Miracle-dz/UniFAR-FTUs huggingface dataset:
Stationary Dynamic Stress Solutions for a Rectangular Load Applied within a 3D Viscoelastic Isotropic Full-Space

This paper presents stress influence functions for uniformly distributed, time-harmonic rectangular loads within a three-dimensional, viscoelastic, isotropic full-space. The coupled differential equations relating displacements and stresses in the full-space are solved through double Fourier integral transforms in the wave number domain, in which they can be solved algebraically. The final stress fields are expressed in terms of double indefinite integrals arising from the Fourier transforms. The paper presents numerical schemes with which to integrate these functions accurately. The article presents numerical validation of the synthesized stress kernels and their behavior for high frequencies and large distances from the excitation source. The influence of damping ratio on the dynamic results is also investigated. This article is complementary to previous results of the authors in which the corresponding displacement solutions were derived. Stress influence functions, together with their displacement counterparts, are a fundamental part of many numerical methods of discretization such the boundary element method.
Statistical Search for Migrations of Aftershock Sequences

Aftershock sequences in Kuril of August 11, 1969 and June 17, 1973 are investigated by a statistical method. On the assumption of a unilateral propagation of aftershock activity, the direction of propagation is likely to be normal to the trench axis. On the basis of this result, time-space plots of other sequences in Kuril are examined. It is commonly observed that a quiescent area of aftershock activity originates at the epicenter of the main shock, and spreads seaward with time. The spreading velocity of a quiescent area is estimated at 8. --16 km/day.
The DNA sequence of the gene and genetic control sites for the excreted B. subtilis enzyme

The sequence of a 1409 base pair restriction fragment containing the B.subtilis gene for (1-3),(1-4)-A -D-glucan endoglu- canase is reported. The gene is encoded in a 726 base pair seg- ment. The deduced amino acid sequence of the protein has a hydro- phobic signal peptide at the NH2-terminus similar to those found in five other secreted proteins from Bacillus. The gene is pre- ceded by a sequence resembling promoters for the vegetative B. subtilis RNA polymerase. This is followed by a sequence resemb- ling a B.subtilis ribosome binding site nine nucleotides before the first codon of the gene. Two sequences, one before and one after the gene, can be arranged in secondary structures similar to transcriptional terminators. There is also a short open read- ing frame coding for a hydrophobic protein on the minus strand just upstream from the P-glucanase gene. A possible role for this gene in the control of expression of -glucanase is suggested.
Indoor air quality impacts of residential HVAC systems, phase II. B report:: IAQ control retrofit simulations and analysis

The National Institute of Standards and Technology (NIST) performed a preliminary study of the potential for using central forced-air heating and cooling system modifications to control indoor air quality (IAQ) in residential buildings. The objective of this effort was to provide insight into the use of state-of-the-art IAQ models to evaluate such modifications, the potential of these modifications to mitigate residential IAQ problems, the pollutant sources they are most likely to impact, and their potential limitations. This study was not intended to determine definitively whether the IAQ control options studied are reliable and cost-effective. The report summarizes the results on Phase II.B of this project, which consisted of three main efforts: computer simulations of contaminant levels with IAQ control retrofits, evaluation of the effectiveness of the IAQ control retrofits, and development of recommendations for future research.
Indistinguishability between quantum randomness and pseudorandomness under efficiently calculable randomness measures

We present a no-go theorem for the distinguishability between quantum random numbers (i.e., random numbers generated quantum mechanically) and pseudo-random numbers (i.e., random numbers generated algorithmically). The theorem states that one cannot distinguish these two types of random numbers if the quantum random numbers are efficiently classically simulatable and the randomness measure used for the distinction is efficiently computable. We derive this theorem by using the properties of cryptographic pseudo-random number generators, which are believed to exist in the field of cryptography. Our theorem is found to be consistent with the analyses on the actual data of quantum random numbers generated by the IBM Quantum and also those obtained in the Innsbruck experiment for the Bell test, where the degrees of randomness of these two set of quantum random numbers turn out to be essentially indistinguishable from those of the corresponding pseudo-random numbers. Previous observations on the algorithmic randomness of quantum random numbers are also discussed and reinterpreted in terms of our theorems and data analyses.
The Project MinE databrowser: bringing large-scale whole-genome sequencing in ALS to researchers and the public

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive fatal neurodegenerative disease affecting 1 in 350 people. The aim of Project MinE is to elucidate the pathophysiology of ALS through whole-genome sequencing at least 15,000 ALS patients and 7,500 controls at 30X coverage. Here, we present the Project MinE data browser (databrowser.projectmine.com), a unique and intuitive one-stop, open-access server that provides detailed information on genetic variation analyzed in a new and still growing set of 4,366 ALS cases and 1,832 matched controls. Through its visual components and interactive design, the browser specifically aims to be a resource to those without a biostatistics background and allow clinicians and preclinical researchers to integrate Project MinE data into their own research. The browser allows users to query a transcript and immediately access a unique combination of detailed (meta)data, annotations and association statistics that would otherwise require analytic expertise and visits to scattered resources.
A Phase I Dose-Escalation Study of SYHA1813, a VEGFRs and CSF1R Inhibitor, in Patients with Recurrent High-Grade Gliomas or Advanced Solid Tumors

SYHA1813 is a potent, multi-kinase inhibitor that targets vascular endothelial growth factor receptors (VEGFRs)/ colony-stimulating factor 1 receptor (CSF1R). This study aimed to evaluate the safety, pharmacokinetics (PK), and antitumor activity of escalating doses of SYHA1813 in patients with recurrent high-grade gliomas (HGG) or advanced solid tumors. This study adopted a combination of accelerated titration and a 3 + 3 design for dose escalation, with a starting dose of 5 mg once daily. The dose escalation continued at successive dose levels until the maximum-tolerated dose (MTD) was determined. A total of 14 patients were enrolled and treated, including 13 with WHO grade III or IV gliomas, and 1 with colorectal cancer. Two patients experienced dose-limiting toxicities (grade 4 hypertension and grade 3 mucositis oral) at 30 mg SYHA1813. The MTD was de�ned as 15 mg once daily. Hypertension (n = 6, 42.9%) was the most frequent treatment-related adverse event. Among evaluable patients (n = 10), 2 (20%) patients reached partial response, and 7 (70%) had stable disease. The exposure increased with increasing doses within the studied dose range of 5 to 30 mg. Biomarker assessments demonstrated signi�cant reductions in soluble VEGFR2 (P = .0023) and increases in VEGFA (P = .0092) and placental growth factor (P = .0484). The safety of SYHA1813 was manageable, and encouraging anti-tumor e�cacy was observed in patients with recurrent malignant glioma. Chinese Clinical Trial Registry (www.chictr.org.cn/index.aspx), identi�er ChiCTR2100045380.
LEA*: An A* Variant Algorithm with Improved Edge Efficiency for Robot Motion Planning

In this work, we introduce a new graph search algorithm, lazy edged based A* (LEA*), for robot motion planning. By using an edge queue and exploiting the idea of lazy search, LEA* is optimally vertex efficient similar to A*, and has improved edge efficiency compared to A*. LEA* is simple and easy to implement with minimum modification to A*, resulting in a very small overhead compared to previous lazy search algorithms. We also explore the effect of inflated heuristics, which results in the weighted LEA* (wLEA*). We show that the edge efficiency of wLEA* becomes close to LazySP and, thus is near-optimal. We test LEA* and wLEA* on 2D planning problems and planning of a 7-DOF manipulator. We perform a thorough comparison with previous algorithms by considering sparse, medium, and cluttered random worlds and small, medium, and large graph sizes. Our results show that LEA* and wLEA* are the fastest algorithms to find the plan compared to previous algorithms.
Markov Chain-Guided Graph Construction and Sampling Depth Optimization for EEG-Based Mental Disorder Detection

Graph Neural Networks (GNNs) have received considerable attention since its introduction. It has been widely applied in various fields due to its ability to represent graph structured data. However, the application of GNNs is constrained by two main issues. Firstly, the"over-smoothing"problem restricts the use of deeper network structures. Secondly, GNNs' applicability is greatly limited when nodes and edges are not clearly defined and expressed, as is the case with EEG data.In this study, we proposed an innovative approach that harnesses the distinctive properties of the graph structure's Markov Chain to optimize the sampling depth of deep graph convolution networks. We introduced a tailored method for constructing graph structures specifically designed for analyzing EEG data, alongside the development of a vertex-level GNN classification model for precise detection of mental disorders. In order to verify the method's performance, we conduct experiments on two disease datasets using a subject-independent experiment scenario. For the Schizophrenia (SZ) data, our method achieves an average accuracy of 100% using only the first 300 seconds of data from each subject. Similarly, for Major Depressive Disorder (MDD) data, the method yields average accuracies of over 99%. These experiments demonstrate the method's ability to effectively distinguish between healthy control (HC) subjects and patients with mental disorders. We believe this method shows great promise for clinical diagnosis.
Addressing Mixed Traffic Through Platooning of Vehicles

Connected and automated vehicles (CAVs) provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better operating decisions to improve safety and reduce pollution, energy consumption, and travel delays. While several studies have shown the benefits of CAVs in reducing energy and alleviating traffic congestion in specific traffic scenarios, e.g., crossing signal-free intersections, merging at roadways and roundabouts, cruising in congested traffic, passing through speed reduction zones, and lane-merging or passing maneuvers, most of these efforts have focused on 100% CAV penetration rates without considering human-driven vehicles (HDVs). One key question that still remains unanswered is ``how can CAVs and HDVs be coordinated safely? '' In this paper, we report on an optimal control framework to coordinate CAVs and HDVs at any traffic scenario. The idea is to directly control the CAVs to force the trailing HDVs to form platoons. Thus, we indirectly control the HDVs by coordinating the platoon of HDVs led by CAVs.
Glial Cell Adhesion Molecule (GlialCAM) Determines Proliferative versus Invasive Cell States in Glioblastoma

The malignant brain cancer glioblastoma (GBM) contains groups of highly invasive cells that drive tumor progression as well as recurrence after surgery and chemotherapy. The molecular mechanisms that enable these GBM cells to exit the primary mass and disperse throughout the brain remain largely unknown. Here we report using human tumor specimens and primary spheroids from male and female patients that glial cell adhesion molecule (GlialCAM), which has normal roles in brain astrocytes and is mutated in the developmental brain disorder megalencephalic leukoencephalopathy with subcortical cysts (MLC), is differentially expressed in subpopulations of GBM cells. High levels of GlialCAM promote cell–cell adhesion and a proliferative GBM cell state in the tumor core. In contrast, GBM cells with low levels of GlialCAM display diminished proliferation and enhanced invasion into the surrounding brain parenchyma. RNAi-mediated inhibition of GlialCAM expression leads to activation of proinvasive extracellular matrix adhesion and signaling pathways. Profiling GlialCAM-regulated genes combined with cross-referencing to single-cell transcriptomic datasets validates functional links among GlialCAM, Mlc1, and aquaporin-4 in the invasive cell state. Collectively, these results reveal an important adhesion and signaling axis comprised of GlialCAM and associated proteins including Mlc1 and aquaporin-4 that is critical for control of GBM cell proliferation and invasion status in the brain cancer microenvironment. SIGNIFICANCE STATEMENT Glioblastoma (GBM) contains heterogeneous populations of cells that coordinately drive proliferation and invasion. We have discovered that glial cell adhesion molecule (GlialCAM)/hepatocyte cell adhesion molecule (HepaCAM) is highly expressed in proliferative GBM cells within the tumor core. In contrast, GBM cells with low levels of GlialCAM robustly invade into surrounding brain tissue along blood vessels and white matter. Quantitative RNA sequencing identifies various GlialCAM-regulated genes with functions in cell–cell adhesion and signaling. These data reveal that GlialCAM and associated signaling partners, including Mlc1 and aquaporin-4, are key factors that determine proliferative and invasive cell states in GBM.
Learning from the Origins∗

How do political preferences and voting behaviors respond to information coming from abroad? I focus on international migration network and document that across a set of topics, opinion changes at the origins spill over to 1stand 2nd-gen immigrants. Local diasporas, social media, and family ties to the origins facilitate the transmission, while social integration at destination weakens it. Using a plausibly exogenous variation in origin-country exposure to the European Refugee Crisis of 2015, I show that origin-country shocks trigger learning from the origins. When the Crisis hits the origins, changing sentiment about refugees spreads to immigrants abroad, and affects the extent of far-right voting. Data from Google Trends and Facebook shows that elevated attention to events at the origins and communication with like-minded groups are the mechanisms. Similar spillovers from the staggered passage of same-sex marriage laws suggest that the phenomenon generalizes beyond refugee attitudes.
Monte-Carlo tree search with uncertainty propagation via optimal transport

This paper introduces a novel backup strategy for Monte-Carlo Tree Search (MCTS) designed for highly stochastic and partially observable Markov decision processes. We adopt a probabilistic approach, modeling both value and action-value nodes as Gaussian distributions. We introduce a novel backup operator that computes value nodes as the Wasserstein barycenter of their action-value children nodes; thus, propagating the uncertainty of the estimate across the tree to the root node. We study our novel backup operator when using a novel combination of $L^1$-Wasserstein barycenter with $\alpha$-divergence, by drawing a notable connection to the generalized mean backup operator. We complement our probabilistic backup operator with two sampling strategies, based on optimistic selection and Thompson sampling, obtaining our Wasserstein MCTS algorithm. We provide theoretical guarantees of asymptotic convergence to the optimal policy, and an empirical evaluation on several stochastic and partially observable environments, where our approach outperforms well-known related baselines.
Vegan/vegetarian diet and human milk donation: An EMBA survey across European milk banks

Abstract The nutritional adequacy of human milk (HM) from vegan/vegetarian mothers has been a matter of debate, and a variety of recommendations regarding the eligibility of these mothers as human milk donors exists. According to the latest evidence, HM from vegans/vegetarians is similar in its composition to that from omnivores, however, some differences may be observed regarding vitamin B12 and omega‐3 fatty acids concentrations. Maternal supplementation of these compounds has been proven effective in increasing their HM concentration. With this survey, we aimed to explore the practices currently in use in European human milk banks (HMBs) regarding potential donors following vegan/vegetarian diets. The online survey was distributed to European HMBs between January and July 2022. A total of 188 HMBs were contacted, and 118 replied (response rate 63%). Vegan and vegetarian mothers were recommended supplements of vitamin B12 to be accepted as donors in 27% and 26% of HMBs, respectively. Additional omega‐3 fatty acid supplementation was required in 8% HMBs. In the remaining HMBs, these mothers were either systematically excluded or included regardless of supplementation. The dosage of the recommended supplements was extremely variable. Fifty‐one percent of HMBs were following recommendations to guide their practice, national or local recommendations mainly. Great variability in European HMBs practices towards potential donors following vegan/vegetarian diets exists. Some of these practices can result in loss of donors and/or in potential nutritional deficiencies. Standardised evidence‐based recommendations on this issue and their implementation in daily HMB practice are needed.
Learning Orbitally Stable Systems for Diagrammatically Teaching

Diagrammatic Teaching is a paradigm for robots to acquire novel skills, whereby the user provides 2D sketches over images of the scene to shape the robot's motion. In this work, we tackle the problem of teaching a robot to approach a surface and then follow cyclic motion on it, where the cycle of the motion can be arbitrarily specified by a single user-provided sketch over an image from the robot's camera. Accordingly, we introduce the \emph{Stable Diffeomorphic Diagrammatic Teaching} (SDDT) framework. SDDT models the robot's motion as an \emph{Orbitally Asymptotically Stable} (O.A.S.) dynamical system that learns to follow the user-specified sketch. This is achieved by applying a \emph{diffeomorphism}, i.e. a differentiable and invertible function, to morph a known O.A. S. system. The parameterised diffeomorphism is then optimised with respect to the Hausdorff distance between the limit cycle of our modelled system and the sketch, to produce the desired robot motion. We provide theoretical insight into the behaviour of the optimised system and also empirically evaluate SDDT, both in simulation and on a quadruped with a mounted 6-DOF manipulator. Results show that we can diagrammatically teach complex cyclic motion patterns with a high degree of accuracy.
Multisource Holography

Holographic displays promise several benefits including high quality 3D imagery, accurate accommodation cues, and compact form-factors. However, holography relies on coherent illumination which can create undesirable speckle noise in the final image. Although smooth phase holograms can be speckle-free, their non-uniform eyebox makes them impractical, and speckle mitigation with partially coherent sources also reduces resolution. Averaging sequential frames for speckle reduction requires high speed modulators and consumes temporal bandwidth that may be needed elsewhere in the system. In this work, we propose multisource holography, a novel architecture that uses an array of sources to suppress speckle in a single frame without sacrificing resolution. By using two spatial light modulators, arranged sequentially, each source in the array can be controlled almost independently to create a version of the target content with different speckle. Speckle is then suppressed when the contributions from the multiple sources are averaged at the image plane. We introduce an algorithm to calculate multisource holograms, analyze the design space, and demonstrate up to a 10 dB increase in peak signal-to-noise ratio compared to an equivalent single source system. Finally, we validate the concept with a benchtop experimental prototype by producing both 2D images and focal stacks with natural defocus cues.
EdgeP4: A P4-Programmable Edge Intelligent Ethernet Switch for Tactile Cyber-Physical Systems

Tactile Internet based operations, e.g., telesurgery, rely on end-to-end closed loop control for accuracy and corrections. The feedback and control are subject to network latency and loss. We design two edge intelligence algorithms hosted at P4 programmable end switches. These algorithms locally compute and command corrective signals, thereby dispense the feedback signals from traversing the network to the other ends and save on control loop latency and network load. We implement these algorithms entirely on data plane on Netronome Agilio SmartNICs using P4. Our first algorithm, $\textit{pose correction}$, is placed at the edge switch connected to an industrial robot gripping a tool. The round trip between transmitting force sensor array readings to the edge switch and receiving correct tip coordinates at the robot is shown to be less than $100~\mu s$. The second algorithm, $\textit{tremor suppression}$, is placed at the edge switch connected to the human operator. It suppresses physiological tremors of amplitudes smaller than $100~\mu m$ which not only improves the application's performance but also reduces the network load up to $99.9\%$. Our solution allows edge intelligence modules to seamlessly switch between the algorithms based on the tasks being executed at the end hosts.
Constructor algorithms for building unconventional computers able to solve NP-complete problems

Nature often builds physical structures tailored for specific information processing tasks with computations encoded using diverse phenomena. These can sometimes outperform typical general-purpose computers. However, describing the construction and function of these unconventional computers is often challenging. Here, we address this by introducing constructor algorithms in the context of a robotic wire machine that can be programmed to build networks of connected wires in response to a problem and then act upon these to efficiently carry out a desired computation. We show how this approach can be used to solve the NP-complete Subset Sum Problem (SSP) and provide information about the number of solutions through changes in the voltages and currents measured across these networks. This work provides a foundation for building unconventional computers that encode information purely in the lengths and connections of electrically conductive wires. It also demonstrates the power of computing paradigms beyond digital logic and opens avenues to more fully harness the inherent computational capabilities of diverse physical, chemical and biological substrates.