Miracle-dz/UniFAR-FTUs huggingface dataset:
On multi-path longitudinal spin relaxation in brain tissue

The purpose of this paper is to conﬁrm previous reports that identiﬁed magnetization transfer (MT) as an inherent driver of longitudinal relaxation in brain tissue by asserting a substantial diﬀerence between the T 1 relaxation times of the free and the semi-solid spin pools . Further, we aim to identify an avenue towards the quantiﬁcation of these relaxation processes on a voxel-by-voxel basis in a clinical imaging setting, i.e. with a nominal resolution of 1mm isotropic and full brain coverage in 12min. To this end, we optimized a hybrid-state pulse sequence for mapping the parameters of an unconstrained MT model. We scanned 4 people with relapsing-remitting multiple sclerosis (MS) and 4 healthy controls with this pulse sequence and estimated T f 1 ≈ 1 . 90s and T s 1 ≈ 0 . 327s for the free and semi-solid spin pool of healthy WM, respectively, conﬁrming previous reports and questioning the commonly used assumptions T s 1 = T f 1 or T s 1 = 1s. Further, we estimated a fractional size of the semi-solid spin pool of m s 0 ≈ 0 . 202, which is larger than previously assumed. An analysis of T f 1 in normal appearing white matter revealed statistically signiﬁcant diﬀerences between individuals with MS and controls. In conclusion, we conﬁrm that longitudinal spin relaxation in brain tissue is dominated by MT and that the hybrid state facilitates a voxel-wise ﬁt of the unconstrained MT model, which enables the analysis of subtle neurodegeneration.
A Big-Data Driven Framework to Estimating Vehicle Volume based on Mobile Device Location Data

Vehicle volume serves as a critical metric and the fundamental basis for traffic signal control, transportation project prioritization, road maintenance plans and more. Traditional methods of quantifying vehicle volume rely on manual counting, video cameras, and loop detectors at a limited number of locations. These efforts require significant labor and cost for expansions. Researchers and private sector companies have also explored alternative solutions such as probe vehicle data, while still suffering from a low penetration rate. In recent years, along with the technological advancement in mobile sensors and mobile networks, Mobile Device Location Data (MDLD) have been growing dramatically in terms of the spatiotemporal coverage of the population and its mobility. This paper presents a big-data driven framework that can ingest terabytes of MDLD and estimate vehicle volume at a larger geographical area with a larger sample size. The proposed framework first employs a series of cloud-based computational algorithms to extract multimodal trajectories and trip rosters. A scalable map matching and routing algorithm is then applied to snap and route vehicle trajectories to the roadway network. The observed vehicle counts on each roadway segment are weighted and calibrated against ground truth control totals, i.e., Annual Vehicle-Miles of Travel (AVMT), and Annual Average Daily Traffic (AADT). The proposed framework is implemented on the all-street network in the state of Maryland using MDLD for the entire year of 2019. Results indicate that our proposed framework produces reliable vehicle volume estimates and also demonstrate its transferability and the generalization ability.
Verification of crossbar-based lattice through modeling technique

The use of Nano crossbar-based switching lattice implementation of Boolean functions has been proposed as an alternative to traditional CMOS-based implementations in digital circuits. As Moore law is expected to come to an end soon, the use of crossbar-based switching lattice implementation may be a solution to meet the demands of future electronic designs. In recent years, various methods and tools have been proposed for implementing boolean functions with crossbar structures. In this work, a method for verifying crossbar-based lattice has been proposed and implemented. This kind of verification will be necessary for any design that utilizes this crossbar-based implementation of boolean logic.
An Analysis of the Automatic Bug Fixing Performance of ChatGPT

To support software developers in finding and fixing software bugs, several automated program repair techniques have been introduced. Given a test suite, standard methods usually either synthesize a repair, or navigate a search space of software edits to find test-suite passing variants. Recent program repair methods are based on deep learning approaches. One of these novel methods, which is not primarily intended for automated program repair, but is still suitable for it, is ChatGPT. The bug fixing performance of ChatGPT, however, is so far unclear. Therefore, in this paper we evaluate ChatGPT on the standard bug fixing benchmark set, QuixBugs, and compare the performance with the results of several other approaches reported in the literature. We find that ChatGPT's bug fixing performance is competitive to the common deep learning approaches CoCoNut and Codex and notably better than the results reported for the standard program repair approaches. In contrast to previous approaches, ChatGPT offers a dialogue system through which further information, e.g., the expected output for a certain input or an observed error message, can be entered. By providing such hints to ChatGPT, its success rate can be further increased, fixing 31 out of 40 bugs, outperforming state-of-the-art.
Towards Multi-robot Exploration: A Decentralized Strategy for UAV Forest Exploration

Efficient exploration strategies are vital in tasks such as search-and-rescue missions and disaster surveying. Unmanned Aerial Vehicles (UAVs) have become particularly popular in such applications, promising to cover large areas at high speeds. Moreover, with the increasing maturity of onboard UAV perception, research focus has been shifting toward higher-level reasoning for single- and multi-robot missions. However, autonomous navigation and exploration of previously unknown large spaces still constitutes an open challenge, especially when the environment is cluttered and exhibits large and frequent occlusions due to high obstacle density, as is the case of forests. Moreover, the problem of long-distance wireless communication in such scenes can become a limiting factor, especially when automating the navigation of a UAV swarm. In this spirit, this work proposes an exploration strategy that enables UAVs, both individually and in small swarms, to quickly explore complex scenes in a decentralized fashion. By providing the decision-making capabilities to each UAV to switch between different execution modes, the proposed strategy strikes a great balance between cautious exploration of yet completely unknown regions and more aggressive exploration of smaller areas of unknown space. This results in full coverage of forest areas of variable density, consistently faster than the state of the art. Demonstrating successful deployment with a single UAV as well as a swarm of up to three UAVs, this work sets out the basic principles for multi-root exploration of cluttered scenes, with up to 65% speed up in the single UAV case and 40% increase in explored area for the same mission time in multi-UAV setups.
On the construction and identification of Boltzmann processes

Given the existence of a solution f(t; x; v)_{t \in \mathbb{R}_+^0} of the Boltzmann equation for hard spheres, we introduce a stochastic differential equation driven by a Poisson random measure that depends on f(t; x; v). The marginal distributions of its solution solves a linearized Boltzmann equation in the weak form. Further, if the distributions admit a probability density, we establish, under suitable conditions, that the density at each t coincides with f(t; x; v). The stochastic process is therefore called the Boltzmann process.
Cross-type submicron Josephson junctions using SNS technology for Josephson voltage standard applications

We have developed a very simple method for manufacturing submicrometer Josephson junctions (JJs) based on niobium and titanium layers. Together with the use of a microwave circuit consisting of modified coplanar strips this type of JJs offers the possibility of easy integration of several thousand submicrometer junctions in a microwave circuit. Up to this point we have manufactured and successfully tested arrays with 2000 super-/normal-/super-conductor junctions with areas from 0.3 /spl mu/m/spl times/0.3 /spl mu/m to 2.0 /spl mu/m/spl times/2.0 /spl mu/m. First measurements with microwave irradiation showed stable Shapiro steps at voltages up to 40 mV and, thereby, the proper operation of all 2,000 junctions was seen.
Ontology Pre-training for Poison Prediction

Integrating human knowledge into neural networks has the potential to improve their robustness and interpretability. We have developed a novel approach to integrate knowledge from ontologies into the structure of a Transformer network which we call ontology pre-training: we train the network to predict membership in ontology classes as a way to embed the structure of the ontology into the network, and subsequently fine-tune the network for the particular prediction task. We apply this approach to a case study in predicting the potential toxicity of a small molecule based on its molecular structure, a challenging task for machine learning in life sciences chemistry. Our approach improves on the state of the art, and moreover has several additional benefits. First, we are able to show that the model learns to focus attention on more meaningful chemical groups when making predictions with ontology pre-training than without, paving a path towards greater robustness and interpretability. Second, the training time is reduced after ontology pre-training, indicating that the model is better placed to learn what matters for toxicity prediction with the ontology pre-training than without. This strategy has general applicability as a neuro-symbolic approach to embed meaningful semantics into neural networks.
A Novel Feeder-level Microgrid Unit Commitment Algorithm Considering Cold-load Pickup, Phase Balancing, and Reconfiguration

This paper presents a novel 2-stage microgrid unit commitment (Microgrid-UC) algorithm considering cold-load pickup (CLPU) effects, three-phase load balancing requirements, and feasible reconfiguration options. Microgrid-UC schedules the operation of switches, generators, battery energy storage systems, and demand response resources to supply 3-phase unbalanced loads in an islanded microgrid for multiple days. A performance-based CLPU model is developed to estimate additional energy needs of CLPU so that CLPU can be formulated into the traditional 2-stage UC scheduling process. A per-phase demand response budget term is added to the 1st stage UC objective function to meet 3-phase load unbalance limits. To reduce computational complexity in the 1st stage UC, we replace the spanning tree method with a feasible reconfiguration topology list method. The proposed algorithm is developed on a modified IEEE 123-bus system and tested on the real-time simulation testbed using actual load and PV data. Simulation results show that Microgrid-UC successfully accounts for CLPU, phase imbalance, and feeder reconfiguration requirements.
The truth about gluten!

Abstract Wheat was first cultivated in the Fertile Crescent (South Western Asia) with a farming expansion that lasted from around 9000 BC to 4000 BC. Whilst humans have been exposed to wheat for around the last 10 000 years, humans have existed for greater than 2·5 million years. Therefore, wheat (and thereby gluten) are relatively new introductions to our diet! By the end of the 20th century, global wheat output had expanded by 5-fold, with a corresponding increase in the prevalence of gluten-related disorders. Coeliac disease (CD) is a state of heightened immunological responsiveness to ingested gluten in genetically susceptible individuals. CD now affects 1 % or more of all adults, for which the management is a strict lifelong gluten-free diet (GFD). However, there is a growing body of evidence to show that a far greater proportion of individuals without CD are self-initiating a GFD. This includes individuals initiating a GFD as a lifestyle choice, people with irritable bowel-type symptoms and those diagnosed with non-coeliac gluten (or wheat) sensitivity. Despite a greater recognition of gluten-related disorders, gaps still remain in our understanding of both their aetiology and management. This article explores the role of the gluten-free diet in gluten-related disorders, along with current uncertainties.
DETAILED AND SIMPLIFIED NONEQUILIBRIUM HELIUM IONIZATION IN THE SOLAR ATMOSPHERE

Helium ionization plays an important role in the energy balance of the upper chromosphere and transition region. Helium spectral lines are also often used as diagnostics of these regions. We carry out one-dimensional radiation-hydrodynamics simulations of the solar atmosphere and find that the helium ionization is set mostly by photoionization and direct collisional ionization, counteracted by radiative recombination cascades. By introducing an additional recombination rate mimicking the recombination cascades, we construct a simplified three-level helium model atom consisting of only the ground states. This model atom is suitable for modeling nonequilibrium helium ionization in three-dimensional numerical models. We perform a brief investigation of the formation of the He i 10830 and He ii 304 spectral lines. Both lines show nonequilibrium features that are not recovered with statistical equilibrium models, and caution should therefore be exercised when such models are used as a basis for interpretating observations.
A robust normalized local filter to estimate compositional heterogeneity directly from cryo-EM maps

Cryo electron microscopy (cryo-EM) is used by biological research to visualize biomolecular complexes in 3D, but the heterogeneity of cryo-EM reconstructions is not easily estimated. Current processing paradigms nevertheless exert great effort to reduce flexibility and heterogeneity to improve the quality of the reconstruction. Clustering algorithms are typically employed to identify populations of data with reduced variability, but lack assessment of remaining heterogeneity. We have developed a fast and simple algorithm based on spatial filtering to estimate the heterogeneity of a reconstruction. In the absence of flexibility, this estimate approximates macromolecular component occupancy. We show that our implementation can derive reliable input parameters automatically, that the resulting estimate is accurate, and the reconstruction can be modified accordingly to emulate altered constituent occupancy, which may benefit conventionally employed maximum-likelihood classification methods. Here, we demonstrate the utility of this method for cryo-EM map interpretation, quantification, and particle-image signal subtraction.
Conductivity scaling with bundle length and diameter in single walled carbon nanotube networks

Transparent single walled carbon nanotube (SWNT) networks were printed on plastic substrates. Nanotubes in the network form small bundles, and the authors evaluated the dc conductivity (σdc) as a function of the average bundle length (Lav) in the network. They find σdc to vary as σdc∼Lav1.46 for bundles of the same diameter and give a qualitative argument for why this agrees with a model where the resistance between SWNT bundles dominates the overall network resistance.
Vision-based collective motion: A locust-inspired reductionist model

Naturally occurring collective motion is a fascinating phenomenon in which swarming individuals aggregate and coordinate their motion. Many theoretical models of swarming assume idealized, perfect perceptual capabilities, and ignore the underlying perception processes, particularly for agents relying on visual perception. Specifically, biological vision in many swarming animals, such as locusts, utilizes monocular non-stereoscopic vision, which prevents perfect acquisition of distances and velocities. Moreover, swarming peers can visually occlude each other, further introducing estimation errors. In this study, we explore necessary conditions for the emergence of ordered collective motion under restricted conditions, using non-stereoscopic, monocular vision. We present a model of vision-based of collective motion for locust-like agents: elongated shape, omni-directional visual sensor parallel to the horizontal plane, and lacking stereoscopic depth perception. The model addresses (i) the non-stereoscopic estimation of distance and velocity, (ii) the presence of occlusions in the visual field. We consider and compare three strategies that an agent may use to interpret partially-occluded visual information at the cost of the computational complexity required for the visual perception processes. Computer-simulated experiments conducted in various geometrical environments (toroidal, corridor, and ring-shaped arenas) demonstrate that the models can result in an ordered or near-ordered state. At the same time, they differ in the rate at which order is achieved. Moreover, the results are sensitive to the elongation of the agents. Experiments in geometrically constrained environments reveal differences between the models and elucidate possible tradeoffs in using them to control swarming agents. These suggest avenues for further study in biology and robotics. Author summary Swarm collective motion is a wide-ranging phenomenon in nature, with applications in multi-agent, multi-robot systems. In most natural swarming species, individuals rely on monocular, non-stereoscopic vision as the key sensory modality for their interactions. For example, the migratory locust (locusta migratoria) displays large swarms of individuals, moving in alignment and relying solely on non-stereoscopic visual perception. Inspired by these locust swarms, we have developed a monocular, non-stereoscopic vision-based model that achieves synchronized motion in a swarm of two-dimensional agents, even with inaccurate estimates of distances and velocities, particularly in the presence of occlusions. We explore three general strategies for handling occlusions, which differ in the requirements they place on the complexity of the visual perception process. We show that strategies may reach a highly ordered motion state but differ in their convergence rate.
Toward stability of dynamic FC estimates in neuroimaging and electrophysiology: Solutions and limits

Time-varying functional connectivity methods are used to map the spatiotemporal organization of brain activity. However, their estimation can be unstable, in the sense that different runs of the inference may yield different solutions. But to draw meaningful relations to behaviour, estimates must be robust and reproducible. Here, we propose two solutions using the Hidden Markov Model (HMM) as a descriptive model of time-varying FC. The first, best-ranked HMM, involves running the inference multiple times and selecting the best model based on a quantitative measure combining fitness and model complexity. The second, hierarchical clustered HMM, generates stable aggregated state timeseries by applying hierarchical clustering to the state timeseries obtained from multiple runs. Experimental results on fMRI and MEG data demonstrate that these approaches substantially improve the stability of time-varying FC estimations. Overall, hierarchical clustered HMM is preferred when the inference variability is high, while the best-ranked HMM performs better otherwise.
CONSTANS alters the circadian clock in Arabidopsis thaliana

Plants are sessile organisms that have acquired highly plastic developmental strategies to adapt to the environment. Among these processes, the floral transition is essential to ensure reproductive success and is finely regulated by several internal and external genetic networks. The photoperiodic pathway, which controls the plant response to day length, is one of the most important pathways controlling flowering. In Arabidopsis photoperiodic flowering, CONSTANS (CO) is the central gene activating the expression of the florigen FLOWERING LOCUS T (FT) in the leaves at the end of a long day. CO expression is strongly regulated by the circadian clock. However, to date, no evidence has been reported regarding a feedback loop from the photoperiod pathway back to the circadian clock. Using transcriptional networks, we have identified relevant network motifs regulating the interplay between the circadian clock and the photoperiod pathway. Gene expression, chromatin immunoprecipitation experiments and phenotypic analysis allowed us to elucidate the role of CO over the circadian clock. Plants with altered CO expression showed a different internal clock period, measured by daily rhythmic movements in the leaves. We show that CO is able to activate key genes related to the circadian clock, such as CCA1, LHY, PRR5 and GI, at the end of a long day by binding to specific sites on their promoters. Moreover, a significant number of PRR5 repressed target genes are upregulated by CO, and this could explain the phase transition promoted by CO. The CO-PRR5 complex interacts with the bZIP transcription factor HY5 and helps to localize the complex in the promoters of clock genes. Our results indicate that there may be a feedback loop in which CO communicates back to the circadian clock, feeding seasonal information to the circadian system.
Comparison of wholemount dissection methods for neuronal subtype marker expression in the mouse myenteric plexus

Background Accurately reporting the identity and representation of enteric nervous system (ENS) neuronal subtypes along the length of the gastrointestinal (GI) tract is critical to advancing our understanding of ENS control of GI tract function. Reports of varying proportions of subtype marker expression have employed different dissection techniques to achieve wholemount muscularis preparations of myenteric plexus. In this study we asked whether differences in GI dissection methods could introduce variability into the quantification of marker expression. Methods We compared three commonly used methods of ENS wholemount dissection: two flat-sheet preparations that differed in the order of microdissection and fixation as well as a rod-mounted peeling technique. We assessed marker expression using immunohistochemistry, genetic reporter lines, confocal microscopy, and automated image analysis. Key Results and Conclusions We found no significant differences between the two flat-sheet preparation methods in the expression of calretinin, neuronal nitric oxide synthase (nNOS), or somatostatin (SST) in ileum myenteric plexus. However, the rod-mounted peeling method resulted in decreased marker labeling for both calretinin and nNOS. This method also resulted in decreased transgenic reporter fluorescent protein (tdTomato) for substance P in ileum and choline acetyltransferase (ChAT) in both ileum and distal colon. These results suggest that labeling among some markers, both native protein and transgenic fluorescent reporters, is decreased by the rod-mounted mechanical method of peeling, demonstrating a critical variability in wholemount muscularis dissection methods.
Inverse correlations between the ion temperature anisotropy and plasma beta in the Earth's quasi-parallel magnetosheath

Average proton parameters in the magnetosheath downstream from the quasi-perpendicular shock (the quasi-perpendicular magnetosheath) for high solar wind dynamic pressure conditions are observed to vary continuously from high-beta, low-temperature anisotropy to low-beta, high-temperature anisotropy. Observations and theory have shown that this inverse correlation is a direct consequence of pitch angle scattering by electromagnetic ion cyclotron (EMIC) waves, which regulate the anisotropy, restoring the plasma toward marginal stability. Although the previously documented spectral characteristics of EMIC waves are not evident downstream of the quasi-parallel bow shock, the inverse anisotropy-beta relation found in the quasi-perpendicular magnetosheath also holds in the quasi-parallel magnetosheath. This indicates that the EMIC instability regulates the ion anisotropy regardless of the shock geometry.