Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Spiking Neural Network Accelerator with Sparse Weight

Spiking neural network (SNN) is a promising generation of neural networks inspired by biology, which has the advantages of high energy efficiency and hardware-friendly. In SNN, the input is not encoded as real-valued activations but as sequences of binary spikes. The weights are usually stored in the form of crossbars. Due to the unstructured pruning and compression in model training, the weights of the SNN model exhibit spatial sparsity. Recently, many matrix compression algorithms have been applied to on-chip weight storage in SNN accelerators. However, when the stored non-zero values exceed a certain proportion, their storage cost is relatively expensive. In this paper, a sparse compressed spiking neural network accelerator based on bit-mask compression algorithm is presented. Implemented on a FPGA platform, the accelerator can achieve the peak performance of 2.56GSOP/s and the energy efficiency of 1.53GSOP/W.
L∞/ L2 Hankel Norm Analysis and Characterization of Critical Instants for Continuous-Time Linear Periodically Time-Varying Systems

This paper is concerned with the Hankel norm analysis of linear periodically time-varying systems. An arbitrary <tex>$\Theta\in[0,\ h)$</tex> is first taken as the instant separating past and future, where <tex>$h$</tex> denotes the period of such systems, and what is called the quasi <tex>$L_{\infty}/L_{2}$</tex> Hankel norm at <tex>$\Theta$</tex> is defined. Then, a computation method of this norm for each <tex>$\Theta$</tex> is derived. The supremum of the quasi <tex>$L_{\infty}/L_{2}$</tex> Hankel norms over <tex>$\Theta\in[0,\ h)$</tex> is further defined as the <tex>$L_{\infty}/L_{2}$</tex> Hankel norm, and it is also shown that it can be computed directly without dealing with any quasi <tex>$L_{\infty}/L_{2}$</tex> Hankel norms. A relevant question of whether the supremum is attained as the maximum is also studied. In particular, it is discussed when and how the existence/absence of a critical instant attaining the maximum (and all the values of critical instants, if one exists) can be determined without computing all (or any of) the quasi <tex>$L_{\infty}/L_{2}$</tex> Hankel norms over <tex>$\Theta\in[0,\ h)$</tex>.
BFP-Net: Boundary Feature Pyramid for Medical Image Segmentation

In this paper, we propose a novel method, namely boundary feature pyramid network (BFP-Net), which can effectively segment targets with blurred boundaries. Specifically, we first propose a global feature fusion module (GFFM) at the top of BFP-Net to fuse feature maps within different scales. It can learn more global region localization of the target and help the learning of boundaries more effectively. Then, we propose a series of boundary enhancement modules (BEMs) at the decoder to effectively extract and integrate boundary information during the upsampling process, thereby enhancing the ability to capture fine details (such as the boundaries). Furthermore, we introduce a boundary-enhanced composite loss function to effectively segment both the regions and their boundaries within different scales. Finally, extensive experiments on two widely-used datasets demonstrate that BFP-Net is more effective in fusing contextual information and guiding feature map boundaries compared to previous competitive methods.
Cretaceous to early Paleogene A cool temperate climate on the Antarctic Peninsula through the latest

ABSTRACTConstraining past ﬂ uctuations in global temperatures is cen-tral to our understanding of the Earth’s climatic evolution. Marine proxies dominate records of past temperature reconstructions, whereas our understanding of continental climate is relatively poor, particularly in high-latitude areas such as Antarctica. The recently developed MBT/CBT (methylation index of branched tetraethers/cyclization ratio of branched tetraethers) paleothermometer offers an opportunity to quantify ancient continental climates at temporal resolutions typically not afforded by terrestrial macroﬂ oral prox-ies. Here, we have extended the application of the MBT/CBT proxy into the Cretaceous by presenting paleotemperatures through an expanded sedimentary succession from Seymour Island, Antarctica, spanning the latest Maastrichtian and Paleocene. Our data indicate the existence of a relatively stable, persistently cool temperate cli-mate on the Antarctic Peninsula across the Cretaceous-Paleogene boundary. These new data help elucidate the climatic evolution of Antarctica across one of the Earth’s most pronounced biotic reorga-nizations at the Cretaceous-Paleogene boundary, prior to major ice-sheet development in the late Paleogene. Our work emphasizes the likely existence of temporal and/or spatial heterogeneities in climate of the southern high latitudes during the early Paleogene.INTRODUCTION
Few-Shot Radar Target Recognition via Meta-Learning and Integrated Time-Frequency Domain Features

Currently, deep neural network is the main method for radar automatic target recognition. Because of the necessity for an extensive volume of annotated data to optimize model parameters, these deep models face serious risks of overfitting in few-short condition. To address the preceding issue, this paper presents a radar few-short target recognition approach via meta-learning mechanism. Firstly, scattering features from geometrical theory of diffraction (GTD) model are extracted to explore more valuable information of the target. Secondly, high resolution range profile data are fused with GTD features by our proposed learner. Thirdly, the proposed meta-learner can use the priori knowledge learned from one polarization state target recognition to guide the learner to better realize another polarization state target recognition, which means the proposed method can perfectly fuse multi-polarization dataset to enhance target recognition performance. Simulation results demonstrate that our method can achieve higher target recognition accuracy compared with conventional deep learning approaches in few-short condition.
Deep Adaptive Indirect Herding of Multiple Target Agents with Unknown Interaction Dynamics

The herding problem has received growing interest in the robotics and controls community in recent years. In particular, indirect herding is an abstraction for many potential applications in fields such as wildlife management, crowd control, traffic management, and environment cleanups. Existing works in indirect herding, however, have not taken advantage of recent advances in the field of adaptive control, which have allowed for the development of adaptive controllers using Lyapunov-based deep neural networks (Lb-DNNs). These results, however, are only applicable for systems that are directly controlled, and not for indirect control problems such as the herding problem. This paper develops a novel approach to address the indirect herding problem using an Lb-DNN adaptive backstepping design. The Lb-DNN adaptive backstepping controller enables the herding agent to learn the interaction dynamics and adaptively herd the target agents in real-time, using actual interactions during task-execution. A Lyapunov-based switched systems analysis is used to develop sufficient dwell-time conditions which guarantee exponential convergence of all states to an ultimate bound. Simulations are provided to demonstrate the performance of the developed Lb-DNN adaptive backstepping controller.
A dual-channel deep learning approach to continuous prediction of acute kidney injury in the intensive care unit

Acute Kidney Injury (AKI) often occurs in the intensive care units (ICU), where it is associated with high morbidity and mortality. Early and continuous prediction of AKI plays a crucial role in preventing AKI events, providing timely treatment, and reducing mortality. Continuously monitoring the high dimensional vital signs and lab measurements of the patients has always been a challenging task. In this study, we proposed a novel dual-channel deep learning approach named DC-AKI to address this challenge. DC-AKI extracts feature of temporal variables at different granularity, comprehensively and accurately obtains effective information, and finally continuous predicting risks of the patients. One channel of the model calculates corresponding local interactions of the variables with convolution kernels. The other channel uses the gated recurrent units (GRU) network and attention mechanism to obtains global interactions information among the variables. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Sigmoid classifier for prediction. We trained and tested our model on the MIMIC IV dataset, and the AUC ROC value of DC-AKI for predicting the risk of AKI in ICU patients 48 hours in advance was 0.9527, outperforming other methods. And our model was externally validated on the eICU dataset with an AUC ROC of 0.9054. These results demonstrate the robustness and accuracy of the model on different datasets. This finding provides clinicians with an opportunity to identify and manage patients with AKI in a timely manner, thereby improving patient prognosis. The implemented code is available online at https://github.com/BioMedBigDataCenter/DC-AKI.
Geophysical Investigation of Geothermal Potential of the Gilgil Area Nakuru County, Kenya Using Gravity

In this study, gravity survey was used to investigate the geothermal potential field in Gilgil area Nakuru County, Kenya. The ground based CG-5 Autograv gravimeter was used to accurately measure gravity at each field station. A total of 147 gravity stations were established over an area of about 68 km2 and gravity corrections done. The complete bouguer anomaly was computed and a contour map for the study area plotted using surfer 8.0 software. Qualitative interpretation of the map shows gravity highs in the study area which were interpreted as dense bodies within the subsurface. Five profiles along the gravity highs were drawn and oriented in the directions SW-NE, NW-SE and almost N-S. The regional trend of the profiles was subtracted from the observed data yielding the residual anomaly. 2D Euler deconvolution was done on the profile data and revealed subsurface faults and bodies at a depth range of 790m-4331m. Forward modelling of selected profiles using Grav 2DC software revealed presence of dense intrusive bodies on the northern and southern parts of the study area with the density contrast range of 0.25-0.28. These bodies were interpreted as intrusive dykes that have higher density than surrounding rocks. Such intrusive dykes may be geothermal heat sources.
The Rise of Smart Retail: Analysing the Technological Shifts in Singapore's E-commerce Landscape due to COVID-19

Rise of Smart Retail and Online changes of consumers in Singapore, being the Head Quarters of most of the multinational corporations need to analyse, as such changes are possible to be seen in the other countries soon. The major concepts that are involved in this study are consumer attitude and online shopping. Considered relevant to this research on digitalization, the factors that affect online shopping behaviour, online shopping behaviour business model and impact of COVID-19 are studied. This research is grounded on realism, a philosophy based on the principles of positivist and interpretivist research philosophies and ideas crucial to the understanding of the idiosyncrasies of human nature, focusing on reality and the beliefs present in an environment. Research highlights that businesses can provide better products and render better services to existing customers and attract new ones. Since the results suggest there is an increased opportunity online, those who do not have a digital presence yet should consider entering ecommerce, as communication and computing are changing due to COVID-19.
The Wiener Theory of Causal Linear Prediction Is Not Effective

In this paper, it will be shown that the minimum mean square error (MMSE) for predicting a stationary stochas-tic time series from its past observations is not generally Turing computable, even if the spectral density of the stochastic process is differentiable with a computable first derivative. This implies that for any approximation sequence that converges to the MMSE there does not exist an algorithmic stopping criterion that guarantees that the computed approximation is sufficiently close to the true value of the MMSE. Furthermore, it will be shown that under the same conditions on the spectral density, it is also the case that coefficients of the optimal prediction filter are not generally Turing computable.
Different Algorithms (Might) Uncover Different Patterns: A Brain-Age Prediction Case Study

Machine learning is a rapidly evolving field with a wide range of applications, including biological signal analysis, where novel algorithms often improve the state-of-the-art. However, robustness to algorithmic variability - measured by different algorithms, consistently uncovering similar findings - is seldom explored. In this paper we investigate whether established hypotheses in brain-age prediction from EEG research validate across algorithms. First, we surveyed literature and identified various features known to be informative for brain-age prediction. We employed diverse feature extraction techniques, processing steps, and models, and utilized the interpretative power of SHapley Additive exPlanations (SHAP) values to align our findings with the existing research in the field. Few of our models achieved state-of-the-art performance on the specific data-set we utilized. Moreover, analysis demonstrated that while most models do uncover similar patterns in the EEG signals, some variability could still be observed. Finally, a few prominent findings could only be validated using specific models. We conclude by suggesting remedies to the potential implications of this lack of robustness to model variability.
Food technological potentials of CO2 gas hydrate technology for the concentration of selected juices*

ABSTRACT CO2 gas hydrate technology seems to be a gentle way to concentrate juices, especially comparing to evaporation processes which achieve high levels of concentration and is furthermore energetically favorable in contrast to freeze concentration processes. CO2 can form gas hydrates at around 30–80 bar and 274–283 K. For evaluating this new technology, it is not only important to know the phase equilibrium lines of commercial juices like apple and orange juices but also how the application of e.g. a bubble column affects the gas hydrate formation and separation from the concentrated product. In order to support the experimental outcome, numerical modeling seems suitable to understand the physical background of this novel concentration technology. This includes the simulation of temperature, velocity, pressure and concentration fields using finite volume technique. All three work packages combined will lead to a better understanding of the behavior of gas hydrate technology used to concentrate liquid food.
Reachable Set Estimation for Discrete-Time Periodic Piecewise Systems

This paper investigates the reachable set estimation problem for discrete-time periodic piecewise systems subject to bounded-peak disturbances for the first time. Based on the periodic linear-interpolative formulation, the discrete time-scheduling Lyapunov function-s with non-jumping or jumping modes are constructed to develop criteria of reachable set estimation that can ensure the asymptotic stability and reachability of the investigated system. Moreover, an index optimizing the bounding region of the desirable reachable set is given via resorting to the ellipsoid technique, and their results are compared. Finally, numerical examples are given to validate the effectiveness of the proposed results.
The Association of Metabolic Syndrome and Its Components with Electrocardiogram Parameters and Abnormalities Among an Iranian Rural Population: The Fasa PERSIAN Cohort Study

Background: Metabolic syndrome (MetS) as a set of cardiac risk factors and its growing prevalence is one of the major concerns in different societies. In this study, we aimed to investigate the relationship between Mets and electrocardiogram (ECG) parameters and abnormalities as indicators for subclinical cardiovascular diseases (CVD). Methods: In this sub-analysis study, we used the data from Fasa PERSIAN Cohort Study which includes subjects age 35–70 years. Subjects with available ECG data included in the study (n=7002) and subjects with missing data on MetS components and non-sinus rhythm ECG were excluded (n=44). The MetS definition based on the Adult Treatment Panel (ATP) III guidelines and also a 12-lead ECG was obtained from all participants. Results: Our study population (n=6958) showed a mean age of 48.60±9.34 years and also 1656 (24.2%) subjects had MetS. Except for P duration, PR interval and S amplitude in men and P amplitude, S amplitude, Sokolow-Lyon Index, and QT interval in women, other ECG parameters differ significantly between subjects with and without Mets (P<0.05). Also among ECG abnormalities, prolonged P duration (≥120ms), QRS duration (≥100ms), and QTc interval (>450ms in male, >470ms in female) had a significant association with MetS in the total population. Waist circumferences (WC) showed the most count of significant relationship with ECG parameters in both genders. In males, WC more than ATP cut-points had significant associations with prolonged P and QRS duration, and also blood pressure (BP) had significant associations with prolonged P and QRS durations and QTc interval. In females, the MetS component except triglyceride had at least a significant relationship with prolonged P and/or QRS duration. Conclusion: MetS and its component especially WC and BP were associated with ECG parameters and abnormalities. These associations with ECG as a marker of subclinical CVD showed the importance of MetS and each component in our population to monitor in the further longitudinal studies.
The molecular systematics and diversi ﬁ cation of a taxonomically unstable group of Asian cicada tribes related to Cicadini Latreille, 1802 (Hemiptera : Cicadidae)

The cicadas (Hemiptera: Cicadidae) related to tribe Cicadini exhibit some of the most remarkable phenotypes in the family, with many genera possessing striking colour patterns and unusual morphological features. This largely Asian group of 13 tribes has proven challenging for cicada taxonomists, in part because of likely convergent evolution or losses of these phenotypes. We present the ﬁ rst focused molecular phylogeny of this clade, including ~60 described genera. The genetic dataset contains 839 ingroup-informative sites (out of 2575) from mitochondrial cytochrome c oxidase subunit I, nuclear elongation factor-1 a , and nuclear acetyltransferase. We use Bayesian and maximum likelihood trees to test recent changes in tribe-and subtribe-level classi ﬁ cation, and we reconstruct ancestral character states for potentially convergent traits in ﬂ uencing tribe descriptions. We use fossil and molecular clock calibrations to estimate the temporal and geographic context of the radiation. The tribes Gaeanini, Leptopsaltriini, Platypleurini, Psithyristriini, and Tosenini appear polyphyletic and in need of revision, in part because of convergent evolution of opaque wings and multiple convergent gains or losses of abdominal tubercles. Kalabita Moulton, 1923
Liquefaction-Induced Damage in The2010-2011 Christchurch (New Zealand) Earthquakes

A series of strong local earthquakes hit the city of Christchurch (New Zealand) in the period between September 2010 and December 2011. The earthquakes produced strong ground motions, and were very damaging. The magnitude 6.2 22 February 2011 earthquake was particularly devastating causing heavy damage to the city and 185 fatalities. The earthquake caused widespread and severe liquefaction over approximately one third of the city area which arguably was the most severe and extensive liquefaction in native soils on record. This paper presents an overview of the liquefaction-induced damage to the land, buildings and infrastructure caused by the 2010-2011 earthquakes.
Demographic Effects on Prices: Is Aging Deflationary?

Although the Bank of Japan has continued unconventional monetary easing over the years, it is still far from achieving the inflation target set by the central bank. On the back of this, there has been a growing interest in the relationship between demography and inflation, a relationship which conventional macroeconomics has not discussed much. Analyzing Japanese prefectural panel data, this paper examines demographic effects on inflation with linear regression based on the Phillips curve with some demographic variables. The result shows that the aging population has inflationary pressure on prices, while the declining population has deflationary pressure. The aging population also reduces the impact of population change and economic variation on prices, flattening the Phillips curve. As a result, this paper clarifies a multifaceted relationship between demography and inflation, suggesting that demography is not the main cause of deflation and low inflation in Japan.
Creating a reflective community of practice to promote new media integration in the Greek EFL classroom: an online-learning case study

The purpose of this paper is to examine whether an online, reflective community of practice can promote the endorsement of new media pedagogies in the Greek EFL (English as a foreign language) classroom. To this end, a four-month online learning programme addressing the specific needs of nineteen in-service EFL teachers was designed, implemented and evaluated exploring the participating teachers’ digital and reflective practices and attitudes. Moreover, we sought to study whether distance learning training can enhance the community feeling encouraging the exchange of good teaching practices infusing learners’ motivation. The mixed-methods postgraduate research results were promising and revealed the intrinsic motivation of the teachers to learn new web tools, share ideas and pedagogical scenarios within the socio-constructive zeitgeist of foreign language pedagogy, ultimately enhancing their own and their learners’ new literacies and lifelong learning skills. After all, the explosion of online, informal learning outside our schools, and the challenges it holds for our continuous professional development (CPD) is too powerful to ignore.
eCommons@AKU eCommons

Objective: To assess the outcome of endovascular treatment of intracranial aneurysm over 15 years. Methods: The retrospective study was conducted at the at Radiology Department of Aga Khan University Hospital, Karachi, and comprised medical records from April 2003 to April 2018 of patients who received endovascular treatment for intracranial aneurysm. Multiple variables reviewed included demographics, clinical presentation, aneurysm morphology, technique used, technique outcome and clinical outcome. Data was analysed using SPSS 22. Results: Of the 242 patients, 111(45.8%) were males and 131(54.1%) were female. The overall mean age was 46.3+/- 13.543 years (range: 9-78years). Aneurysm size was <5mm in 95(40.4%) patients, 5-10mm in 98(41.7%) and >10mm in 42(17.9%) patients. Aneurysms were located in the anterior communicating artery in 93(38.4%) patients, internal carotid artery 48(19.8%) patients and posterior communicating artery 26(10.7%) patients. Patients with higher initial Hess and Hunt grade were more likely to have higher modified Rankin scale score after treatment (p=0.001). Overall, 222(91.7%) patients were treated successfully. Complications were noted in 37(15.2%) patients and 10(4.0%) patients died. Conclusions: No correlation was found between Hess and Hunt grades and aneurysm severity based on aneurysm size, neck and ruptured/unruptured cases.