Miracle-dz/UniFAR-FTUs huggingface dataset:
The presence of high numbers of empty shells in anthropogenic habitats is insufficient to attract shell adopters among the insects

Terrestrial snail shells can be considered partial resources for insects, and as such, the expansion of numerous snail species to anthropogenic habitats makes them increasingly available. Here, we address for the first time a complete profile of insects that use empty terrestrial snail shells during the winter period in Central Europe. The specialisation for shells made by certain snail species was uncommon; however, a number of species showed significant preferences for certain shell types. We found that the presence of empty snail shells in anthropogenic habitats drives the presence of many empty snail shell adopters in these habitats. Nevertheless, the increased availability of snail shells proved to be insufficient for a transition of all the species of snail shell adopters from natural to anthropogenic habitats. The avoidance of anthropogenic habitats among snail shell adopters was particularly distinct in species that use them only as a winter retreat but which require additional feeding and breeding resources, such as the true bugs. The availability of snail shells is thus a pre‐requisite of the presence of specialised snail shell adopters but is not necessarily sufficient to establish their presence in the respective habitat.
The Cost of Power Outages in the Business and Public Sectors in Israel: Revealed Preference vs. Subjective Valuation

Cross-section data on investment in back-up generators and uninterruptable power supplies (UPS) are used to infer the implied cost of electricity outages in the business and public sectors in Israel. Two-limit tobit models of the demand for back-up are estimated and used to simulate the mitigated and unmitigated cost of power outages. These "revealed preference estimates of outage costs are then compared with estimates based on the method of subjective evaluation.
Rotor Position Alignment of FSTPI Based PMSM Drive Using Low Frequency Signal Injection

A PMSM drive with an incremental encoder or using sensorless control requires alignment to a predetermined rotor position (initial position) or initial rotor position detection at start-up. It is desired to lock the rotor to a known state (usually zero angle) at start-up if the initial rotor position detection is not available or difficult to obtain. In this work, a simple and proper zero angle initial rotor position alignment of four-switch three-phase (FSTP) inverter-based PMSM drive is proposed. Low-frequency voltage signal is applied to the d-axis voltage reference of the open-loop FSTPI based PMSM drive scheme without requiring complex trigonometric calculations, PI current regulators and current sensing. Therefore, fluctuated capacitor voltages at the DC-link are obtained allowing current flown through phase a locking the rotor with zero angle, properly. The proposed method has been implemented using a low-cost FSTP voltage source inverter (VSI) for PMSM drive with a floating-point TMS320F28335 DSP. The effectiveness and the feasibility of the proposed zero angle initial rotor position alignment method for PMSM driven by FSTP inverter have been demonstrated through experimental results.
Advanced Image Processing in Cardiac Magnetic Resonance Imaging with Application in Myocardial Perfusion Quantification

Cardiac magnetic resonance imaging (CMRI) has been proven to be a valuable source of diagnostic information concerning heart health. One application, myocardial blood flow (MBF) quantification using first-pass contrast-enhanced myocardial perfusion, has aided the detection of coronary artery disease and provides an accurate evaluation of myocardial ischemia, an identifier of coronary artery stenosis. However, the image processing and analysis requires tedious user interaction, increasing the time and effort required to utilize it. In addition, it can introduce subjectivity and variability into the data analysis, which further limits the potential use of the modality. This dissertation presents several automated image processing algorithms to increase the accuracy, consistency, and efficiency of CMR image processing, and validates them on large, clinical datasets. First, an automated method is proposed to measure the arterial input function (AIF) from the left ventricle (LV), which is required for the accurate quantification of MBF. The proposed algorithm consists of several automated image processing steps including motion correction, intensity correction, detection of the LV, independent component analysis, and LV pixel thresholding to calculate the AIF signal. The method was validated in 270 clinical studies by comparing automated results to manual reference measurements using several quality metrics. Additionally, the MBF was calculated and compared in a subset of 21 clinical studies from healthy volunteers using the automated and manual AIF measurements. The proposed method successfully processed 99.63% of the image series. Manual and automatic AIF measurement showed strong agreement, and the automated method effectively selected bright LV pixels, excluded papillary muscles, and required much less processing time than the manual approach. No significant difference was found in MBF estimates between manually and automatically
How is Pain Acceptance related to Interoceptive Awareness ?

Introduction Chronic pain is one of the most common reasons for patients to seek medical consultation. Due to the lack of effective conservative or symptomatic treatments, behavioral strategies such as Acceptance and Commitment Therapy (ACT) are being incorporated in Rehabilitation Medicine. ACT focuses on increasing the capacity to change behavioral responses to pain by improving three therapeutic processes that will impact the openness, engagement and awareness. Awareness facilitates the access to, and interpretation of bodily signals by potentiating interoceptive awareness (IA), and it is less examined. IA is measured by the Multidimensional Assessment of Interoceptive Awareness (MAIA) and ends up with eight subscales. Pain acceptance (PA) consisting of activity engagement (AE) and pain willingness (PW) is known to promote functioning and stands for the ability to engage in meaningful activities despite the presence of pain. When combining AE with PW, four profiles of PA behaviors are identified; representing different ways of handling pain and thus indicates differential needs of treatment. The Chronic Pain Acceptance Questionnaire (CPAQ-8) measures PA. Aim To examine whether different profiles of PA demonstrate different levels of IA.
Adaptive Sampling Using LS-OPT ®

LS-OPT is a design optimization and probabilistic analysis package with an interface to LS-DYNA® that provides a flexible framework to solve several types of design problems. In order to solve the problem, it runs simulations at multiple samples that are selected all at once (single iteration) or iteratively [1]. The iterative approach has two main advantages:  it does not require prior knowledge about the sufficient number of samples and instead provides a convergence history  it can use updated information from the previous runs to select the samples smartly and thus typically reduces the number of required simulations
Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference

Tackling Natural Language Inference with a logic-based method is becoming less and less common. While this might have been counterintuitive several decades ago, nowadays it seems pretty obvious. The main reasons for such a conception are that (a) logic-based methods are usually brittle when it comes to processing wide-coverage texts, and (b) instead of automatically learning from data, they require much of manual effort for development. We make a step towards to overcome such shortcomings by modeling learning from data as abduction: reversing a theorem-proving procedure to abduce semantic relations that serve as the best explanation for the gold label of an inference problem. In other words, instead of proving sentence-level inference relations with the help of lexical relations, the lexical relations are proved taking into account the sentence-level inference relations. We implement the learning method in a tableau theorem prover for natural language and show that it improves the performance of the theorem prover on the SICK dataset by 1.4% while still maintaining high precision (>94%). The obtained results are competitive with the state of the art among logic-based systems.
View selection in multi-view stacking: Choosing the meta-learner

Multi-view stacking is a framework for combining information from different views (i.e. different feature sets) describing the same set of objects. In this framework, a base-learner algorithm is trained on each view separately, and their predictions are then combined by a meta-learner algorithm. In a previous study, stacked penalized logistic regression, a special case of multi-view stacking, has been shown to be useful in identifying which views are most important for prediction. In this article we expand this research by considering seven different algorithms to use as the meta-learner, and evaluating their view selection and classification performance in simulations and two applications on real gene-expression data sets. Our results suggest that if both view selection and classification accuracy are important to the research at hand, then the nonnegative lasso, nonnegative adaptive lasso and nonnegative elastic net are suitable meta-learners. Exactly which among these three is to be preferred depends on the research context. The remaining four meta-learners, namely nonnegative ridge regression, nonnegative forward selection, stability selection and the interpolating predictor, show little advantages in order to be preferred over the other three.
Exploring Dynamic Context for Multi-path Trajectory Prediction

To accurately predict future positions of different agents in traffic scenarios is crucial for safely deploying intelligent autonomous systems in the real-world environment. However, it remains a challenge due to the behavior of a target agent being affected by other agents dynamically and there being more than one socially possible paths the agent could take. In this paper, we propose a novel framework, named Dynamic Context Encoder Network (DCENet). In our framework, first, the spatial context between agents is explored by using self-attention architectures. Then, the two-stream encoders are trained to learn temporal context between steps by taking the respective observed trajectories and the extracted dynamic spatial context as input. The spatial-temporal context is encoded into a latent space using a Conditional Variational Auto-Encoder (CVAE) module. Finally, a set of future trajectories for each agent is predicted conditioned on the learned spatial-temporal context by sampling from the latent space, repeatedly. DCENet is evaluated on one of the most popular challenging benchmarks for trajectory forecasting Trajnet and reports a new state-of-the-art performance. It also demonstrates superior performance evaluated on the benchmark inD for mixed traffic at intersections. A series of ablation studies is conducted to validate the effectiveness of each proposed module. Our code is available at https://github.com/wtliao/DCENet.
Optimizing entanglement generation and distribution using genetic algorithms

Long-distance quantum communication via entanglement distribution is of great importance for the quantum internet. However, scaling up to such long distances has proved challenging due to the loss of photons, which grows exponentially with the distance covered. Quantum repeaters could in theory be used to extend the distances over which entanglement can be distributed, but in practice hardware quality is still lacking. Furthermore, it is generally not clear how an improvement in a certain repeater parameter, such as memory quality or attempt rate, impacts the overall network performance, rendering the path toward scalable quantum repeaters unclear. In this work we propose a methodology based on genetic algorithms and simulations of quantum repeater chains for optimization of entanglement generation and distribution. By applying it to simulations of several different repeater chains, including real-world fiber topology, we demonstrate that it can be used to answer questions such as what are the minimum viable quantum repeaters satisfying given network performance benchmarks. This methodology constitutes an invaluable tool for the development of a blueprint for a pan-European quantum internet. We have made our code, in the form of NetSquid simulations and the smart-stopos optimization tool, freely available for use either locally or on high-performance computing centers.
Dissipation engineered directional filter for quantum ratchets

We demonstrate transport rectification in a hermitian Hamiltonian quantum ratchet by a dissipative, dynamic impurity. While the bulk of the ratchet supports transport in both directions, the properly designed loss function of the local impurity acts as a direction-dependent filter for the moving states. We analyse this scheme theoretically by making use of Floquet-S-Matrix theory. In addition, we provide the direct experimental observation of one-way transmittance in periodically modulated plasmonic waveguide arrays containing a local impurity with engineered losses.
The Combinatorial Multi-Bandit Problem and its Application to Energy Management

We study a Combinatorial Multi-Bandit Problem motivated by applications in energy systems management. Given multiple probabilistic multi-arm bandits with unknown outcome distributions, the task is to optimize the value of a combinatorial objective function mapping the vector of individual bandit outcomes to a single scalar reward. Unlike in single-bandit problems with multi-dimensional action space, the outcomes of the individual bandits are observable in our setting and the objective function is known. Guided by the hypothesis that individual observability enables better trade-offs between exploration and exploitation, we generalize the lower regret bound for single bandits, showing that indeed for multiple bandits it admits parallelized exploration. For our energy management application we propose a range of algorithms that combine exploration principles for multi-arm bandits with mathematical programming. In an experimental study we demonstrate the effectiveness of our approach to learn action assignments for 150 bandits, each having 24 actions, within a horizon of 365 episodes.
Centromeric CENP-A loading requires accurate mitotic timing, which is linked to checkpoint proteins

A defining feature of centromeres is the presence of the histone H3 variant CENP-A that replaces H3 in a subset of centromeric nucleosomes. In Drosophila cultured cells CENP-A deposition at centromeres takes place during the metaphase stage of the cell cycle and strictly depends on the presence of its specific chaperone CAL1. How CENP-A loading is restricted to mitosis is unknown. We found that CAL1 interacts with the spindle assembly checkpoint protein and RZZ complex component Zw10 and thus constitutes the anchor for the recruitment of RZZ. We identify Zw10 as determinant of a mitotic duration and show that it is inversely correlated with CENP-A levels at centromeres. Moreover, overexpression of CAL1 is associated with increased CENP-A levels at centromeres and completely uncouples CENP-A loading from mitosis. Therefore CAL1 controls CENP-A incorporation at centromeres both quantitatively and temporally, connecting it to the spindle assembly checkpoint to further ensure mitotic fidelity.
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction

In recent years, distantly-supervised relation extraction has achieved a certain success by using deep neural networks. Distant Supervision (DS) can automatically generate large-scale annotated data by aligning entity pairs from Knowledge Bases (KB) to sentences. However, these DS-generated datasets inevitably have wrong labels that result in incorrect evaluation scores during testing, which may mislead the researchers. To solve this problem, we build a new dataset NYTH, where we use the DS-generated data as training data and hire annotators to label test data. Compared with the previous datasets, NYT-H has a much larger test set and then we can perform more accurate and consistent evaluation. Finally, we present the experimental results of several widely used systems on NYT-H. The experimental results show that the ranking lists of the comparison systems on the DS-labelled test data and human-annotated test data are different. This indicates that our human-annotated data is necessary for evaluation of distantly-supervised relation extraction.
Relaxation timescales and electron-phonon coupling in optically pumped 
YBa2Cu3O6+x
 revealed by time-resolved Raman scattering

Quantum matter with striking properties has recently been generated by driving materials with ultrafast laser pulses. However, observing these phases by following a single property as a function of time after photoexcitation provides a limited picture. We directly tracked hot phonons and transient heating in a high temperature superconductor YBa$_2$Cu$_3$O$_{6.9}$ by time-resolved Raman scattering with time-resolution of 220 fs. In addition, we used the hardening of the Raman-active apical oxygen phonon right after photoexcitation as an indirect probe of hot electrons, which equilibrated with hot phonons around 0.5 ps. Analysis based on the two-temperature model revealed temperature-independent electron-phonon coupling with $\lambda \approx $ 0.8 as well as significant anharmonicity that increased with temperature. It is essential to reduce this anharmonicity to generate longer lived nonequilibrium states in copper oxides. We also discuss new insights into photoinduced superconductivity recently reported at lower doping that follow from these results.
On the evolution of a binary system with arbitrarily misaligned orbital and stellar angular momenta due to quasi-stationary tides

We consider the evolution of a binary system interacting due to tidal effects without restriction on the orientation of the orbital, and where significant, spin angular momenta, and orbital eccentricity. We work in the low tidal forcing frequency regime in the equilibrium tide approximation. Internal degrees of freedom are fully taken into account for one component, the primary. In the case of the companion the spin angular momentum is assumed small enough to be neglected but internal energy dissipation is allowed for as this can be significant for orbital circularisation in the case of planetary companions. We obtain a set of equations governing the evolution of the orbit resulting from tidal effects. These depend on the masses and radii of the binary components, the form and orientation of the orbit, and for each involved component, the spin rate, the Coriolis force, the normalised rate of energy dissipation associated with the equilibrium tide due to radiative processes and viscosity, and the classical apsidal motion constant. These depend on stellar parameters with no need of additional assumptions or a phenomenological approach as has been invoked in the past. They can be used to determine the evolution of systems with initial significant misalignment of spin and orbital angular momenta as hypothesised for systems containing Hot Jupiters. The inclusion of the Coriolis force may lead to evolution of the inclination between orbital and spin angular momenta and precession of the orbital plane which may have observational consequences.
A Method for Constraint Inference Using Pose and Wrench Measurements

Many physical tasks such as pulling out a drawer or wiping a table can be modeled with geometric constraints. These geometric constraints are characterized by restrictions on kinematic trajectories and reaction wrenches (forces and moments) of objects under the influence of the constraint. This paper presents a method to infer geometric constraints involving unmodeled objects in human demonstrations using both kinematic and wrench measurements. Our approach takes a recording of a human demonstration and determines what constraints are present, when they occur, and their parameters (e.g. positions). By using both kinematic and wrench information, our methods are able to reliably identify a variety of constraint types, even if the constraints only exist for short durations within the demonstration. We present a systematic approach to fitting arbitrary scleronomic constraint models to kinematic and wrench measurements. Reaction forces are estimated from measurements by removing friction. Position, orientation, force, and moment error metrics are developed to provide systematic comparison between constraint models. By conducting a user study, we show that our methods can reliably identify constraints in realistic situations and confirm the value of including forces and moments in the model regression and selection process.
CURE: A Security Architecture with CUstomizable and Resilient Enclaves

Security architectures providing Trusted Execution Environments (TEEs) have been an appealing research subject for a wide range of computer systems, from low-end embedded devices to powerful cloud servers. The goal of these architectures is to protect sensitive services in isolated execution contexts, called enclaves. Unfortunately, existing TEE solutions suffer from significant design shortcomings. First, they follow a one-size-fits-all approach offering only a single enclave type, however, different services need flexible enclaves that can adjust to their demands. Second, they cannot efficiently support emerging applications (e.g., Machine Learning as a Service), which require secure channels to peripherals (e.g., accelerators), or the computational power of multiple cores. Third, their protection against cache side-channel attacks is either an afterthought or impractical, i.e., no fine-grained mapping between cache resources and individual enclaves is provided. In this work, we propose CURE, the first security architecture, which tackles these design challenges by providing different types of enclaves: (i) sub-space enclaves provide vertical isolation at all execution privilege levels, (ii) user-space enclaves provide isolated execution to unprivileged applications, and (iii) self-contained enclaves allow isolated execution environments that span multiple privilege levels. Moreover, CURE enables the exclusive assignment of system resources, e.g., peripherals, CPU cores, or cache resources to single enclaves. CURE requires minimal hardware changes while significantly improving the state of the art of hardware-assisted security architectures. We implemented CURE on a RISC-V-based SoC and thoroughly evaluated our prototype in terms of hardware and performance overhead. CURE imposes a geometric mean performance overhead of 15.33% on standard benchmarks.