Miracle-dz/UniFAR-FTUs huggingface dataset:
Waveform-Guide Transformation of IMU Measurements for Smartphone-Based Localization

Pedestrian dead reckoning (PDR) is one key localization technique using inertial momentum units (IMU) installed on a smartphone. However, IMU measurements are affected by the user’s unpredictable smartphone-carrying pattern, degrading the resulting localization accuracy and making its practical usage questionable. This article aims to tackle the issue by proposing a novel technique called WAveform GuIded Transformation of IMU measurements (WAIT), which transforms real IMU measurements to error-free waveforms. WAIT is based on a Resnet-based autoencoder architecture that is trained to generate the waveforms no matter what smartphone-carrying pattern the IMU measurement comes in. The resulting consistent waveform can be used as input for various elementary algorithms required for updating a user’s location and pedestrian trajectory, such as step count and heading direction estimation algorithms. Besides, WAIT’s architecture remains unchanged regardless of the number of concerned smartphone-carrying patterns, and there is no performance degradation. The effectiveness of WAIT is well verified by field experiments conducted with three different smartphone-carrying patterns and two users, showing that the average positioning error is reduced from 0.967 to 0.433 (m) compared to benchmark PDR algorithm using a different deep neural network (DNN) model for each smartphone-carrying pattern.
Clock-like Mutation Signature May Be Prognostic for Worse Survival Than Signatures of UV Damage in Cutaneous Melanoma

Simple Summary Although immunotherapy and targeted therapy have dramatically improved melanoma survival, some patients rapidly progress and decease within a few months after a stage IV diagnosis. Up until now, pathological, clinical and biological markers are known prognostic factors for survival of melanoma, while still no prognostic genetic alterations have been identified. Therefore, we aimed to find genetic alterations that predict a short or long survival by sequencing 190 melanoma-related genes of tumor material from 79 patients to contribute to the growing body of knowledge regarding mutational profiling. While no individual gene mutations or combinations of alterations could be linked to overall survival in our study cohort, a clock-like mutational signature according to the Catalog of Somatic Mutations in Cancer (COSMIC) was associated with poor survival whereas a UV mutational signature was prognostic for a longer survival. Those findings are congruent with earlier findings of other authors. Therefore, the prognostic relevance of mutational signatures must be further evaluated in prospective studies. Abstract Novel treatment modalities comprising immune checkpoint inhibitors and targeted therapies have revolutionized treatment of metastatic melanoma. Still, some patients suffer from rapid progression and decease within months after a diagnosis of stage IV melanoma. We aimed to assess whether genomic alterations may predict survival after the development of stage IV disease, irrespective of received therapy. We analyzed tumor samples of 79 patients with stage IV melanoma using a custom next-generation gene-sequencing panel, MelArray, designed to detect alterations in 190 melanoma-relevant genes. We classified the patients: first, as short survivors (survival ≤6 months after stage IV disease, n = 22) and long survivors (survival >6 months, n = 57); second, by using a cut-off of one year; and third, by comparing the longest surviving 20 patients to the shortest surviving 20. Among analyzed genes, no individual gene alterations, or combinations of alterations, could be dichotomously associated with survival. However, the cohort’s mutational profiles closely matched three known mutational signatures curated by the Catalog of Somatic Mutations in Cancer (COSMIC): UV signature COSMIC_7 (cosine-similarity 0.932), clock-like signature COSMIC_5 (cosine-similarity 0.829), and COSMIC_30 (cosine-similarity 0.726). Patients with UV signature had longer survival compared to patients with clock-like and COSMIC 30 (p < 0.0001). Subgroup dichotomization at 6 months showed that 75% of patients with UV signature survived longer than 6 months, and about 75% of patients with clock-like signature survived less than 6 months after development of stage IV disease. In our cohort, clock-like COSMIC_5 mutational signature predicted poor survival while a UV signature COSMIC_7 predicted longer survival. The prognostic value of mutational signatures should be evaluated in prospective studies.
Active disturbance rejection control approach to output-feedback stabilization of MIMO systems with stochastic uncertainty

In this paper, the active disturbance rejection control (ADRC) approach is applied to output-feedback stabilization for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with stochastic inverse dynamics and stochastic disturbance. We first design an extended state observer (ESO) to estimate both unmeasured states and stochastic total disturbance which includes unknown system dynamics, unknown stochastic inverse dynamics, external stochastic disturbance, and uncertainty caused by the deviation of control parameters from their nominal values. The stochastic total disturbance is then compensated in the feedback loop. As a result, we prove theoretically the mean square practical stability for the closed-loop system with constant gain ESO. Some numerical simulations are presented to demonstrate the effectiveness of the proposed output-feedback control scheme.
Integrated Cooperative Synchronization for Wireless Sensor Networks

A precise decentralized clock synchronization method, referred to as integrated cooperative synchronization (ICS) is proposed in this letter. ICS is a delay compensating method that relies on mean-field message passing, where the measurement phase is integrated into the message passing phase. By selecting an extended factorization of the underlying joint a-posteriori distribution of the clock parameters and an appropriate message scheduling for link initialization, ICS is conceptually much simpler than conventional message-passing methods while achieving similar accuracy and reduced computational complexity. Moreover, it has only slightly higher implementation requirements compared to less accurate consensus methods.
A robust PARAFAC method

Modelling higher order arrays of data has gained importance in chemometrics (see e.g. Andersen et al. (2003), Smilde et al. (2004), Tomasi et al. (2005)). Different models exist for this purpose among which PARAFAC (parallel factor analysis) is one of the most important ones. PARAFAC helps understanding the underlying structure of three-way data if these data are approximately trilinear. The algorithm to compute the PARAFAC parameters (see Bro (1998), Smilde et al. (2004)) is based on an alternating least squares procedure, which can not withstand the presence of outliers. Because outliers are frequently common in chemometrics, a robust alternative is necessary. In the literature (see Pravdova et al. (1999), Riu et al. (2003)) some methods have already been investigated, but they can not cope with groups of outliers or with high-dimensional data. We propose a robust PARAFAC version starting with unfolding the three-way array and applying a method for robust principal components analysis.
Effect of physical activity on attention in school-age children with ADHD: a systematic review and meta-analysis of randomized controlled trials

Background: Attention problems are one of the core symptoms of Attention-deficit/hyperactivity disorder (ADHD) in children. Previous studies have shown that physical activity intervention has a positive impact on executive function in children and adolescents with ADHD, but there is limited research on attention problems in school-aged children with ADHD. There are still uncertainties about the appropriate physical activity interventions to improve attention problems in this population. This study conducted a Meta-analysis of randomized controlled trials (RCTs) related to physical activity intervention for attention problems in school-aged children with ADHD, providing a certain reference for precise intervention in attention problems for this population. Methods: We systematically searched the following databases up to October 2022: PubMed, Embase, Web of Science, and Cochrane Library, to identify RCTs that investigated the effects of physical activity interventions on children with ADHD. Two investigators independently conducted literature screening, extraction, and quality assessment. We performed a meta-analysis using Stata 15.1. Results: In total, we included 10 studies in this meta-analysis. The results indicated that physical activity intervention had a moderate effect in improving attention problems in school-aged children with ADHD (SMD = −0.48, 95% CI: 0.85, −0.07, p < 0.05). Furthermore, subgroup analysis showed that the effect of physical activity intervention was moderated by intervention type, frequency, and period, rather than the physical activity environment or single intervention time. Conclusion: Our study suggests that cognitively engaging exercise is more effective in improving attention problems in school-aged children with ADHD. Specifically, when cognitive-engaging exercise is used as the type of physical activity and the intervention frequency is less than 3 times per week, with an intervention period of less than weeks, it is most beneficial for improving attention problems in school-aged children with ADHD. However, we should also consider individual differences in children with respect to their ADHD symptoms and accurately evaluate each child’s specific symptoms before intervention. Systematic Review Registration: identifier (CRD42022363255).
Timbre-constrained recursive time-varying analysis for musical note separation

Note separation in music signal processing becomes difficult when there are overlapping partials from co-existing notes produced by either the same or different musical instruments. In order to deal with this problem, it is necessary to involve certain invariant features of musical instrument sounds into the separation processing. For example, the timbre of a note of a musical instrument may be used as one possible invariant feature. In this paper, a timbre estimate is used to represent this feature such that it becomes a constraint when note separation is performed on a mixture signal. To demonstrate the proposed method, a timedependent recursive regularization analysis is employed. Spectral envelopes of different notes are estimated and a modified parameter update strategy is applied to the recursive regularization process. The experiment results show that the flaws due to the overlapping partial problem can be effectively reduced through the proposed approach.
Real Consequences of Shocks to Intermediaries Supplying Corporate Hedging Instruments

I show that shocks to financial intermediaries that supply hedging instruments to corporations have real effects. I exploit a quasi-natural experiment in South Korea in 2010, where regulations required banks to hold enough capital for taking positions in foreign exchange derivatives (FXD). Using the variation in exposure to this regulation across banks, I find that the regulation caused a reduction in the supply of FXD, leading to a significant decline in exports for firms that held derivatives contracts with more exposed banks. These results indicate the crucial role of intermediaries in allocating risks through the provision of derivatives and establish a causal relationship between financial hedging and real economic outcomes.
EXPERIENCING CRITICAL MATHEMATICS EDUCATION CHANGES IN STUDENTS’ PERSPECTIVES ON THE RELEVANCE OF MATHEMATICS1

When students ponder why they consider it relevant to learn math it becomes apparent that their attributions of relevance do not meet what educational policies state as goals. While they generally consider the subject to be important, their explanations why they do so are limited to only elementary mathematical skills needed in everyday life or unspecified skills for later work life, rarely connecting them to broader societal topics. Integrating Critical Mathematics Education (CME) tasks into math lessons might be a chance to let attributions of relevance evolve further. This contribution shows in how far the students’ perspectives on the relevance of math changed after they experienced several CME units. 16 students from three different schools have participated in two rounds of interviews where they have been asked about their image of math and math learning and if and what they consider relevant. Within the second round of interviews, after they experienced several CME units, additional questions about their experiences in these lessons have been added. The analysis shows that students’ general attribution of relevance did not change considerably. However, effects could be identified in their reflections about the CME units. Students talked about how these units let them experience a connection between math and societal topics that leads to a more personal and direct relevance of math for their lives. However, their understanding of what counts as doing math might hinder them to easily transfer their experiences within these units to their general attributions of relevance of math. If teachers regularly include CME tasks into their math lessons and make reflections on what mathematical activities have been applied to make sense of them an integral part of instruction, CME seems to offer the opportunity for students to broaden their conceptions of math and experience connections between math and societal topics more clearly.
Have exchange-liste d ﬁrms b ecome less important for the economy? ✩

Publicly traded ﬁrms contribute less to total nonfarm employment and GDP now than in the 1970s. Major reasons for this development are the decline of manufacturing, the shift towards more production abroad in manufacturing, and the growth of the service economy as ﬁrms providing services are less likely to be listed on exchanges. A ﬁrm’s stock market capitalization is much less instructive about its employment now than earlier. Market capitalizations have not become systematically less informative about ﬁrms’ contribution to GDP. Listed stock market superstars account for less employment than they did in the 1970s.
Evaluating approaches to high-throughput phenotyping and genotyping for genomic selection in alfalfa Contact information for PI

High-throughput phenotyping and genotyping provide vast information resources to be used in evaluation and selection of breeding materials, but their implementation has been limited in alfalfa due to the complexity of the genetics and the perennial nature of the crop. Indicators of forage growth and development can be measured inexpensively and non-destructively throughout each regrowth cycle using vegetative indices (VI) collected by an unmanned aerial vehicle (UAV) equipped with a multi-spectral camera. By considering VIs as genetically correlated traits to end-use phenotypes such as forage yield and quality, potential exists to reduce the phenotypic load by reducing the number of plots that must be harvested. Random regression models are well suited for ﬁtting longitudinal phenotypes, such as VIs collected through time, and can be implemented to estimate growth curves for each population under evaluation, assess the genetic variation in growth and its relation to end-use products like forage yield and quality. Genetic relationships calculated from genome-wide markers allow for information sharing, better model ﬁtting and the ability to predict un-observed materials. However, calculation of genetic relationships is problematic in alfalfa because varieties are not genetically distinct individuals, but instead synthetic populations. We demonstrate a population-level genotyping approach to estimate the genetic relatedness between alfalfa populations using pairwise F st statistics calculated from sequenced-based allele counts in bulk samples. Using a diallel of historic alfalfa germplasm sources previously evaluated at New Mexico State University, and a panel of Cornell alfalfa currently under ﬁeld evaluation, we demonstrate the eﬃcacy of using genetic relationships between populations to ﬁt genotype-speciﬁc growth curves and predict the genetic merit of varieties and breeding populations.
Monte Carlo simulation-aided design of a thermal neutron generator system from 241 Am-Be isotopic sources

Collimated thermal neutron beams are obtained from neutron extraction channels in nuclear reactors for various applications in research and technology, such as neutron imaging techniques (neutron radiography, neutron radioscopy, neutron tomography, and neutron-based autoradiography). Practical setups for neutron radiography using ion beams from particle accelerators and radioisotopic sources of fast neutrons have been also developed. However, only radioisotopic sources enable autonomous and transportable thermalization systems that can produce thermal neutron collimated beams. This work presents the performance results for a prototype of a compact system that generates a collimated beam of thermal neutrons using low-activity isotopic 241 Am-Be sources. It was designed with the aid of Monte Carlo simulation using the PHITS v 3.17 program. Experimental measurements of the fluence of the neutron beam produced by the built prototype showed good agreement with the simulated values by the Monte Carlo method.
The Relationship Between Leaf Litter and the Biodiversity of Arthropods in the Four Forest Types of the Cloud Forest

A tropical forest is a complex ecosystem, full of interactions between both biotic and abiotic factors. These constant interactions support the liveliness and health of every organism. One cannot survive without the other. This perfectly describes the relationship of the trees and arthropods. Trees absorb nutrients and water from the soil with its roots, as well as add a layer of leaf litter for the insects to live in (Zheng 2017). On the other hand, many of the arthropods are decomposers, essential for recycling the energy of the system by turning dead organisms into inorganic materials available for primary producers. Additionally, arthropods provide water regulation, translocation of organic matter, and soil structure formation (Menta 2020). This symbiosis generates a positive feedback loop between the soil and arthropods, where the soil provides a habitat for the arthropods to thrive in and in return, the decomposers convert leaf litter into nutrient rich soil (Ghiglieno 2020).
A Positive Perspective on Term Representation (Invited Talk)

We use the focused proof system LJF as a framework for describing term structures and substitution. Since the proof theory of LJF does not pick a canonical polarization for primitive types, two different approaches to term representation arise. When primitive types are given the negative polarity, LJF proofs encode terms as tree-like structures in a familiar fashion. In this situation, cut elimination also yields the familiar notion of substitution. On the other hand, when primitive types are given the positive polarity, LJF proofs yield a structure in which explicit sharing of term structures is possible. Such a representation of terms provides an explicit method for sharing term structures. In this setting, cut elimination yields a different notion of substitution. We illustrate these two approaches to term representation by applying them to the encoding of untyped λ -terms. We also exploit concurrency theory techniques – namely traces and simulation – to compare untyped λ -terms using such different structuring disciplines.
Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres

—This paper proposes a method of migrating work-load among geo-distributed data centres that are equipped with on-site renewable energy sources (RES), such as solar and wind energy, to decarbonise data centres. It aims to optimise the performance of such a system by introducing a tunable Reinforcement Learning (RL) based load-balancing algorithm that implements a Neural Network to intelligently migrate workload. By migrating workload within the network of geo-distributed data centres, spatial variations in electricity price and intermittent RES can be capitalised upon to enhance data centres’ operations. The proposed algorithm is evaluated by running simulations using real-world data traces. It is found that the proposed algorithm is able to reduce costs by 6 . 1% whilst also increasing the utilisation of RES by 10 . 7% .
Approximating Optimal Morphing Attacks using Template Inversion

Recent works have demonstrated the feasibility of inverting face recognition systems, enabling to recover convincing face images using only their embeddings. We leverage such template inversion models to develop a novel type of deep morphing attack based on inverting a theoretical optimal morph embedding, which is obtained as an average of the face embeddings of source images. We experiment with two variants of this approach: the first one exploits a fully self-contained embedding-to-image inversion model, while the second leverages the synthesis network of a pre-trained StyleGAN network for increased morph realism. We generate morphing attacks from several source datasets and study the effectiveness of those attacks against several face recognition networks. We showcase that our method can compete with and regularly beat the previous state of the art for deep-learning based morph generation in terms of effectiveness, both in white-box and black-box attack scenarios, and is additionally much faster to run. We hope this might facilitate the development of large scale deep morph datasets for training detection models.
Research on Deep Learning Model of Multimodal Heterogeneous Data Based on LSTM

— To solve the problems of multimodal heterogeneous data fusion and feature learning, a pattern recognition method based on long short-term memory (LSTM) is proposed to improve the classification accuracy. By fusing the deep learning models corresponding to different data types, a shared pattern recognition model is generated based on association analysis. Firstly, classification models for different data type are trained. Long-term memory ability of LSTM is used for the time-dependent data characteristics. Then the fusion strategy is analyzed. And an adaptive determination method for weight fusion is proposed. Finally, the algorithm flow is given, including data preprocessing, model training and heterogeneous data fusing. The experimental results show that the classification accuracy of the proposed method is higher than the models using the single data type alone .