Miracle-dz/UniFAR-FTUs huggingface dataset:
Patterns of early neocortical amyloid beta accumulation: a positron emission tomography population-based study

Introduction: The widespread deposition of amyloid-beta plaques in late-stage Alzheimers disease (AD) is well defined and confirmed by in vivo positron emission tomography (PET). However, there are discrepancies between which regions contribute to the earliest topographical amyloid-beta; deposition within the neocortex. Methods: This study investigated amyloid-beta signals in the perithreshold SUVr range using Pittsburgh compound B (PiB) PET in a population-based study cross-sectionally and longitudinally. PiB-PET scans from 1,088 participants were assessed to determine the early patterns of PiB loading in the neocortex. Results: Early-stage amyloid-beta loading is seen first in the temporal, cingulate, and occipital regions. Regional early deposition patterns are similar in both Apolipoprotein {varepsilon}4 (APOE) carriers and non-carriers. Hierarchical clustering analysis shows groups with different patterns of early amyloid deposition. Discussion: These findings of initial amyloid-beta deposition patterns may be of significance for diagnostics and understanding the development of different AD phenotypes.
A QPO in Mkn 421 from Archival RXTE Data

We report a 325(−7, +8) day quasi-periodic oscillation (QPO) in the X-ray emission of the blazar Mkn 421 based on data obtained with the Rossi X-ray Timing Explorer satellite. The QPO is seen prominently in the ASM data (at least 15 cycles) due to the fact that it has had near-continuous sampling for more than a decade. The PCA data, where the sampling is not uniform and shows many large gaps, provide supporting evidence at lower significance. The QPOs are an important observable in accretion disks, can be modulated by various orbital timescales, and may be generated by a number of mechanisms. They have been studied extensively in X-ray binaries and should be present in active galactic nuclei (AGN) if they are governed by a common set of physical principles. In jetted sources, QPOs can probe jet–disk interactions or helical oscillations. This QPO has previously been claimed intermittently in X-ray, radio, and gamma-ray data, but the continuous, 15 yr extent (1996–2011) of the ASM observations (in which Mkn 421 is the brightest AGN observed) provides a unique window. The QPO appears to be present for nearly the entire extent of the ASM observations. We explore various physical origins and modulating mechanisms, particularly interpretations of the QPO as a result of disk–jet interactions, due to either an accretion disk limit cycle, jet instabilities, or helical motions. Limit cycle oscillations would not interact with either Keplerian or Lense–Thirring modulated oscillations; however, those associated with jet instabilities or helical motions in the jet would likely be modulated by Lense–Thirring precession.
Quick introduction into AdS/CFT correspondence in physics of strongly correlated systems

The AdS/CFT correspondence appeared at the end of the 1990s as an approach established a connection between the string theory and conformal field theory models. In general, the existence of such relation could be expected if both model types have the same origin [1–8]. The evolution of hadron physics models has evolved in different ways over time. It was mandated by the fact that hadrons interact strongly, and in theory, there is no a small parameter on which it would be possible to make an expansion similar to the well-known quantum electrodynamics diagram approach. Initially, string theory was provided with phenomenological foundations in the form of Regge trajectories and conformal field theory got its essential evolution as a consequence of the phase transitions theory development [9]. When coming close to the phase-transition point, the physical value fluctuations have an increasing characteristic length. This fact requires scale invariance, and thus, conformal transformations and conformal theories immediately appear. From this, further development of such an approach has allowed the establishment experimentally testable theory corollaries as well. Note that these theories were divergent from one another increasingly not only in corollaries, but also in mathematical techniques, until a certain consistency between their parameters were established. The basic concept of the AdS/CFT correspondence is in the formulae establishing a correspondence for average values [2, 6, 8, 10–14]: 〈
On-line Dose Calculation Using Deep Learning for Beams Selection in Non-Coplanar Radiotherapy

Non-coplanar Intensity-Modulated Radiation Therapy (IMRT) goes a step further by orienting the gantry carrying the radiation beam and the patient couch in a non-coplanar manner to accurately target the cancer region and better avoid organs-at-risk. The use of a non-coplanar treatment trajectory significantly enhances the degree of freedom and flexibility but increases drastically the complexity of the optimization. In inverse planning optimization the dose contribution for all potential beam directions is usually pre-calculates and pre-loads into the Treatment Planning System (TPS). The size the dose matrix becomes more critical when moving from coplanar IMRT to non-coplanar IMRT since the number of beams increases drastically. A solution would be to calculate"on-the-fly"the dose contribution to each new candidate beam during optimization. This is only possible if a dose calculation engine is fast enough to be used online during optimization iterations, which is not the case in standard method. Therefore, in this work we propose an IMRT optimization scheme using deep learning based dose engine to compute the dose matrix on-line. The proposed deep learning approach will be combined into a simulated-annealing-based optimization method for non-coplanar IMRT. Since the dose engine will compute the dose contribution on-line during the optimization, the final main optimization method requires to keep in memory a very lightweight dose matrix. The proposed method was compared with clinical data showing a good agreement considering dosimetry of the treatment plans. The main advantage of the proposed method was the reduction of the memory storage from 9GB to 10MB during the optimization process.
Revisiting Temporal Blocking Stencil Optimizations

Iterative stencils are used widely across the spectrum of High Performance Computing (HPC) applications. Many efforts have been put into optimizing stencil GPU kernels, given the prevalence of GPU-accelerated supercomputers. To improve the data locality, temporal blocking is an optimization that combines a batch of time steps to process them together. Under the observation that GPUs are evolving to resemble CPUs in some aspects, we revisit temporal blocking optimizations for GPUs. We explore how temporal blocking schemes can be adapted to the new features in the recent Nvidia GPUs, including large scratchpad memory, hardware prefetching, and device-wide synchronization. We propose a novel temporal blocking method, EBISU, which champions low device occupancy to drive aggressive deep temporal blocking on large tiles that are executed tile-by-tile. We compare EBISU with state-of-the-art temporal blocking libraries: STENCILGEN and AN5D. We also compare with state-of-the-art stencil auto-tuning tools that are equipped with temporal blocking optimizations: ARTEMIS and DRSTENCIL. Over a wide range of stencil benchmarks, EBISU achieves speedups up to 2.53x and a geometric mean speedup of 1.49x over the best state-of-the-art performance in each stencil benchmark.
Harvesting Event Schemas from Large Language Models

Event schema provides a conceptual, structural and formal language to represent events and model the world event knowledge. Unfortunately, it is challenging to automatically induce high-quality and high-coverage event schemas due to the open nature of real-world events, the diversity of event expressions, and the sparsity of event knowledge. In this paper, we propose a new paradigm for event schema induction -- knowledge harvesting from large-scale pre-trained language models, which can effectively resolve the above challenges by discovering, conceptualizing and structuralizing event schemas from PLMs. And an Event Schema Harvester (ESHer) is designed to automatically induce high-quality event schemas via in-context generation-based conceptualization, confidence-aware schema structuralization and graph-based schema aggregation. Empirical results show that ESHer can induce high-quality and high-coverage event schemas on varying domains.
A Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information

Existing question answering methods often assume that the input content (e.g., documents or videos) is always accessible to solve the task. Alternatively, memory networks were introduced to mimic the human process of incremental comprehension and compression of the information in a fixed-capacity memory. However, these models only learn how to maintain memory by backpropagating errors in the answers through the entire network. Instead, it has been suggested that humans have effective mechanisms to boost their memorization capacities, such as rehearsal and anticipation. Drawing inspiration from these, we propose a memory model that performs rehearsal and anticipation while processing inputs to memorize important information for solving question answering tasks from streaming data. The proposed mechanisms are applied self-supervised during training through masked modeling tasks focused on coreference information. We validate our model on a short-sequence (bAbI) dataset as well as large-sequence textual (NarrativeQA) and video (ActivityNet-QA) question answering datasets, where it achieves substantial improvements over previous memory network approaches. Furthermore, our ablation study confirms the proposed mechanisms' importance for memory models.
PRIME: A Price-Reverting Impact Model of a cryptocurrency Exchange

In a financial exchange, market impact is a measure of the price change of an asset following a transaction. This is an important element of market microstructure, which determines the behaviour of the market following a trade. In this paper, we first provide a discussion on the market impact observed in the BTC/USD Futures market, then we present a novel multi-agent market simulation that can follow an underlying price series, whilst maintaining the ability to reproduce the market impact observed in the market in an explainable manner. This simulation of the financial exchange allows the model to interact realistically with market participants, helping its users better estimate market slippage as well as the knock-on consequences of their market actions. In turn, it allows various stakeholders such as industrial practitioners, governments and regulators to test their market hypotheses, without deploying capital or destabilising the system.
Image Data Local Compression Research Based on Wavelet Transform

The wavelet transform with multi-resolution characteristic in time and frequency domain is an important method on image compression. it provides image compressed both high image quality and high compression ratio. For meeting the increasing requirements in image visual and data compression, Some wavelet coefficients from multi-layers decomposition must be extracted and preprocessed by multi-scale at different frequency bands, Main feature information such as the edge part and mutation point of image signal should be extracted effectively, a large number of redundant information can be removed. The compression process will be stop until the image signal can be reconstructed approximately from these feature information. If fewer wavelet coefficient images can restore image well, the feature extraction and data compression are realized. The simulation shows that wavelet transform is fully competent for image data compression. Keywords-data compression; feature extraction; wavelet transform; image decomposition
Vision and Control for Grasping Clear Plastic Bags

We develop two novel vision methods for planning effective grasps for clear plastic bags, as well as a control method to enable a Sawyer arm with a parallel gripper to execute the grasps. The first vision method is based on classical image processing and heuristics (e.g., Canny edge detection) to select a grasp target and angle. The second uses a deep-learning model trained on a human-labeled data set to mimic human grasp decisions. A clustering algorithm is used to de-noise the outputs of each vision method. Subsequently, a workspace PD control method is used to execute each grasp. Of the two vision methods, we find the deep-learning based method to be more effective.
Hawkes Process Based on Controlled Differential Equations

Hawkes processes are a popular framework to model the occurrence of sequential events, i.e., occurrence dynamics, in several fields such as social diffusion. In real-world scenarios, the inter-arrival time among events is irregular. However, existing neural network-based Hawkes process models not only i) fail to capture such complicated irregular dynamics, but also ii) resort to heuristics to calculate the log-likelihood of events since they are mostly based on neural networks designed for regular discrete inputs. To this end, we present the concept of Hawkes process based on controlled differential equations (HP-CDE), by adopting the neural controlled differential equation (neural CDE) technology which is an analogue to continuous RNNs. Since HP-CDE continuously reads data, i) irregular time-series datasets can be properly treated preserving their uneven temporal spaces, and ii) the log-likelihood can be exactly computed. Moreover, as both Hawkes processes and neural CDEs are first developed to model complicated human behavioral dynamics, neural CDE-based Hawkes processes are successful in modeling such occurrence dynamics. In our experiments with 4 real-world datasets, our method outperforms existing methods by non-trivial margins.
The boundary of the gravitational standard-model extension

A modification of General Relativity that is based on the gravitational Standard-Model Extension and incorporates nondynamical background fields has recently been studied via the ADM formalism. Our objective in this paper is to develop a better understanding of the additional contributions that arise on the spacetime boundary $\partial M$. An extension of the previously introduced boundary terms, which are relevant in the context of asymptotically flat spacetimes, follows from the decomposition of $\partial M$ into timelike and spacelike hypersurfaces. Furthermore, we present an alternative method of deriving the field equations satisfied by the induced metric on the purely spacelike hypersurfaces of the foliated spacetime. This leads to the dynamical part of the Einstein equations modified by the background fields. Our results have the potential to be applicable in various contexts such as modified black holes and cosmology.
Aligning the Western Balkans power sectors with the European Green Deal

Located in Southern Europe, the Drina River Basin is shared between three countries: Bosnia and Herzegovina, Montenegro, and Serbia. The power sectors of the three countries have a particularly high dependence on coal for power generation. In this paper we analyse different development pathways for achieving climate neutrality in these countries and explore the potential of variable renewable energy in the area, and its role in the decarbonization of the power sector. We investigate the possibility of whether hydro and non-hydro renewables can enable a net zero transition by 2050, and how renewable energy might affect the hydropower cascade shared by the three countries. The Open-Source Energy Modelling System (OSeMOSYS) was used to develop a model representation of the power sector of the countries. The findings of this analysis show that the renewable potential of the countries is a significant 94.4 GW. This potential is 68% to 287% higher than that of previous assessments, depending on the study of comparison. By 2050, 17% of this potential is utilized for VRE capacity additions under an Emission Limit scenario assuming net-zero. These findings suggest that the local VRE potential is sufficient to support the transition to net-zero. Scenarios with higher shares of solar and thermal power show increased power generation from the hydropower cascade, thus reducing the water available for purposes other than power generation.
Learning Quadruped Locomotion using Bio-Inspired Neural Networks with Intrinsic Rhythmicity

Biological studies reveal that neural circuits located at the spinal cord called central pattern generator (CPG) oscillates and generates rhythmic signals, which are the underlying mechanism responsible for rhythmic locomotion behaviors of animals. Inspired by CPG's capability to naturally generate rhythmic patterns, researchers have attempted to create mathematical models of CPG and utilize them for the locomotion of legged robots. In this paper, we propose a network architecture that incorporates CPGs for rhythmic pattern generation and a multi-layer perceptron (MLP) network for sensory feedback. We also proposed a method that reformulates CPGs into a fully-differentiable stateless network, allowing CPGs and MLP to be jointly trained with gradient-based learning. The results show that our proposed method learned agile and dynamic locomotion policies which are capable of blind traversal over uneven terrain and resist external pushes. Simulation results also show that the learned policies are capable of self-modulating step frequency and step length to adapt to the locomotion velocity.
Homotopy types of Vietoris-Rips complexes of Hypercube Graphs

We describe the homotopy types of Vietoris-Rips complexes of hypercube graphs at scale $3$. We represent the vertices in the hypercube graph $Q_m$ as the collection of all subsets of $[m]=\{1, 2, \ldots, m\}$ and equip $Q_m$ with the metric using symmetric difference distance. It is proved in \cite{AA22} that the Vietoris-Rips complexes of hypercube graphs $Q_m$ at scale $2$, $\mathcal{VR}(Q_m; 2)$, is homotopy equivalent to $c_m$-many spheres with dimension $3$ where $c_m=\sum_{0\leq j<i<m} (j+1)(2^{m-2}-2^{i-1})$. Questions are raised in \cite{AA22} for determining the homotopy types of $\mathcal{VR}(Q_m, r)$ with large scales $r=3, 4, \ldots, m-2$. We prove that for $m\geq 5$, $$\mathcal{VR}(Q_m; 3)\simeq (\bigvee_{2^{m-4}\cdot{m\choose 4}} S^7) \vee (\bigvee_{\sum_{i=4}^{m-1}2^{i-4}\cdot{i\choose 4}} S^4). $$
Accelerating Java Ray Tracing Applications on Heterogeneous Hardware

Ray tracing has been typically known as a graphics rendering method capable of producing highly realistic imagery and visual effects generated by computers. More recently the performance improvements in Graphics Processing Units (GPUs) have enabled developers to exploit sufficient computing power to build a fair amount of ray tracing applications with the ability to run in real-time. Typically, real-time ray tracing is achieved by utilizing high performance kernels written in CUDA, OpenCL, and Vulkan which can be invoked by high-level languages via native bindings; a technique that fragments application code bases as well as limits portability. This paper presents a hardware-accelerated ray tracing rendering engine, fully written in Java, that can seamlessly harness the performance of underlying GPUs via the TornadoVM framework. Through this paper, we show the potential of Java and acceleration frameworks to process in real time a compute intensive application. Our results indicate that it is possible to enable real time ray tracing from Java by achieving up to 234, 152, 45 frames-per-second in 720p, 1080p, and 4K resolutions, respectively.
Automated Smell Detection and Recommendation in Natural Language Requirements

Requirement specifications are typically written in natural language (NL) due to its usability across multiple domains and understandability by all stakeholders. However, unstructured NL is prone to quality problems (e.g., ambiguity) when writing requirements, which can result in project failures. To address this issue, we present a tool, named Paska, that takes as input any NL requirements, automatically detects quality problems as smells in the requirements, and offers recommendations to improve their quality. Our approach relies on natural language processing (NLP) techniques and a state-of-the-art controlled natural language (CNL) for requirements (Rimay), to detect smells and suggest recommendations using patterns defined in Rimay to improve requirement quality. We evaluated Paska through an industrial case study in the financial domain involving 13 systems and 2725 annotated requirements. The results show that our tool is accurate in detecting smells (89% precision and recall) and suggesting appropriate Rimay pattern recommendations (96% precision and 94% recall).
BlendFields: Few-Shot Example-Driven Facial Modeling

Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods are either data-driven, requiring an extensive corpus of data not publicly accessible to the research community, or fail to capture fine details because they rely on geometric face models that cannot represent fine-grained details in texture with a mesh discretization and linear deformation designed to model only a coarse face geometry. We introduce a method that bridges this gap by drawing inspiration from traditional computer graphics techniques. Unseen expressions are modeled by blending appearance from a sparse set of extreme poses. This blending is performed by measuring local volumetric changes in those expressions and locally reproducing their appearance whenever a similar expression is performed at test time. We show that our method generalizes to unseen expressions, adding fine-grained effects on top of smooth volumetric deformations of a face, and demonstrate how it generalizes beyond faces.
A Lightweight Authentication Protocol Against Modeling Attacks Based on a Novel LFSR-APUF

Simple authentication protocols based on conventional physical unclonable functions (PUFs) are vulnerable to modeling attacks and other security threats. This article proposes an arbiter PUF based on a linear feedback shift register (LFSR-APUF). Different from the previously reported linear feedback shift register (LFSR) for challenge extension, the proposed scheme feeds the external random challenges into the LFSR module to obfuscate the linear mapping relationship between the challenge and response. It can prevent attackers from obtaining valid challenge–response pairs (CRPs), increasing its resistance to modeling attacks significantly. A 64-stage LFSR-APUF has been implemented on a field programmable gate array (FPGA) board. The experimental results reveal that the proposed design can effectively resist various modeling attacks, such as logistic regression (LR), evolutionary strategy (ES), artificial neuro network (ANN), and support vector machine (SVM) with a prediction rate of 51.79% and a slight effect on the randomness, reliability, and uniqueness. Further, a lightweight authentication protocol is established based on the proposed LFSR-APUF. The protocol incorporates a low-overhead, ultralightweight, novel private bit conversion Cover function that is uniquely bound to each device in the authentication network. The proposed authentication protocol not only resists spoofing attacks, physical attacks, and modeling attacks effectively but also ensures the security of the entire authentication network by transferring important information in encrypted form from the server to the database even when the attacker completely controls the server.