Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in Fundus Autofluorescence after Anti-vascular Endothelial Growth Factor According to the Type of Choroidal Neovascularization in Age-related Macular Degeneration

Purpose To describe the changes of fundus autofluorescence (FAF) in patients with age-related macular degeneration before and after intravitreal injection of anti-vascular endothelial growth factor according to the type of choroidal neovascularization (CNV) and to evaluate the correlation of FAF with spectral domain optical coherence tomography (SD-OCT) parameters and vision. Methods This was a retrospective study. Twenty-one treatment-naïve patients with neovascular age-related macular degeneration were included. Study eyes were divided into two groups according to the type of CNV. Fourteen eyes were type 1 CNV and seven eyes were type 2 CNV. All eyes underwent a complete ophthalmologic examination, including an assessment of best-corrected visual acuity, SD-OCT, fluorescein angiography, and FAF imaging, before and 3 months after intravitreal anti-vascular endothelial growth factor injection. Gray scales of FAF image for CNV areas, delineated as in fluorescein angiography, were analyzed using the ImageJ program, which were adjusted by comparison with normal background areas. Correlation of changes in FAF with changes in SD-OCT parameters, including CNV thickness, photoreceptor inner and outer segment junction disruption length, external limiting membrane disruption length, central macular thickness, subretinal fluid, and intraretinal fluid were analyzed. Results Eyes with both type 1 and type 2 CNV showed reduced FAF before treatment. The mean gray scales (%) of type 1 and type 2 CNV were 52.20% and 42.55%, respectively. The background values were 106.72 and 96.86. After treatment, the mean gray scales (%) of type 1 CNV and type 2 CNV were changed to 57.61% (p = 0.005) and 57.93% (p = 0.008), respectively. After treatment, CNV thickness, central macular thickness, and inner and outer segment junction disruption length were decreased while FAF increased. Conclusions FAF was noted to be reduced in eyes with newly diagnosed wet age-related macular degeneration, but increased after anti-vascular endothelial growth factor therapy regardless of CNV lesion type.
Impact of paging channel overloads or attacks on a cellular network

IP and cellular phone networks used to be isolated from each other. In recent years however, the two networks have started to overlap with the emergence of devices that access the Internet using cellular infrastructures. One important question then, given this overlap, is whether actions or threats on the Internet side can impact the telecom or cellular side. We address this problem in the paper and specifically consider threats on the paging channel, which is a key conduit shared by both Internet and cellular traffic.Our contributions are as follows: we illustrate through experiments on a CDMA2000 cellular network that overloads or attacks launched from the Internet can significantly increase the paging load and increase the delay of paging messages, including cellular call setup requests, which in turn could lead to non-completed calls; we derive a simple but accurate queuing model for the paging system in a CDMA2000 network and use this model to demonstrate that the paging channel exhibits sharp rather than graceful degradation under load; and through this model, we identify critical parameters that impact paging performance. Although our study is focused on CDMA2000 networks, we believe that similar problems exist in other types of cellular networks that employ a single control channel with limited bandwidth for both voice and data services.
Human neural stem cell transplantation promotes functional recovery in rats with experimental intracerebral hemorrhage.

BACKGROUND AND PURPOSE Cell transplantation has been used to reduce behavioral deficit in cerebral ischemia. However, there is no report about cell transplantation in experimental intracerebral hemorrhage (ICH). We hypothesize that intravenously transplanted human neural stem cells (NSCs) can migrate and differentiate into neurons or glial cells, thereby improving functional outcome in ICH. METHODS Experimental ICH was induced by intrastriatal administration of bacterial collagenase in adult rats. One day after surgery, the rats were randomly divided into 2 groups to receive intravenously either immortalized Lac z-positive human NSCs (5x10(6) cells in 500 microL, n=12) or the same amount of saline (n=13). The animals were evaluated for 8 weeks with modified limb placing and rotarod tests. Transplanted NSCs were detected by X-gal histochemistry or beta-gal immunohistochemistry with double labeling of GFAP, NeuN, neurofilament, or CNPase. RESULTS Intravenously transplanted NSCs migrated selectively to the perihematomal areas and differentiated into neurons (approximately 10% of beta-gal+ cells) and astrocytes (approximately 75%). The NSC-transplanted group showed better functional performance on rotarod test after 2 weeks and on modified limb placing test after 5 weeks compared with the control group (P<0.05), and these effects persisted for up to 8 weeks. There was no difference in the final hemispheric area between the 2 groups. CONCLUSIONS Intravenously transplanted NSCs can enter the rat brain with ICH, survive, migrate, and improve functional recovery. Transplantation of human NSCs can be used to restore neurological deficits in experimental ICH.
Chimpanzee use of space on a forest-agricultural mosaic at Madina in Cantanhez National Park, Guinea-Bissau

Continuous human population growth and increased fragmentation of natural habitats are leading to numerous non-human primate species living in proximity to human settlements. Like other non-human primates, chimpanzees (Pan troglodytes verus) are frequently reported to inhabit human-influenced habitats. However, our understanding about chimpanzee behavioural adaptations to these habitats is still limited. This dissertation presents a cross-disciplinary approach to understand chimpanzee and human sympatry in a forest-agricultural mosaic habitat at Madina in Cantanhez National Park, Guinea-Bissau. The aim of this research is to examine the home range and habitat preference on this unhabituated and unstudied community of chimpanzees to better inform their long-term survival alongside local people. A set of interviews were done in order to understand local people’s perceptions toward chimpanzees, their agricultural activities, and use of forest resources. These data help to create a framework to understand the extent of human activities in the area. During the study period, there were high levels of human-influenced changes on the landscape due to slash-and-burn activities. Cashew seems to have an increasing value to human communities. However, human-chimpanzee interactions are considered ‘low-conflict’ due to local cultural beliefs towards chimpanzees and differential utilization of this cash crop by both species. A map of the study area was created showing a highly fragmented habitat, and a set of indirect (faeces, feeding traces, nest) and direct (opportunistic observations) chimpanzee signs were collected to understand this communities distribution. For the first time, two commonly used methods to calculate animal home range size (Minimum Convex Polygon and Kernel analysis) were used in an unhabituated chimpanzee community living in a human-influenced habitat. The data demonstrate that Kernel analysis gives more realistic results suggesting that Madina chimpanzee community has an estimated home range size of 8.93 Km 2 . The correlation of home range size and community size of different chimpanzee communities suggests that Madina chimpanzees may have a community size of about 48 individuals. These results show a relatively small home range and large community size, which suggests that Madina chimpanzees may currently be successfully exploiting this anthropogenic habitat. Analysis of the distribution of chimpanzee signs show higher levels of utilization of ‘natural’ habitats by the species. However, feeding trace distribution showed that orchards and young forest areas have similar levels of utilization by chimpanzees for foraging. Analysis of the feeding traces during the study period suggests that chimpanzees are feeding on similar proportions of cultivated and forest resources suggesting that chimpanzees are integrating cultivars into their feeding strategies. This may be caused by the relative tolerance shown by neighbouring human communities
Fourier Transform-based Estimators for Data Sketches

In this paper we consider the problem of estimating the $f$-moment ($\sum_{v\in [n]} (f(\mathbf{x}(v))-f(0))$) of a dynamic vector $\mathbf{x}\in \mathbb{G}^n$ over some abelian group $(\mathbb{G},+)$, where the $\|f\|_\infty$ norm is bounded. We propose a simple sketch and new estimation framework based on the \emph{Fourier transform} of $f$. I.e., we decompose $f$ into a linear combination of homomorphisms $f_1,f_2,\ldots$ from $(\mathbb{G},+)$ to $(\mathbb{C},\times)$, estimate the $f_k$-moment for each $f_k$, and synthesize them to obtain an estimate of the $f$-moment. Our estimators are asymptotically unbiased and have variance asymptotic to $\|\mathbf{x}\|_0^2 (\|f\|_\infty^2 m^{-1} + \|\hat{f}\|_1^2 m^{-2})$, where the size of the sketch is $O(m\log n\log|\mathbb{G}|)$ bits. When $\mathbb{G}=\mathbb{Z}$ this problem can also be solved using off-the-shelf $\ell_0$-samplers with space $O(m\log^2 n)$ bits, which does not obviously generalize to finite groups. As a concrete benchmark, we extend Ganguly, Garofalakis, and Rastogi's singleton-detector-based sampler to work over $\mathbb{G}$ using $O(m\log n\log|\mathbb{G}|\log(m\log n))$ bits. We give some experimental evidence that the Fourier-based estimation framework is significantly more accurate than sampling-based approaches at the same memory footprint.
Geom-DeepONet: A Point-cloud-based Deep Operator Network for Field Predictions on 3D Parameterized Geometries

Modern digital engineering design process commonly involves expensive repeated simulations on varying three-dimensional (3D) geometries. The efficient prediction capability of neural networks (NNs) makes them a suitable surrogate to provide design insights. Nevertheless, few available NNs can handle solution prediction on varying 3D shapes. We present a novel deep operator network (DeepONet) variant called Geom-DeepONet, which encodes parameterized 3D geometries and predicts full-field solutions on an arbitrary number of nodes. To the best of the authors' knowledge, this is the first attempt in the literature and is our primary novelty. In addition to expressing shapes using mesh coordinates, the signed distance function for each node is evaluated and used to augment the inputs to the trunk network of the Geom-DeepONet, thereby capturing both explicit and implicit representations of the 3D shapes. The powerful geometric encoding capability of a sinusoidal representation network (SIREN) is also exploited by replacing the classical feedforward neural networks in the trunk with SIREN. Additional data fusion between the branch and trunk networks is introduced by an element-wise product. A numerical benchmark was conducted to compare Geom-DeepONet to PointNet and vanilla DeepONet, where results show that our architecture trains fast with a small memory footprint and yields the most accurate results among the three with less than 2 MPa stress error. Results show a much lower generalization error of our architecture on unseen dissimilar designs than vanilla DeepONet. Once trained, the model can predict vector solutions, and speed can be over $10^5$ times faster than implicit finite element simulations for large meshes.
UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction

Vehicle trajectory prediction has increasingly relied on data-driven solutions, but their ability to scale to different data domains and the impact of larger dataset sizes on their generalization remain under-explored. While these questions can be studied by employing multiple datasets, it is challenging due to several discrepancies, e.g., in data formats, map resolution, and semantic annotation types. To address these challenges, we introduce UniTraj, a comprehensive framework that unifies various datasets, models, and evaluation criteria, presenting new opportunities for the vehicle trajectory prediction field. In particular, using UniTraj, we conduct extensive experiments and find that model performance significantly drops when transferred to other datasets. However, enlarging data size and diversity can substantially improve performance, leading to a new state-of-the-art result for the nuScenes dataset. We provide insights into dataset characteristics to explain these findings. The code can be found here: https://github.com/vita-epfl/UniTraj
Motor Performance After Neonatal Extracorporeal Membrane Oxygenation: A Longitudinal Evaluation

OBJECTIVE: To assess longitudinally children’s motor performance 5 to 12 years after neonatal extracorporeal membrane oxygenation (ECMO) and to evaluate associations between clinical characteristics and motor performance. METHODS: Two hundred fifty-four neonatal ECMO survivors in the Netherlands were tested with the Movement Assessment Battery for Children at 5, 8, and/or 12 years. Percentile scores were transformed to z scores for longitudinal evaluation (norm population mean = 0 and SD = 1). Primary diagnoses: meconium aspiration syndrome (n = 137), congenital diaphragmatic hernia (n = 49), persistent pulmonary hypertension of the newborn (n = 36), other diagnoses (n = 32). RESULTS: Four hundred fifty-six tests were analyzed. At 5, 8, and 12 years motor performance was normal in 73.7, 74.8, and 40.5%, respectively (vs 85% expected based on reference values; P < .001 at all ages). In longitudinal analyses mean (95% confidence interval [CI]) z scores were –0.42 (–0.55 to –0.28), –0.25 (–0.40 to –0.10) and –1.00 (–1.26 to –0.75) at 5, 8, and 12 years, respectively. Mean score at 8 years was significantly higher than at 5 years (difference 0.16, 95% CI 0.02 to 0.30), and mean score at 12 years was significantly lower than at both other ages (differences –0.59 and –0.75; 95% CI –0.33 to –0.84 and –0.49 to –1.00, respectively). Children with congenital diaphragmatic hernia encountered problems at all ages. The presence of chronic lung disease was negatively related with outcome. CONCLUSIONS: Motor problems in neonatal ECMO survivors persist throughout childhood and become more obvious with time.
Climate change, population pressure, and wars in European history

ABSTRACT Recent studies show that wars were more prevalent during colder periods in human history. Nevertheless, the temporal consistency of the climate-war correlation in Europe over extended period has rarely been examined systematically. In this study, we extended the European violent conflict record in the Conflict Catalog [Brecke 1999. “Violent conflicts 1400 A.D. to the present in different regions of the world. ” Paper presented at the 1999 Meeting of the Peace Science Society (International), Ann Arbor, MI, 8–10 October 1999] back to the year AD900, and examined quantitatively the climate-war consistency in Europe in AD900–1999. The period covers the Medieval Warm Period, Little Ice Age, and twentieth-century warming. Grounded on a total number of 2309 recorded violent conflicts in Europe over the last 1100 years, our statistical results were: (1) the negative temperature-war correlation was significant in terms of multi-decadal cycles; (2) in the second half of the period (AD1450–1999): the climate-war relationship was more apparent during longer cycles; a large spatial extent of slight cooling was more pertinent than a small spatial extent of severe cooling in affecting social stability in Europe; and the overall temperature-war correlation was stronger; and (3) the climate-war association was temporarily distorted when population pressure was drastically reduced. The association became significant again once the population system pushed against its Malthusian constraints. In sum, the climate-war association in Europe was statistically significant at the multi-decadal timescale. Yet, its strength varied across different periods and was contingent upon population pressure during the time. The findings in this study may provide some hints in assessing the effectiveness of human adaptations to climate change in the long-term.
DSGG: Dense Relation Transformer for an End-to-end Scene Graph Generation

Scene graph generation aims to capture detailed spatial and semantic relationships between objects in an image, which is challenging due to incomplete labelling, long-tailed relationship categories, and relational semantic overlap. Existing Transformer-based methods either employ distinct queries for objects and predicates or utilize holistic queries for relation triplets and hence often suffer from limited capacity in learning low-frequency relationships. In this paper, we present a new Transformer-based method, called DSGG, that views scene graph detection as a direct graph prediction problem based on a unique set of graph-aware queries. In particular, each graph-aware query encodes a compact representation of both the node and all of its relations in the graph, acquired through the utilization of a relaxed sub-graph matching during the training process. Moreover, to address the problem of relational semantic overlap, we utilize a strategy for relation distillation, aiming to efficiently learn multiple instances of semantic relationships. Extensive experiments on the VG and the PSG datasets show that our model achieves state-of-the-art results, showing a significant improvement of 3.5\% and 6.7\% in mR@50 and mR@100 for the scene-graph generation task and achieves an even more substantial improvement of 8.5\% and 10.3\% in mR@50 and mR@100 for the panoptic scene graph generation task. Code is available at \url{https://github.com/zeeshanhayder/DSGG}.
Beehive: A Flexible Network Stack for Direct-Attached Accelerators

Accelerators have become increasingly popular in datacenters due to their cost, performance, and energy benefits. Direct-attached accelerators, where the network stack is implemented in hardware and network traffic bypasses the main CPU, can further enhance these benefits. However, modern datacenter software network stacks are complex, with interleaved protocol layers, network management functions, as well as virtualization support. They also need to flexibly interpose new layers to support new use cases. By contrast, most hardware network stacks only support basic protocol compatibility and are often difficult to extend due to using fixed processing pipelines. This paper proposes Beehive, a new, open-source hardware network stack for direct-attached FPGA accelerators designed to enable flexible and adaptive construction of complex protocol functionality. Our approach is based on a network-on-chip (NoC) substrate, automated tooling for the independent scale-up of protocol elements, compiletime deadlock analysis, and a flexible diagnostics and control plane. Our implementation interoperates with standard Linux TCP and UDP clients, allowing existing RPC clients to interface with the accelerator. We use three applications to illustrate the advantages of our approach: a throughputoriented erasure coding application, an accelerator for distributed consensus operations that reduces the latency and energy cost of linearizability, and TCP live migration support for dynamic server consolidation.
TexRO: Generating Delicate Textures of 3D Models by Recursive Optimization

This paper presents TexRO, a novel method for generating delicate textures of a known 3D mesh by optimizing its UV texture. The key contributions are two-fold. We propose an optimal viewpoint selection strategy, that finds the most miniature set of viewpoints covering all the faces of a mesh. Our viewpoint selection strategy guarantees the completeness of a generated result. We propose a recursive optimization pipeline that optimizes a UV texture at increasing resolutions, with an adaptive denoising method that re-uses existing textures for new texture generation. Through extensive experimentation, we demonstrate the superior performance of TexRO in terms of texture quality, detail preservation, visual consistency, and, notably runtime speed, outperforming other current methods. The broad applicability of TexRO is further confirmed through its successful use on diverse 3D models.
Exploring the Task-agnostic Trait of Self-supervised Learning in the Context of Detecting Mental Disorders

Self-supervised learning (SSL) has been investigated to generate task-agnostic representations across various domains. However, such investigation has not been conducted for detecting multiple mental disorders. The rationale behind the existence of a task-agnostic representation lies in the overlapping symptoms among multiple mental disorders. Consequently, the behavioural data collected for mental health assessment may carry a mixed bag of attributes related to multiple disorders. Motivated by that, in this study, we explore a task-agnostic representation derived through SSL in the context of detecting major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) using audio and video data collected during interactive sessions. This study employs SSL models trained by predicting multiple fixed targets or masked frames. We propose a list of fixed targets to make the generated representation more efficient for detecting MDD and PTSD. Furthermore, we modify the hyper-parameters of the SSL encoder predicting fixed targets to generate global representations that capture varying temporal contexts. Both these innovations are noted to yield improved detection performances for considered mental disorders and exhibit task-agnostic traits. In the context of the SSL model predicting masked frames, the generated global representations are also noted to exhibit task-agnostic traits.
Region-based contrast enhancement of mammograms

Diagnostic features in mammograms vary widely in size and shape. Classical image enhancement techniques cannot adapt to the varying characteristics of such features. An adaptive method for enhancing the contrast of mammographic features of varying size and shape is presented. The method uses each pixel in the image as a seed to grow a region. The extent and shape of the region adapt to local image gray-level variations, corresponding to an image feature. The contrast of each region is calculated with respect to its individual background. Contrast is then enhanced by applying an empirical transformation based on each region's seed pixel value, its contrast, and its background. A quantitative measure of image contrast improvement is also defined based on a histogram of region contrast and used for comparison of results. Using mammogram images digitized at high resolution (less than 0.1 mm pixel size), it is shown that the validity of microcalcification clusters and anatomic details is considerably improved in the processed images.