Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Interpretable Neural Network-Based Rule Model for Healthcare Decision Making

In healthcare applications, understanding how machine/deep learning models make decisions is crucial. In this study, we introduce a neural network framework, $\textit{Truth Table rules}$ (TT-rules), that combines the global and exact interpretability properties of rule-based models with the high performance of deep neural networks. TT-rules is built upon $\textit{Truth Table nets}$ (TTnet), a family of deep neural networks initially developed for formal verification. By extracting the necessary and sufficient rules $\mathcal{R}$ from the trained TTnet model (global interpretability) to yield the same output as the TTnet (exact interpretability), TT-rules effectively transforms the neural network into a rule-based model. This rule-based model supports binary classification, multi-label classification, and regression tasks for small to large tabular datasets. After outlining the framework, we evaluate TT-rules' performance on healthcare applications and compare it to state-of-the-art rule-based methods. Our results demonstrate that TT-rules achieves equal or higher performance compared to other interpretable methods. Notably, TT-rules presents the first accurate rule-based model capable of fitting large tabular datasets, including two real-life DNA datasets with over 20K features.
A New Chengjiang Worm Sheds Light on the Radiation and Disparity in Early Priapulida

Simple Summary Priapulida form a small relict group of marine invertebrates characterized by a vermiform shape, an annulated trunk, and an eversible anterior part. Worms with a comparable body plan were diverse and abundant throughout the Cambrian Era, although uncertainties remain concerning their relation to modern priapulids. The problem lies in the lack of morphological detail (ornament, symmetry) available from Cambrian worms and the fact that a comprehensive phylogeny of Cambrian worms with a robust homology framework is not available to define priapulids. The exceptionally preserved worm Ercaivermis sparios, described here from the early Cambrian of China, displays an unusual octagonal symmetry, suggesting that different symmetry types may have co-existed in the early history of Priapulida, before five-fold symmetry was naturally selected to become overwhelmingly dominant. Abstract The vast majority of early Paleozoic ecdysozoan worms are often resolved as stem-group Priapulida based on resemblances with the rare modern representatives of the group, such as the structure of the introvert and the number and distribution of scalids (a spiny cuticular outgrowth) and pharyngeal teeth. In Priapulida, both scalids and teeth create symmetry patterns, and three major diagnostic features are generally used to define the group: 25 longitudinal rows of scalids (five-fold symmetry), 8 scalids around the first introvert circle and the pentagonal arrangement of pharyngeal teeth. Here we describe Ercaivermis sparios gen. et sp. nov., a new priapulid from the early Cambrian Chengjiang Lagerstätte, characterized by an annulated trunk lacking a sclerotized ornament, four pairs of anal hooks and 16 longitudinal rows of scalids along its introvert and eight scalids around each introvert circle, giving the animal an unusual octoradial symmetry. Cladistic analyses resolve Ercaivermis as a stem-group priapulid. Ercaivermis also suggests that several biradial symmetry patterns (e.g., pentagonal, octagonal) expressed in the cuticular ornament, may have co-existed among early Cambrian priapulids and that the pentaradial mode may have become rapidly dominant during the course of evolution, possibly via the standardization of patterning, i.e., the natural selection of one symmetry type over others.
Alpha and theta band activity share information relevant to proactive and reactive control during conflict‐modulated response inhibition

Response inhibition is an important instance of cognitive control and can be complicated by perceptual conflict. The neurophysiological mechanisms underlying these processes are still not understood. Especially the relationship between neural processes directly preceding cognitive control (proactive control) and processes underlying cognitive control (reactive control) has not been examined although there should be close links. In the current study, we investigate these aspects in a sample of N = 50 healthy adults. Time‐frequency and beamforming approaches were applied to analyze the interrelation of brain states before (pre‐trial) and during (within‐trial) cognitive control. The behavioral data replicate a perceptual conflict‐dependent modulation of response inhibition. During the pre‐trial period, insular, inferior frontal, superior temporal, and precentral alpha activity was positively correlated with theta activity in the same regions and the superior frontal gyrus. Additionally, participants with a stronger pre‐trial alpha activity in the primary motor cortex showed a stronger (within‐trial) conflict effect in the theta band in the primary motor cortex. This theta conflict effect was further related to a stronger theta conflict effect in the midcingulate cortex until the end of the trial. The temporal cascade of these processes suggests that successful proactive preparation (anticipatory information gating) entails a stronger reactive processing of the conflicting stimulus information likely resulting in a realization of the need to adapt the current action plan. The results indicate that theta and alpha band activity share and transfer aspects of information when it comes to the interrelationship between proactive and reactive control during conflict‐modulated motor inhibition.
The Challenges of Implementing Online-Based Leadership in The Application of Education Innovations

The purpose of this study was to find out the challenges of online-based leadership implementation in post-pandemic educational innovation applications at Public Aliyah Madrasah (MAN). This study uses a qualitative approach with purposive and snowball sampling techniques. The findings show that: The challenge in improving the quality and competitiveness of online-based schools after the pandemic is the need to develop superior human resources to prepare the community and nation to face the knowledge age as a competitive era. The challenge in Human Resources (HR) and online-based finance after the pandemic is to build a new structure that improves teacher coordination and collaboration that utilizes the expertise of all educators, ultimately leading to better working conditions and workplaces; As for the challenges in improving the quality of online-based leadership post-pandemic, namely leadership that is disruptive to the current progress and creative in terms of policies which include the stages of preparation, enlightenment, elaboration, and verification.
NDOB-Based Composite Sliding Mode Control for Translational Mechanism Position Servo System

This paper proposes a novel non-singular fast terminal sliding mode (NFTSM) controller containing nonlinear disturbance observer (NDOB) and robust exact differentiator (RED) for the translational mechanism position servo system. In order to handle the problems of poor robustness and long convergence time of PI controller for the translational mechanism position servo system, we designed a NFTSM controller using the backstepping principle. Considering the presence of measurement noise in grating encoders in practical applications, we then use a RED to estimate the velocity instead of backward differentiators. Next, to suppress external load disturbances, a NDOB is designed to compensate for the disturbance. It improves the robustness of the system and shortens the convergence time. Finally, we verify the effectiveness of the designed control method through stability analysis and simulation, and provide ideas for future experiments.
Methods of Analysis of Amazon Product Reviews and Rating Prediction

: Online shopping reviews have become an important data source for merchants to make smarter decisions in product development, operations, and marketing. In this paper, we propose a modeling strategy to optimize data analysis and processing of online shopping review data. We address four main problems: identifying commonly used words in positive, negative, and helpful reviews, predicting the products to which the comments refer using semantic analysis, predicting the product rating based on the comments using sentiment analysis, and proposing ways to distinguish human comments from machine-generated ones. Additionally, we provide a recommendation letter to customers on how to read product reviews.
3D Face Reconstruction: The Road to Forensics

3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bringing evidence to a lawsuit unclear. An extensive investigation of the constraints, potential, and limits of its application in forensics is still missing. Shedding some light on this matter is the goal of the present survey, which starts by clarifying the relation between forensic applications and biometrics, with a focus on face recognition. Therefore, it provides an analysis of the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discusses the current obstacles that separate 3D face reconstruction from an active role in forensic applications. Finally, it examines the underlying datasets, with their advantages and limitations, while proposing alternatives that could substitute or complement them.
DeepliteRT: Computer Vision at the Edge

The proliferation of edge devices has unlocked unprecedented opportunities for deep learning model deployment in computer vision applications. However, these complex models require considerable power, memory and compute resources that are typically not available on edge platforms. Ultra low-bit quantization presents an attractive solution to this problem by scaling down the model weights and activations from 32-bit to less than 8-bit. We implement highly optimized ultra low-bit convolution operators for ARM-based targets that outperform existing methods by up to 4.34x. Our operator is implemented within Deeplite Runtime (DeepliteRT), an end-to-end solution for the compilation, tuning, and inference of ultra low-bit models on ARM devices. Compiler passes in DeepliteRT automatically convert a fake-quantized model in full precision to a compact ultra low-bit representation, easing the process of quantized model deployment on commodity hardware. We analyze the performance of DeepliteRT on classification and detection models against optimized 32-bit floating-point, 8-bit integer, and 2-bit baselines, achieving significant speedups of up to 2.20x, 2.33x and 2.17x, respectively.
MFF-TrackNet: 6D Pose Tracking Network via Multi-Attention Feature Fusion

Tracking 6D pose of objects in video sequences is an important task for robot manipulation and augmented reality. Current neural network architecture for 6D pose tracking is mainly composed of two feature extraction branches, one is for the current observation, the other is for the rendered image acquired from the earlier pose estimation result. 6D pose transformation is obtained by comparing the difference between the features extracted by the two branches. However, the weights of the two branches are not shared, so that the two feature extraction processes are not related. Given the fact that the extracted features must be concatenated together in the decoder part of the network to predict pose transformation parameters, in this work, we try to establish the feature interaction mechanism early in the feature extraction process. Two modules based on multi-attention mechanism is designed to connect the two feature extraction branches in order to fuse low, medium, high level features. Experimental results show that the proposed neural network has outperformed the state-of-the-art 6D pose tracking methods. It can steadily track 6D pose variation with no need for reinitialization even under inaccurate initialization condition. Meanwhile, the proposed network can robustly handle challenging situations such as abrupt motions, extreme rotations, or slippage within the end-effector during the application in robotic manipulation.
Does Quartessence Ease Tensions

Tensions between cosmic microwave background observations and the growth of the large-scale structure inferred from late-time probes pose a serious challenge to the concordance $\Lambda$CDM cosmological model. State-of-the-art data from the Planck satellite predicts a higher rate of structure growth than what preferred by low-redshift observables. Such tension has hitherto eluded conclusive explanations in terms of straightforward modifications to $\Lambda$CDM, e.g. the inclusion of massive neutrinos or a dynamical dark energy component. Here, we investigate models of 'quartessence' -- a single dark component mimicking both dark matter and dark energy -- whose non-vanishing sound speed inhibits structure growth at late times on scales smaller than its corresponding Jeans' length. In principle, this could reconcile high- and low-redshift observations. We put this hypothesis to test against temperature and polarisation spectra from the latest Planck release, SDSS DR12 measurements of baryon acoustic oscillations and redshift-space distortions, and cosmic shear correlation functions from KiDS. This the first time that any specific model of quartessence is applied to actual data. We show that, if we naively apply $\Lambda$CDM nonlinear prescription to quartessence, the combined data sets allow for tight constraints on the model parameters. Apparently, quartessence alleviates the tension between the total matter fraction and late-time structure clustering, although in fact the tension is transferred from the latter to the quartessence sound speed parameter. However, we found that this strongly depends upon information from nonlinear scales. Indeed, if we relax this assumption, quartessence models appear still viable. For this reason, we argue that the nonlinear behaviour of quartessence deserves further investigation and may lead to a deeper understanding of the physics of the dark Universe.
Shallow and deep learning of extreme rainfall events from convective atmospheres

Abstract. Our subject is a new catalogue of radar-based heavy rainfall events (CatRaRE) over Germany and how it relates to the concurrent atmospheric circulation. We classify daily ERA5 fields of convective indices according to CatRaRE, using an array of 13 statistical methods, consisting of 4 conventional (“shallow”) and 9 more recent deep machine learning (DL) algorithms; the classifiers are then applied to corresponding fields of simulated present and future atmospheres from the Coordinated Regional Climate Downscaling Experiment (CORDEX) project. The inherent uncertainty of the DL results from the stochastic nature of their optimization is addressed by employing an ensemble approach using 20 runs for each network. The shallow random forest method performs best with an equitable threat score (ETS) around 0.52, followed by the DL networks ALL-CNN and ResNet with an ETS near 0.48. Their success can be understood as a result of conceptual simplicity and parametric parsimony, which obviously best fits the relatively simple classification task. It is found that, on summer days, CatRaRE-convective atmospheres over Germany occur with a probability of about 0.5. This probability is projected to increase, regardless of method, both in ERA5-reanalyzed and CORDEX-simulated atmospheres: for the historical period we find a centennial increase of about 0.2 and for the future period one of slightly below 0.1.
DRDE: Dual Run Distribution Based Encoding Scheme for Sustainable IoT Applications

Nowadays, data is ubiquitous and has a significant influence on our day-to-day activities due to the emergence of high speed internet and widespread use of sensor-enabled Internet of Things (IoT) devices. Owing to major improvement in sampling rate of sensors in recent years, a low variation of the sensed physical parameters is witnessed during a small time interval of observation, which in turn shows high correlation in time domain. Data-critical applications like personal healthcare monitoring, video surveillance, and other applications where data dropping creates significant barriers are attracted by high correlation data. However, due to their power-constrained nature, such applications do not benefit much from the transmission of redundant data. The ideal solution to this problem could possibly be achieved by adopting typical lossless source coding strategy with low-complex design. This paper presents a novel encoding scheme termed as Dual Run Distribution based Encoding (DRDE) scheme by exploiting high correlation of sensor data to suitably encode them using symbol run statistics, leading to a reduced length of data with a very large percentage of 0s. Employing silent symbol based communication, the transmitter can be kept in silent state during periods of the most dominant symbol ’0’ in the encoded messages and using a hybrid $FSK-ASK$ modulation/demodulation technique for communication with a non-coherent receiver results in a significant reduction in transmitter energy. We simulate the proposed sensor data encoding technique on real-life data with low-cost, low data-rate transceivers like CC2420. Simulation results show about 88% (theoretical) and 79-82% (practical) savings in transmitter and 12% (theoretical) 23.5% (practical) savings in receiver energy over conventional BFSK with real-life sensor dataset. Furthermore, our proposed method outperforms in terms of overall energy savings and reduction of $CO_{2}$ footprint, generating 1.48 – 0.041 mg/day, which is 78% lesser than conventional BFSK modulation scheme, making our proposed scheme suitable for sustainable IoT applications in WSN compared to existing schemes. Furthermore, we investigate the influence of various data compression algorithms on computation time, CPU power consumption, and transmission cost on an LPC2148 microcontroller built upon a 16-bit/32-bit ARM7TDMI chipset.
The effects of carvacrol and p‐cymene on Aβ1‐42‐induced long‐term potentiation deficit in male rats

Abstract Aims Alzheimer's disease (AD) is the most common type of dementia in which oxidative stress plays an important role. In this disease, learning and memory and the cellular mechanism associated with it, long‐term potentiation (LTP), are impaired. Considering the beneficial effects of carvacrol (CAR) and p‐cymene against AD, their effect was assessed on in vivo hippocampal LTP in the perforant pathway (PP)‐dentate gyrus (DG) pathway in an Aβ1‐42‐induced rat model of AD. Methods Male Wistar rats were randomly assigned to five groups: sham: intracerebroventricular (ICV) injection of phosphate‐buffered saline, Aβ: ICV Aβ1‐42 injections, Aβ + CAR (50 mg/kg), Aβ + p‐cymene (50 mg/kg), and Aβ + CAR + p‐cymene. Administration of CAR and p‐cymene was done by gavage daily 4 weeks before and 4 weeks after the Aβ injection. The population spike (PS) amplitude and field excitatory postsynaptic potentials (fEPSP) slope were determined in DG against the applied stimulation to the PP. Results Aβ‐treated rats exhibited impaired LTP induction in the PP‐DG synapses, resulting in significant reduction in both fEPSP slope and PS amplitude compared to the sham animals. Aβ‐treated rats consumed either CAR or p‐cymene separately (but not their combination), and showed an enhancement in fEPSP slope and PS amplitude of the DG granular cells. Conclusions These data indicate that CAR or p‐cymene can ameliorate Aβ‐associated changes in synaptic plasticity. Surprisingly, the combination of CAR and p‐cymene did not yield the same effect, suggesting a potential interaction between the two substances.
Study of land use simulation on the condition of ecological environment protection - a case research in Dianchi basin in Kunming.

Land use simulation is an important methodology to study land use change.Ecological environment protection is a major constraint of land use.In this paper,we built a multi-factor comprehensive decision model to analysis ecological environment.The result was imported into SLEUTH model to constrain land use change,therefore,realized a loose couple of urban growth model and ecological environment analysis and successfully simulated urban expansion on the condition of effective ecological environment protection.The model was adopted to simulate land-use in Dianchi basin.The simulated result and the actual land-use in 2005 were compared.It indicated that current land use plan had taken ecological environment protection into account,although unreasonable land use still remained.The simulated result proposed a relatively new foundation to evaluate the existing land use situation and assisted future land use planning.
Analysis of interevent times by methods of statistical physics

The problem of excessive losses is significant in economic theory and investment practice, as well as for random processes analysis. One of the recently discovered characteristics of losses and profits on financial markets, which was discovered by Bogachev, Bunde, Ludescher, and Tsallis and is not entirely explained yet, is the universality of the distribution of times between losses (or profits) exceeding a given threshold value. In other words, this distribution does not depend on the underlying asset type or the time resolution of data, but rather only depends on the particular threshold height. Interestingly, similar results were obtained, e.g., in geophysical time series (describing the earthquakes exceeding a particular magnitude). In this thesis I present a thorough description of this universality, employing two complementary approaches: (i) an analytical approach, based on the extreme value theory (EVT) and the continuous-time random walk (CTRW) model, and (ii) a numerical approach, based on the Potts model from statistical mechanics. The thesis makes original contributions to the field of knowledge with the following: (i) an analytical model of the aforementioned universality, with a thorough empirical verification for losses and some proposed applications to valueat-risk simulation, profits description, and geophysical data description, and (ii) an agent-based spin model of financial markets with the novel interpretation of the spin variable (as regards financial-market models), reproducing the main empirical stylized facts, such as the shape of a usual and an absolute-value autocorrelation function of the returns as well as the distribution of times between superthreshold losses. These results extend the knowledge and understanding of stochastic processes, agent-based modeling, financial markets and geophysical systems.