Miracle-dz/UniFAR-FTUs huggingface dataset:
Diﬀusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

As Metaverse emerges as the next-generation Internet paradigm, the ability to eﬃciently generate content is paramount. AIGenerated Content (AIGC) emerges as a key solution, yet the resourceintensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of eﬀectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diﬀusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diﬀusion Soft Actor-Critic (D2SAC) algorithm, enhancing the eﬃciency and eﬀectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a
A More Robust Multigrid Algorithm for Di(cid:27)usion Type Registration Models ∗

Registration refers to the useful process of aligning two similar but di(cid:27)erent intensity image functions in order to either track changes or combine information. Variational models are capable of (cid:28)nding transform maps containing large and non-uniform deformations between such a pair of images. Since (cid:28)nding a transform map is an inverse problem, as with all models, suitable regularisation is necessary to overcome the non-uniqueness of the problem. In the case of di(cid:27)usion type models regularisation terms impose smoothness on the transformation by minimising the gradient of the (cid:29)ow (cid:28)eld. The di(cid:27)usion model also coincides with the basic model for optical (cid:29)ow frameworks of Horn-Schunck (1981, AI). The biggest drawback with variational models is the large computational cost required to solve the highly non-linear system of PDEs; Chumchob-Chen (2011, JCAM) developed a non-linear multigrid (NMG) method to address this cost problem. However, a closer look at the analysis of the NMG scheme highlighted omissions which a(cid:27)ected the convergence of the NMG scheme. Moreover, the NMG method proposed by Chumchob-Chen did not impose any control of non-physical folding which invalidates a map. This paper has proposed several key ideas. First we re-evaluate the analysis of the NMG method to show how the omissions in [16] have a noticeable impact on the convergence of the NMG method. In addition, we also provide a way of estimating the convergence rate of a solver on the coarsest grid in order to estimate the number of iterations that will be required to obtain a solution with appropriate accuracy. Secondly we propose an extension to the Chumchob-Chen NMG method which controls any folding within the deformation. Experimental results on the proposed multigrid framework demonstrate improvements in convergence and the accuracy of registrations compared with previous methods.
Barrier Permeability Using the Patlak Model Perfusion CT Assessment of Blood-Brain Optimal Duration of Acquisition for Dynamic

BACKGROUND AND PURPOSE: A previous study demonstrated the need to use delayed acquisition rather than first-pass data for accurate blood-brain barrier permeability surface product (BBBP) calculation from perfusion CT (PCT) according to the Patlak model, but the optimal duration of the delayed acquisition has not been established. Our goal was to determine the optimal duration of the delayed PCT acquisition to obtain accurate BBBP measurements while minimizing potential motion artifacts and radiation dose. MATERIALS AND METHODS: We retrospectively identified 23 consecutive patients with acute ischemic anterior circulation stroke who underwent a PCT study with delayed acquisition. The Patlak model was applied for the full delayed acquisition (90–240 seconds) and also for truncated analysis windows (90–210, 90–180, 90–150, 90–120 seconds). Linear regression of Patlak plots was performed separately for the full and truncated analysis windows, and the slope of these regression lines was used to indicate BBBP. The full and truncated analysis windows were compared in terms of the resulting BBBP values and the quality of the Patlak fitting. RESULTS: BBBP values in the infarct and penumbra were similar for the full 90-to 240-second acquisition (95% confidence intervals for the infarct and penumbra: 1.62–2.47 and 1.75–2.41 mL (cid:1) 100 g (cid:2) 1 (cid:1) min (cid:2) 1 , respectively) and the 90-to 210-second analysis window (1.82–2.76 and 2.01– 2.74 mL (cid:1) 100 g (cid:2) 1 (cid:1) min (cid:2) 1 , respectively). BBBP values increased significantly with shorter acquisitions. The quality of the Patlak fit was excellent for the full 90-to 240-second and 90-to 210-second acquisitions, but it degraded with shorter
A model-model and data-model comparison for the early Eocene 1 hydrological cycle 2

M. J. Carmichael,, D. J. Lunt, M. Huber, M. Heinemann, J. Kiehl, A. LeGrande, C. A. Loptson, C. D. 3 Roberts, N. Sagoo,, C. Shields, P. J. Valdes, A. Winguth, C. Winguth, and R. D. Pancost 4 5 1 BRIDGE, School of Geographical Sciences and Cabot Institute, University of Bristol, UK 6 2 Organic Geochemistry Unit, School of Chemistry and Cabot Institute, University of Bristol, UK 7 3 Climate Dynamics Prediction Laboratory, Department of Earth Sciences, The University of New Hampshire, USA 8 4 Institute of Geosciences, Kiel University, Germany 9 5 Climate and Global Dynamics Laboratory, UCAR/NCAR, USA 10 6 NASA Goddard Institute for Space Studies, USA 11 7 The Met Office, UK 12 8 Climate Research Group, Department of Earth and Environmental Sciences, University of Texas Arlington, USA 13 a now at: Department of Geology and Geophysics, Yale University, USA 14
Perbandingan Efektivitas Klinis Simvastatin dan Atorvastatin Terhadap Profil Lipid Darah Pasien Dislipidemia di Rumah Sakit Universitas Hasanuddin

: Simvastatin and atorvastatin are statin drugs that work by inhibiting the HMG CoA reductase enzyme. This study aimed to evaluate the clinical effectiveness of simvastatin and atorvastatin in improving the blood lipid profiles of patients with dyslipidemia at the Hasanuddin University Hospital, Makassar, Indonesia. This cross-sectional study uses medical records of dyslipidemia from 2017 to 2020. Linear regression analysis was applied to compare the clinical differences between simvastatin and atorvastatin on the blood lipid parameters. In total, there were 32 simvastatin and 39 atorvastatin users successfully collected using total population sampling. Compared with simvastatin 10 mg, the differences in the effect of atorvastatin 20 mg on LDL, TC, TG and HDL values were 12.11±20.60 (95%Cl: -28.90-53.21), -11.70±11.30 (95%Cl: -14.84-38.24), -5.02±11.54 (95%Cl: -28.04-18.00) and 0.42±4.04 (95%Cl: -7.65-8.48), respectively. Atorvastatin users had better TC, HDL and TG levels than simvastatin users. Meanwhile, simvastatin users had better LDL levels than atorvastatin users. Due to the limited samples, statistical analysis indicates that there were no significant differences in the clinical effectiveness of simvastatin 10 mg and atorvastatin 20 mg. Therefore, a future study with adequate power is needed to ascertain the comparative effectiveness of these statins in Indonesian dyslipidemic patients.
The Development of a New Model for Predictions of Urban Water Demand

Population growth, rapid urbanization and climate change have been producing an ever increasing stress on the limited resources of fresh water. This situation makes prediction models of water demand an important tool for decision making regarding urban water management and conservation. However, there is still the need for improvement of the predictions of the existing models, especially in their estimations of base water use. The purpose of this thesis is to develop a more reliable water demand prediction model which gives a better understanding of water use behaviour. The employed techniques represent a new approach to predictions of daily water use; its base use component is predicted using a function of socioeconomic factors, as opposed to a function of time as in existing approaches. The prediction by the model proposed in this thesis is compared with those by two other existing models, in an application to the city of Brossard in Quebec. Time series of predicted daily urban water use captures observed characteristics very well and improves the results of the weighted coefficient of determination, the relative index of agreement and the root mean square error from the existing approaches. Water use in the city exhibits a downward trend possibly due to an increasing annual charge for water use. The analysis procedures reported in this thesis can be applied to analyze water use in any other cities. The new approach would be a useful tool for decision makers to manage water use by adjusting water consumption policies and price.
Improved interpretation of stable water isotope signals in snow and ice from northern Greenland

This thesis deals with the analysis and interpretation of ice core data from northern Greenland, one of the least studied areas in the Arctic. The focus of the thesis is the study of δ18O values which can be used as a proxy for temperature. The 13 ice cores from the North Greenland Traverse (NGT 93/95) are between 100 and 175 m long and were cored between 1993 and 1995. These cores were dated in this thesis and the high- resolution values of δ18O and accumulation rate averaged to annual mean values. The time series spans the last 500 – 1000 years. The topography of the ice sheet is confirmed as the main influencing factor on the long-term annual mean values of δ18O and snow accumulation rate. The predominant southwest/west winds ensure that the northeast of Greenland is located in the precipitation shadow area of the main ice divide, which divides northern Greenland into east and west regions. The lowest δ18O ratios are found east of the divide. In addition, the eastern ice cores are characterized by small variations in δ18O and thickness of accumulation horizons. The correlation between the time series is low due to the large distance between the ice cores and the influence of stratigraphic local noise. By stacking all individual records the signal-to-noise ratio was improved. The stack is representative for a large area in northern Greenland and allows for conclusions regarding the climate during the last thousand years (ending 1994). Globally-known results, such as the Medieval Climate Anomaly that was followed by the Little Ice Age, could be detected in the averaged δ18O time series. The time series is also sensitive to decadal variations such as the warm signal between 1920 and 1930, which cannot be broken down in climate models. Another warm period around 1420 A.D. was observed, which until now has not been seen in any other data set. It is suspected that changes in the local climate dynamic as a result of changed sea ice coverage in the Arctic Sea are possible reasons. Because there was no clear trend in the δ18O NGT 93/95 data in recent years, additional ice cores were drilled in 2011 and 2012 in northern Greenland at comparable positions, in order to extend the series into more recent times. Five cores, each about 30 m long, were analyzed at the AWI Bremerhaven for this purpose. The cores were yearly dated, and the high-resolution δ18O values were averaged for each year. An overlap of about 100 years made it possible to use the time series as an extension of the NGT 93/95 time series. For the first time, an estimation of the actual air temperatures in Greenland over the last thousand years was possible with only one proxy. Since 1980, there has been a constant warming trend. The gradient of the warming is not unusual when compared with other values from the last thousand years, but the level of the values is unusual high.
Journal of Computational Physics

In this paper, we introduce a high-order accurate ﬁnite element method for incompressible variable density ﬂow. The method uses high-order Taylor-Hood velocity-pressure elements in space and backward diﬀerentiation formula (BDF) time stepping in time. This way of discretization leads to two main issues: ( 𝑖 ) a saddle point system that needs to be solved at each time step; ( 𝑖𝑖 ) a stability issue when the viscosity of the ﬂow goes to zero or if the density proﬁle has a discontinuity. We address the ﬁrst issue by using Schur complement preconditioning and artiﬁcial compressibility approaches. We observed similar performance between these two approaches. To address the second issue, we introduce a modiﬁed artiﬁcial Guermond-Popov viscous ﬂux where the viscosity coeﬃcients are constructed using a newly developed residual-based shock-capturing method. Numerical validations conﬁrm high-order accuracy for smooth problems and accurately resolved discontinuities for problems in 2D and 3D with varying density ratios.
ARTICLE-FACTORS AFFECTING PERFORMANCE OF SMALL AND MEDIUM-SIZED ENTERPRISES IN VIETNAM

The paper aims to determine the factors affecting the performance of Vietnam’s small and medium-sized enterprises (SMEs) in the Ho Chi Minh City area from 2009 to 2019. The authors examine seven statistically significant variables that positively affect SMEs' performance at 5% by using the quantitative method (ordinary least squares regression model - pooled OLS, fixed effect model, random effect model, and generalized least squares). These variables are as follows: firm age, firm size, leverage ratio, revenue growth, gross domestic product (GDP) growth, inflation rate, and quality of local governance. Moreover, the study also applies the trade-off theory, SMEs using the out resources from banks and other outsiders lead to efficient firm performance such as revenue growth and using higher financial leverage will result in higher firm performance including saving up on taxes and improved business efficiency. Furthermore, there is the existence of homoscedasticity and no-autocorrelation in the model when using generalized least squares. These tests confirm that the model estimation is both unbiased and reliable. One of the most significant contributions of this study to the existing literature on the subject is the confirmation that macroeconomic and firm-specific variables affect the performance of SMEs. The results are highly variable from an econometric standpoint, both, in the long and short run.
This paper is included in the Proceedings of the 32nd USENIX Security Symposium.

ZigBee is a popular wireless communication standard for Internet of Things (IoT) networks. Since each ZigBee network uses hop-by-hop network-layer message authentication based on a common network key, it is highly vulnerable to packet-injection attacks, in which the adversary exploits the compromised network key to inject arbitrary fake packets from any spoofed address to disrupt network operations and consume the network/device resources. In this paper, we present PhyAuth, a PHY hop-by-hop message authentication framework to defend against packet-injection attacks in ZigBee networks. The key idea of PhyAuth is to let each ZigBee transmitter embed into its PHY signals a PHY one-time pass-word (called POTP) derived from a device-speciﬁc secret key and an efﬁcient cryptographic hash function. An authentic POTP serves as the transmitter’s PHY transmission permission for the corresponding packet. PhyAuth provides three schemes to embed, detect, and verify POTPs based on different features of ZigBee PHY signals. In addition, PhyAuth involves lightweight PHY signal processing and no change to the ZigBee protocol stack. Comprehensive USRP experiments conﬁrm that PhyAuth can efﬁciently detect fake packets with very low false-positive and false-negative rates while having a negligible negative impact on normal data transmissions.
Associations between pre-pregnancy obesity and asthma symptoms in adolescents

Background The high prevalence of children’s asthma symptoms, worldwide, is unexplained. We examined the relation between maternal pre-pregnancy weight and body mass index (BMI), and asthma symptoms in adolescents. Methods Data from 6945 adolescents born within the Northern Finland Birth Cohort 1986 were used. Prospective antenatal and birth outcome data, including maternal pre-pregnancy weight and BMI, and asthma symptoms in adolescent offspring at age 15 e 16 years, were employed. Logistic regression analyses were performed to examine the associations between relevant prenatal factors and asthma symptoms during adolescence. Results Current wheeze (within the past year) was reported by 10.6% of adolescents, and physician-diagnosed asthma by 6.0%. High maternal pre-pregnancy BMI was a signiﬁcant predictor of wheeze in the adolescents (increase per kilogram per square metre unit; 2.7%, 95% CI 0.9 to 4.4 for ever wheeze; 3.5%, 95% CI 1.3 to 5.8 for current wheeze), and adjusting for potential confounders further increased the risk (2.8%, 95% CI 0.5 to 5.1; 4.7%, 95% CI 1.9 to 7.7, respectively). High maternal pre-pregnancy weight, in the top tertile, also signiﬁcantly increased the odds of current wheeze in the adolescent by 20% (95% CI 4 to 39), and adjusting for potential confounders further increased the risk (OR ¼ 1.52, 95% CI 1.19 to 1.95). Results were similar for current asthma. Furthermore, these signiﬁcant associations were observed only among adolescents without parental history of atopy but not among those with parental history of atopy. Conclusions The association demonstrated here between maternal pre-pregnancy overweight and obesity, and asthma symptoms in adolescents suggests that increase in asthma may be partly related to the rapid rise in obesity in recent years.
Homeostatic synaptic scaling optimizes learning in network models of neural population codes

Studying and understanding the code of large neural populations hinge on learning accurate statistical models of population activity. A new class of such models, based on learning to weigh sparse nonlinear Random Projections (RP) of the population, was recently shown to be highly accurate, efﬁcient, and scalable. Moreover, RP models have a clear biologically-plausible implementation as a shallow neural circuit. Here we extend these models and present RP models that are learned by optimizing the randomly selected sparse projections. This “reshap-ing” of projections is akin to changing synaptic connections in the corresponding neural circuit model. When we applied these Reshaped RP models to recordings of tens of cortical neurons from behaving monkeys, we found them to be more accurate and efﬁcient than the previous class of RP models and on par with backpropagation models. Our exploration of the effect of adding biological features to these circuit models revealed that learning reshaped RP models with homeostatic synaptic scaling yields even more efﬁcient and accurate models. We further show that homeostatic reshaped RP models, which rely on sparse and random connectivity, are superior to fully connected network models. Our results thus suggest a key functional role for homeostatic scaling in neural circuits, beyond regulating network activity, namely – optimizing performance and efﬁciency.
Towards Adaptive ML Traﬀic Processing Systems

Machinelearningtechniquesareacommonsolutionusedtosolveavarietyofnetworkmanagementtasks.Often, anetworkadministratorchoosesthemodeltodeploybasedonofflineinformation,suchasmodelperformance andsystemload.Yet,networktrafficisinherentlydynamicmakingithardtoselectanoptimalmodelthatcan workthroughouteverchangingconditions.Inthispaper,wemakethecasethat,insteadofhavingtoselectthe optimalcandidatemodelbasedonofflineinformation,systemsshouldadaptbasedonthenetworkconditions observed.Wepresentasystemdesignthattakesasinputasetofcandidatemodelsandtheirfeatures,and adaptivelyselectsthebetterconfigurationasafunctionofthenetworkandsystemconditions.
Dystonia in complex regional pain syndrome : clinical, pathophysiological and therapeutic aspects

The clinical characteristics of Complex Regional Pain Syndrome (CRPS) are defined by pain and various combinations of sensory disturbances, autonomic features, and sudomotor and trophic changes. Furthermore, patients with CRPS may suffer from movement disorders, of which dystonia is the most prevalent. Dystonia of CRPS can affect multiple extremities, is often resistant to treatment and seems to have a poor prognosis. Reliable information on the nature, chronology and clinical determinants of dystonia in CRPS patients is lacking but could provide better insight in the underlying pathophysiological mechanism. The results of our studies on clinical and neurophysiologic aspects of patients with multiple CRPS and MDs in CRPS provided data that suggest disturbances at multiple levels of the CNS. Maladaptive neural plasticity likely is an important mechanism underpinning the MDs that may occur in CRPS. The presence of disturbances at multiple levels of the central nervous system parallels recent hypotheses on the underlying mechanisms suggested for other types of dystonia. Finally, the study on intrathecal baclofen administration in patients with CRPS and dystonia showed marked improvement of dystonia and pain scores after one year.
Optical Upconversion of OFDM Signals for Next Generation Fronthauling Using Frequency Doubling and Quadrupling

Fronthaul data rates in case of millimeter wave (mmWave) wireless systems scale up to several Gbps in the currently deployed digitized radio over fiber solutions, resulting in large complexity remote radio heads (RRHs). We propose a promising solution involving optical frequency doubling and quadrupling, where all complex digital processing are moved to the centralized base band unit (BBU), thereby allowing placement of large number of low-complex RRHs at long distances. We experimentally demonstrate optical frequency doubling and quadrupling for the generation and transport of millimeter wave mmWave signals with OFDM containing QPSK, 16/64/256-QAM modulation formats over a distance of 2 km. These schemes enable the use of low-frequency RF signals at the BBU for the generation of mmWaves at the RRH. We evaluate the performance of the systems in terms of error vector magnitude (EVM) and out-of-band emissions of the upconverted data. Our results show that the proposed schemes achieve EVM values within the 3GPP 5G standard requirements. We also investigate the tunability and scalability of the proposed schemes across the n258 band (24.25 - 27.5 GHz) and discuss the feasibility of implementing dense wavelength division multiplexing (DWDM) for servicing multi-channel operation required for multisector antennas. The proposed schemes can support high data rate transmission for future wireless networks including 6G.