire) and Stonyhurst (Lancashire). Also worthy of note are the voluntary observing sites at Bablake School in Coventry, at Newtown Linford (Leicestershire) – where records started in 1959 – and at Middleton (in the Peak District of Derbyshire at 321m), the data for which are used extensively in this article.
The impact of mass gatherings on the local transmission of COVID-19 and the implications for social distancing policies: Evidence from Hong Kong

Mass gatherings provide conditions for the transmission of infectious diseases and pose complex challenges to public health. Faced with the COVID-19 pandemic, governments and health experts called for suspension of gatherings in order to reduce social contact via which virus is transmitted. However, few studies have investigated the contribution of mass gatherings to COVID-19 transmission in local communities. In Hong Kong, the coincidence of the relaxation of group gathering restrictions with demonstrations against the National Security Law in mid-2020 raised concerns about the safety of mass gatherings under the pandemic. Therefore, this study examines the impacts of mass gatherings on the local transmission of COVID-19 and evaluates the importance of social distancing policies. With an aggregated dataset of epidemiological, city-level meteorological and socioeconomic data, a Synthetic Control Method (SCM) is used for constructing a ‘synthetic Hong Kong’ from over 200 Chinese cities. This counterfactual control unit is used to simulate COVID-19 infection patterns (i.e., the number of total cases and daily new cases) in the absence of mass gatherings. Comparing the hypothetical trends and the actual ones, our results indicate that the infection rate observed in Hong Kong is substantially higher than that in the counterfactual control unit (2.63% vs. 0.07%). As estimated, mass gatherings increased the number of new infections by 62 cases (or 87.58% of total new cases) over the 10–day period and by 737 cases (or 97.23%) over the 30-day period. These findings suggest the necessity of tightening social distancing policies, especially the prohibition on group gathering regulation (POGGR), to prevent and control COVID-19 outbreaks.
Optimising personal continuity: a survey of GPs’ and older patients’ views

Background Personal continuity — having a GP who knows their patients and keeps track of them — is an important dimension of continuity of care and is associated with lower mortality rates, higher quality of life, and reduced healthcare costs. In recent decades it has become more challenging for GPs to provide personal continuity owing to changes in society and health care. Aim To investigate GPs’ and older patients’ views on personal continuity and how personal continuity can be improved. Design & setting Cross sectional survey study in The Netherlands. Method A digital and postal survey was sent to 499 GPs and 1599 patients aged 65 years or older. Results were analysed using descriptive statistics for quantitative data and thematic analysis for open questions. Results In total, 249 GPs and 582 patients completed the surveys. A large majority of GPs (92–99%) and patients (91–98%) felt it was important for patients to see their own GP for life events or psychosocial issues. GPs and patients provided suggestions on how personal continuity can be improved. The thematic analysis of these suggestions identified nine themes: 1) personal connection, 2) GP accessibility and availability, 3) communication about (dis)continuity, 4) GP responsibility, 5) triage, 6) time for the patient, 7) actions by third parties, 8) team continuity, and 9) GP vocational training. Conclusion Both GPs and older patients still place high value on personal continuity in the context of a changing society. GPs and patients provided a wide range of suggestions for improving personal continuity. The authors will use these suggestions to develop interventions for optimising personal continuity in general practice.
A new method of data transformation for satellite images: I. Methodology and transformation equations for TM images

These two papers deal with a new method of data transformation. By analysing grey level curves (broken lines) of various ground features in image bands of different satellites, we have found that, inherent in 3‐ or 4‐band satellite images (SPOT, IKONOS, Quick Bird, OrbView, FORMOSAT and MSS) there are three basic remote‐sensing characteristics as follows: (1) the general radiance level L; (2) the visible–infrared radiation balance B; and (3) the band radiance variation vector (direction and speed) V. However, inherent in 5‐ or 7‐band satellite images (NOAA, TM), besides the above three, there is an extra basic remote‐sensing characteristic, i.e. the thermal radiation intensity I. This is denoted by thermal bands, i.e. the TM band 6 or NOAA (AVHRR) bands 4 and 5, which are relatively independent and can be used directly, and hence are unnecessary for information extraction or data transformation. Therefore, the data transformation only lies in extracting the L, B and V from original satellite images. Furthermore, we have also found that there are three basic ground‐cover elements on the Earth's surface: i.e. the bare land (in a broad sense), the vegetation and the water body, which, in different proportions, constitute all ground cover. Moreover, there are three basic (primitive) colours on colour image (including colour composite of satellite images): i.e. red, green and blue, which generate all colours on the colour image. Further research has revealed that the three basic remote‐sensing characteristics, the three basic ground‐cover elements and the three basic colours on the composite can conceptually constitute a three‐to‐three corresponding regular triangle scheme. Perhaps a good method of data transformation should make the scheme realistic, i.e. make the three ‘threes’ all correspond to each other. The research presented here has completed this task by regression calculations and selection of specific variables. First, the methodology and transformation equations for TM images are discussed. The transformed L, B and V images have relatively independent and equally distributed information as well as clear and definite physical, mathematical and geographical significance. They can be used effectively for generating high‐quality colour composites, on which the red, green, blue, yellow, pink, cyan and other colours of various kinds are all generated and all pure, saturated, equilibrated, meaning‐definite and close to the colours of ground features in nature. As a result, interpretations and discriminations of ground features can be easier and conducted not only by experience, but also by logic. The L, B and V images can also be used effectively for classification and digital analysis of ground features. With regard to the transformation equations for SPOT, NOAA, IKONOS, Quick Bird, OrbView, FORMOSAT and MSS images and the method application will be dealt with in the second paper.
MB-DECTNet: a model-based unrolled network for accurate 3D DECT reconstruction

Numerous dual-energy CT (DECT) techniques have been developed in the past few decades. Dual-energy CT (DECT) statistical iterative reconstruction (SIR) has demonstrated its potential for reducing noise and increasing accuracy. Our lab proposed a joint statistical DECT algorithm for stopping power estimation and showed that it outperforms competing image-based material-decomposition methods. However, due to its slow convergence and the high computational cost of projections, the elapsed time of 3D DECT SIR is often not clinically acceptable. Therefore, to improve its convergence, we have embedded DECT SIR into a deep learning model-based unrolled network for 3D DECT reconstruction (MB-DECTNet) that can be trained in an end-to-end fashion. This deep learning-based method is trained to learn the shortcuts between the initial conditions and the stationary points of iterative algorithms while preserving the unbiased estimation property of model-based algorithms. MB-DECTNet is formed by stacking multiple update blocks, each of which consists of a data consistency layer (DC) and a spatial mixer layer, where the spatial mixer layer is the shrunken U-Net, and the DC layer is a one-step update of an arbitrary traditional iterative method. Although the proposed network can be combined with numerous iterative DECT algorithms, we demonstrate its performance with the dual-energy alternating minimization (DEAM). The qualitative result shows that MB-DECTNet with DEAM significantly reduces noise while increasing the resolution of the test image. The quantitative result shows that MB-DECTNet has the potential to estimate attenuation coefficients accurately as traditional statistical algorithms but with a much lower computational cost.
iPAL: A Machine Learning Based Smart Healthcare Framework For Automatic Diagnosis Of Attention Deficit/Hyperactivity Disorder (ADHD)

ADHD is a prevalent disorder among the younger population. Standard evaluation techniques currently use evaluation forms, interviews with the patient, and more. However, its symptoms are similar to those of many other disorders like depression, conduct disorder, and oppositional defiant disorder, and these current diagnosis techniques are not very effective. Thus, a sophisticated computing model holds the potential to provide a promising diagnosis solution to this problem. This work attempts to explore methods to diagnose ADHD using combinations of multiple established machine learning techniques like neural networks and SVM models on the ADHD200 dataset and explore the field of neuroscience. In this work, multiclass classification is performed on phenotypic data using an SVM model. The better results have been analyzed on the phenotypic data compared to other supervised learning techniques like Logistic regression, KNN, AdaBoost, etc. In addition, neural networks have been implemented on functional connectivity from the MRI data of a sample of 40 subjects provided to achieve high accuracy without prior knowledge of neuroscience. It is combined with the phenotypic classifier using the ensemble technique to get a binary classifier. It is further trained and tested on 400 out of 824 subjects from the ADHD200 data set and achieved an accuracy of 92.5% for binary classification The training and testing accuracy has been achieved upto 99% using ensemble classifier.
Stroke-based Rendering: From Heuristics to Deep Learning

In the last few years, artistic image-making with deep learning models has gained a considerable amount of traction. A large number of these models operate directly in the pixel space and generate raster images. This is however not how most humans would produce artworks, for example, by planning a sequence of shapes and strokes to draw. Recent developments in deep learning methods help to bridge the gap between stroke-based paintings and pixel photo generation. With this survey, we aim to provide a structured introduction and understanding of common challenges and approaches in stroke-based rendering algorithms. These algorithms range from simple rule-based heuristics to stroke optimization and deep reinforcement agents, trained to paint images with differentiable vector graphics and neural rendering.
Correlation-induced sensitivity and non-Hermitian skin effect of quasiparticles

Non-Hermitian (NH) Hamiltonians have been shown to exhibit unique signatures, including the NH skin effect and an exponential spectral sensitivity with respect to boundary conditions. Here, we investigate as to what extent these remarkable phenomena, recently predicted and observed in a broad range of settings, may also occur in closed correlated fermionic systems that are governed by a Hermitian many-body Hamiltonian. There, an effectively NH quasiparticle description naturally arises in the Green's function formalism due to inter-particle scattering that represents an inherent source of dissipation. As a concrete platform we construct an extended interacting Su-Schrieffer-Heeger (SSH) model subject to varying boundary conditions, which we analyze using exact diagonalization and non-equilibrium Green's function methods. That way, we clearly identify the presence of the aforementioned NH phenomena in the quasi-particle properties of this Hermitian model system.
Faster maximal clique enumeration in large real-world link streams

Link streams offer a good model for representing interactions over time. They consist of links $(b,e,u,v)$, where $u$ and $v$ are vertices interacting during the whole time interval $[b,e]$. In this paper, we deal with the problem of enumerating maximal cliques in link streams. A clique is a pair $(C,[t_0,t_1])$, where $C$ is a set of vertices that all interact pairwise during the full interval $[t_0,t_1]$. It is maximal when neither its set of vertices nor its time interval can be increased. Some of the main works solving this problem are based on the famous Bron-Kerbosch algorithm for enumerating maximal cliques in graphs. We take this idea as a starting point to propose a new algorithm which matches the cliques of the instantaneous graphs formed by links existing at a given time $t$ to the maximal cliques of the link stream. We prove its validity and compute its complexity, which is better than the state-of-the art ones in many cases of interest. We also study the output-sensitive complexity, which is close to the output size, thereby showing that our algorithm is efficient. To confirm this, we perform experiments on link streams used in the state of the art, and on massive link streams, up to 100 million links. In all cases our algorithm is faster, mostly by a factor of at least 10 and up to a factor of $10^4$. Moreover, it scales to massive link streams for which the existing algorithms are not able to provide the solution.
Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity

Intelligent biological systems are characterized by their embodiment in a complex environment and the intimate interplay between their nervous systems and the nonlinear mechanical properties of their bodies. This coordination, in which the dynamics of the motor system co-evolved to reduce the computational burden on the brain, is referred to as ``mechanical intelligence'' or ``morphological computation''. In this work, we seek to develop machine learning analogs of this process, in which we jointly learn the morphology of complex nonlinear elastic solids along with a deep neural network to control it. By using a specialized differentiable simulator of elastic mechanics coupled to conventional deep learning architectures -- which we refer to as neuromechanical autoencoders -- we are able to learn to perform morphological computation via gradient descent. Key to our approach is the use of mechanical metamaterials -- cellular solids, in particular -- as the morphological substrate. Just as deep neural networks provide flexible and massively-parametric function approximators for perceptual and control tasks, cellular solid metamaterials are promising as a rich and learnable space for approximating a variety of actuation tasks. In this work we take advantage of these complementary computational concepts to co-design materials and neural network controls to achieve nonintuitive mechanical behavior. We demonstrate in simulation how it is possible to achieve translation, rotation, and shape matching, as well as a ``digital MNIST'' task. We additionally manufacture and evaluate one of the designs to verify its real-world behavior.
On parametric instabilities of finite-amplitude internal gravity waves

The equations describing parametric instabilities of a finite-amplitude internal gravity wave in an inviscid Boussinesq fluid are studied numerically. By improving the numerical approach, discarding the concept of spurious roots and considering the whole range of directions of the Floquet vector, Mied's work is generalized to its full complexity. In the limit of large disturbance wavenumbers, the unstable disturbances propagate in the directions of the two infinite curve segments of the related resonant-interaction diagram. They can therefore be classified into two families which are characterized by special propagation directions. At high wavenumbers the maximum growth rates converge to limits which do not depend on the direction of the Floquet vector. The limits are different for both families; the disturbance waves propagating at the smaller angle to the basic gravity wave grow at the larger rate.
Randomized Control of Wireless Temporal Coherence Via Reconfigurable Intelligent Surface

A reconfigurable intelligent surface (RIS) can shape the wireless propagation channel by inducing controlled phase shift variations to the impinging signals. Multiple works have considered the use of RIS with time-varying configurations of reflection coefficients. In this work, we use the RIS to control the channel coherence time and introduce a generalized discrete-time-varying channel model for RIS-aided systems. We characterize the temporal variation of the channel correlation by assuming that the configuration of RIS' elements changes at every time step. The analysis converges to a randomized framework to control the channel coherence time by setting the number of RIS' elements and their phase shifts. The main result is a framework for a flexible block-fading model, where the number of samples within a coherence block can be dynamically adapted.