Comparing deep learning models for volatility prediction using multivariate data

This study aims at comparing several deep learning-based forecasters in the task of volatility prediction using multivariate data, proceeding from simpler or shallower to deeper and more complex models and compare them to the naive prediction and variations of classical GARCH models. Specifically, the volatility of five assets (i.e., S\&P500, NASDAQ100, gold, silver, and oil) was predicted with the GARCH models, Multi-Layer Perceptrons, recurrent neural networks, Temporal Convolutional Networks, and the Temporal Fusion Transformer. In most cases the Temporal Fusion Transformer followed by variants of Temporal Convolutional Network outperformed classical approaches and shallow networks. These experiments were repeated, and the difference between competing models was shown to be statistically significant, therefore encouraging their use in practice

Macroscopic fundamental diagram with volume-delay relationship: model derivation, empirical validation and invariance property

This paper presents a macroscopic fundamental diagram model with volume-delay relationship (MFD-VD) for road traffic networks, by exploring two new data sources: license plate cameras (LPCs) and road congestion indices (RCIs). We derive a first-order, nonlinear and implicit ordinary differential equation involving the network accumulation (the volume) and average congestion index (the delay), and use empirical data from a 266 km$^2$ urban network to fit an accumulation-based MFD with $R^2>0.9$. The issue of incomplete traffic volume observed by the LPCs is addressed with a theoretical derivation of the observability-invariant property: The ratio of traffic volume to the critical value (corresponding to the peak of the MFD) is independent of the (unknown) proportion of those detected vehicles. Conditions for such a property to hold is discussed in theory and verified empirically. This offers a practical way to estimate the ratio-to-critical-value, which is an important indicator of network saturation and efficiency, by simply working with a finite set of LPCs. The significance of our work is the introduction of two new data sources widely available to study empirical MFDs, as well as the removal of the assumptions of full observability, known detection rates, and spatially uniform sensors, which are typically required in conventional approaches based on loop detector and floating car data.Comment: 31 pages, 17 figure

Which features of postural sway are effective in distinguishing Parkinson's disease from controls? A systematic review

Background Postural sway may be useful as an objective measure of Parkinson's disease (PD). Existing studies have analyzed many different features of sway using different experimental paradigms. We aimed to determine what features have been used to measure sway and then to assess which feature(s) best differentiate PD patients from controls. We also aimed to determine whether any refinements might improve discriminative power and so assist in standardizing experimental conditions and analysis of data.Methods In this systematic review of the literature, effect size (ES) was calculated for every feature reported by each article and then collapsed across articles where appropriate. The influence of clinical medication status, visual state, and sampling rate on ES was also assessed.Results Four hundred and forty-three papers were retrieved. 25 contained enough information for further analysis. The most commonly used features were not the most effective (e.g., PathLength, used 14 times, had ES of 0.47, while TotalEnergy, used only once, had ES of 1.78). Increased sampling rate was associated with increased ES (PathLength ES increased to 1.12 at 100 Hz from 0.40 at 10 Hz). Measurement during "OFF" clinical status was associated with increased ES (PathLength ES was 0.83 OFF compared to 0.21 ON).Conclusions This review identified promising features for analysis of postural sway in PD, recommending a sampling rate of 100 Hz and studying patients when OFF to maximize ES. ES complements statistical significance as it is clinically relevant and is easily compared across experiments. We suggest that machine learning is a promising tool for the future analysis of postural sway in PD

High energy storage density and large strain in Bi(Zn2/3Nb1/3)O3-Doped BiFeO3-BaTiO3 ceramics

High recoverable energy density (Wrec ~ 2.1 J/cm3) was obtained in (0.7-x)BiFeO3-0.3BaTiO3-xBi(Zn2/3Nb1/3)O3 + 0.1wt% Mn2O3 (BF-BT-xBZN, x = 0.05) lead-free ceramics at < 200 kV/cm. Fast discharge speeds (< 0.5 μs), low leakage (~ 10-7 A/cm2) and small temperature variation in Wrec (~ 25% from 23 to 150 °C) confirmed the potential for these BiFeO3 based compositions for use in high energy density capacitors. A core-shell microstructure composed of a BiFeO3-rich core and BaTiO3-rich shell was observed by scanning and transmission electron microscopy which may contribute to the high value of energy density. In addition, for x = 0.005, a large electromechanical strain was observed with Spos = 0.463% and effective d33* ~ 424 pm/V, suggesting that this family of ceramics may also have potential for high strain actuators

Nanofat lysate ameliorates pain and cartilage degradation of osteoarthritis through activation of TGF-β–Smad2/3 signaling of chondrocytes

Introduction: Nanofat is an effective cell therapy for osteoarthritis (OA). However, it has clinical limitations due to its short half-life. We developed Nanofat lysate (NFL) to overcome the defect of Nanofat and explore its anti-OA efficacy and mechanism.Methods: Monoiodoacetate (MIA) was employed to establish rat OA model. For pain assessment, paw withdrawal latency (PWL) and thermal withdrawal latency (TWL) were evaluated. Degeneration of cartilage was observed by histopathological and immunohistochemical examination. Primary chondrocytes were treated with TNF-α to establish the cellular model of OA. MTT, wound healing, and transwell assays were performed to assess effects of NFL on chondrocytes. RNA-seq, qPCR and Western blot assays were conducted to clarify the mechanism of NFL.Results and Discussion: The animal data showed that PWL and TWL values, Mankin’s and OARSI scorings, and the Col2 expression in cartilage were significantly improved in the NFL-treated OA rats. The cellular data showed that NFL significantly improved the proliferation, wound healing, and migration of chondrocytes. The molecular data showed that NFL significantly restored the TNF-α-altered anabolic markers (Sox9, Col2 and ACAN) and catabolic markers (IL6 and Mmp13). The RNA-seq identified that TGF-β-Smad2/3 signaling pathway mediated the efficacy of NFL, which was verified by qPCR and Western blot that NFL significantly restored the abnormal expressions of TGFβR2, phosphorylated-Smad2, phosphorylated-Smad2/3, Col2, Mmp13 and Mmp3. After long-term storage, NFL exerted similar effects as its fresh type, indicating its advantage of storability. In sum, NFL was developed as a new therapeutic approach and its anti-OA efficacy and mechanism that mediated by TGF-β-Smad2/3 signaling was determined for the first time. Besides, the storability of NFL provided a substantial advantage than other living cell-based therapies

The Distribution Pattern of Sediment Archaea Community of the Poyang Lake, the Largest Freshwater Lake in China

Archaea plays an important role in the global geobiochemical circulation of various environments. However, much less is known about the ecological role of archaea in freshwater lake sediments. Thus, investigating the structure and diversity of archaea community is vital to understand the metabolic processes in freshwater lake ecosystems. In this study, sediment physicochemical properties were combined with the results from 16S rRNA clone library-sequencing to examine the sediment archaea diversity and the environmental factors driving the sediment archaea community structures. Seven sites were chosen from Poyang Lake, including two sites from the main lake body and five sites from the inflow river estuaries. Our results revealed high diverse archaea community in the sediment of Poyang Lake, including Bathyarchaeota (45.5%), Euryarchaeota (43.1%), Woesearchaeota (3.6%), Pacearchaeota (1.7%), Thaumarchaeota (1.4%), suspended Lokiarchaeota (0.7%), Aigarchaeota (0.2%), and Unclassified Archaea (3.8%). The archaea community compositions differed among sites, and sediment property had considerable influence on archaea community structures and distribution, especially total organic carbon (TOC) and metal lead (Pb) (p<0.05). This study provides primary profile of sediment archaea distribution in freshwater lakes and helps to deepen our understanding of lake sediment microbes

ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models

This paper introduces ConceptMath, a bilingual (English and Chinese), fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models (LLMs). Unlike traditional benchmarks that evaluate general mathematical reasoning with an average accuracy, ConceptMath systematically organizes math problems under a hierarchy of math concepts, so that mathematical reasoning can be evaluated at different granularity with concept-wise accuracies. Based on our ConcepthMath, we evaluate a broad range of LLMs, and we observe existing LLMs, though achieving high average accuracies on traditional benchmarks, exhibit significant performance variations across different math concepts and may even fail catastrophically on the most basic ones. Besides, we also introduce an efficient fine-tuning strategy to enhance the weaknesses of existing LLMs. Finally, we hope ConceptMath could guide the developers to understand the fine-grained mathematical abilities of their models and facilitate the growth of foundation models.Comment: The benchmark dataset will be released soo