Miracle-dz/UniFAR-FTUs huggingface dataset:
The Copper Responsive Transcription Factor SPL7 Represses Key Abscisic Acid Biosynthetic Genes to Balance Growth and Drought Tolerance

Plants adapt to adverse environments by turning on defense against abiotic stresses, which is mainly orchestrated by the phytohormone abscisic acid (ABA). But how ABA homeostasis is modulated to balance growth and stress responses is still largely unknown. Here we report that prior treatment of Arabidopsis seedling with high copper retardates growth but enhances draught tolerance at later stages by modulating ABA accumulation. Subsequent genetic, physiological, transcriptomic, and molecular investigations revealed that the copper responsive transcription factor SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 7 (SPL7) is a strong regulator of ABA accumulation. We showed that SPL7 is destabilized by high copper and consistently suppresses genes encoding three key oxygenases in the ABA biosynthetic pathway of land plants via binding to the GTAC copper response motifs in their promoters. These results revealed a new mechanism whereby copper availability, inversely reflected by SPL7 abundance, modulates de novo ABA biosynthesis to balance growth and drought tolerance. One-sentence summary High copper availability represses SPL7, releasing its suppression on key ABA biosynthetic genes and leading to increased ABA accumulation that inhibits growth but enhances drought tolerance.
New fossils of Jurassic ophiurid brittle stars (Ophiuroidea; Ophiurida) provide evidence for early clade evolution in the deep sea

Understanding of the evolutionary history of the ophiuroids, or brittle stars, is hampered by a patchy knowledge of the fossil record. Especially the stem members of the living clades are poorly known, resulting in blurry concepts of the early clade evolution and imprecise estimates of divergence ages. Here, we describe new ophiuroid fossil from the Lower Jurassic of France, Luxembourg, and Austria and introduce the new taxa Ophiogojira labadiei gen. et sp. nov. from lower Pliensbachian shallow sublittoral deposits, Ophiogojira andreui gen. et sp. nov. from lower Toarcian shallow sublittoral deposits, and Ophioduplantiera noctiluca gen. et sp. nov. from late Sinemurian to lower Pliensbachian bathyal deposits. A Bayesian morphological phylogenetic analysis shows that Ophiogojira holds a basal position within the order Ophiurida, whereas Ophioduplantiera has a more crownward position within the ophiurid family Ophiuridae. The position of Ophioduplantiera in the evolutionary tree suggests that family-level divergences within the Ophiurida must have occurred before the late Sinemurian, and that ancient slope environments played an important role in fostering early clade evolution.
A co-opted ISG15-USP18 binding mechanism normally reserved for deISGylation controls type I IFN signalling

Type I interferon (IFN) signalling induces the expression of several hundred IFN-stimulated genes that provide an unfavourable environment for viral replication. To prevent an overexuberant response and autoinflammatory disease, IFN signalling requires tight control. One critical regulator is the ubiquitin-like protein ISG15, evidenced by autoinflammatory disease in patients with inherited ISG15 deficiencies. Current models suggest that ISG15 stabilises USP18, a well-established negative regulator of IFN signalling. USP18 also functions as an ISG15-specific peptidase, however its catalytic activity is dispensable for controlling IFN signalling. Here, we show that the ISG15-dependent stabilisation of USP18 is necessary but not sufficient for regulation of IFN signalling and that USP18 requires non-covalent interactions with ISG15 to enhance its regulatory function. Intriguingly, this trait has been acquired through co-option of a binding mechanism normally reserved for deISGylation, identifying an unexpected new function for ISG15.
KIF18B is a cell-type specific regulator of spindle orientation in the epidermis

Proper spindle orientation is required for asymmetric cell division and the establishment of complex tissue architecture. In the developing epidermis, spindle orientation requires a conserved cortical protein complex of LGN/NuMA/dynein-dynactin. However, how microtubule dynamics are regulated to interact with this machinery and properly position the mitotic spindle is not fully understood. Furthermore, our understanding of the processes that link spindle orientation during asymmetric cell division to cell fate specification in distinct tissue contexts remains incomplete. We report a role for the microtubule catastrophe factor KIF18B in regulating microtubule dynamics to promote spindle orientation in keratinocytes. During mitosis, KIF18B accumulates at the cell cortex, colocalizing with the conserved spindle orientation machinery. In vivo we find that KIF18B is required for oriented cell divisions within the hair placode, the first stage of hair follicle morphogenesis, but is not essential in the interfollicular epidermis. Disrupting spindle orientation in the placode, using mutations in either KIF18B or NuMA, results in aberrant cell fate marker expression of hair follicle progenitor cells. These data functionally link spindle orientation to cell fate decisions during hair follicle morphogenesis. Taken together, our data demonstrate a role for regulated microtubule dynamics in spindle orientation in epidermal cells. This work also highlights the importance of spindle orientation during asymmetric cell division to dictate cell fate specification.
Revealing Hidden Preconditions and Effects of Compound HTN Planning Tasks - A Complexity Analysis

In Hierarchical Task Network (HTN) planning, compound tasks need to be refined into executable (primitive) action sequences. In contrast to their primitive counterparts, compound tasks do not specify preconditions or effects. Thus, their implications on the states in which they are applied are not explicitly known: they are "hidden" in and depending on the decomposition structure. We formalize several kinds of preconditions and effects that can be inferred for compound tasks in totally ordered HTN domains. As relevant special case we introduce a problem relaxation which admits reasoning about preconditions and effects in polynomial time. We provide procedures for doing so, thereby extending previous work, which could only deal with acyclic models. We prove our procedures to be correct and complete for any totally ordered input domain. These results are embedded into an encompassing complexity analysis of the inference of preconditions and effects of compound tasks, an investigation that has not been made so far.
Combining Preference Elicitation with Local Search and Greedy Search for Matroid Optimization

We propose two incremental preference elicitation methods for interactive preference-based optimization on weighted matroid structures. More precisely, for linear objective (utility) functions, we propose an interactive greedy algorithm interleaving preference queries with the incremental construction of an independent set to obtain an optimal or near-optimal base of a matroid. We also propose an interactive local search algorithm based on sequences of possibly improving exchanges for the same problem. For both algorithms, we provide performance guarantees on the quality of the returned solutions and the number of queries. Our algorithms are tested on the uniform, graphical and scheduling matroids to solve three different problems (committee election, spanning tree, and scheduling problems) and evaluated in terms of computation times, number of queries, and empirical error.
Improving Generative Moment Matching Networks with Distribution Partition

Generative moment matching networks (GMMN) present a theoretically sound approach to learning deep generative mod-els. However, such methods are typically limited by the high sample complexity, thereby impractical in generating complex data. In this paper, we present a new strategy to train GMMN with a low sample complexity while retaining the theoretical soundness. Our method introduces some auxiliary variables, whose values are provided by a pre-trained model such as an encoder network in practice. Conditioned on these variables, we partition the distribution into a set of conditional distributions, which can be effectively matched with a low sample complexity. We instantiate this strategy by presenting an amortized network called GMMN-DP with shared auxiliary variable information for the data generation task, as well as developing an efficient stochastic training algorithm.The experimental results show that GMMN-DP can generate complex samples on datasets such as CelebA and CIFAR-10, where the vanilla GMMN fails.
Near Lossless Transfer Learning for Spiking Neural Networks

Spiking neural networks (SNNs) significantly reduce energy consumption by replacing weight multiplications with additions. This makes SNNs suitable for energy-constrained platforms. However, due to its discrete activation, training of SNNs remains a challenge. A popular approach is to first train an equivalent CNN using traditional backpropagation, and then transfer the weights to the intended SNN. Unfortunately, this often results in significant accuracy loss, especially in deeper networks. In this paper, we propose CQ training (Clamped and Quantized training), an SNN-compatible CNN training algorithm with clamp and quantization that achieves near-zero conversion accuracy loss. Essentially, CNN training in CQ training accounts for certain SNN characteristics. Using a 7 layer VGG-* and a 21 layer VGG-19, running on the CIFAR-10 dataset, we achieved 94.16% and 93.44% accuracy in the respective equivalent SNNs. It outperforms other existing comparable works that we know of. We also demonstrate the low-precision weight compatibility for the VGG-19 structure. Without retraining, an accuracy of 93.43% and 92.82% using quantized 9-bit and 8-bit weights, respectively, was achieved. The framework was developed in PyTorch and is publicly available.
Security and Trust in Open Source Security Tokens

Using passwords for authentication has been proven vulnerable in countless security incidents. Hardware security tokens effectively prevent most password-related security issues and improve security indisputably. However, we would like to highlight that there are new threats from attackers with physical access which need to be discussed. Supply chain adversaries may manipulate devices on a large scale and install backdoors before they even reach end users. In evil maid scenarios, specific devices may even be attacked while already in use. Hence, we thoroughly investigate the security and trustworthiness of seven commercially available open source security tokens, including devices from the two market leaders: SoloKeys and Nitrokey. Unfortunately, we identify and practically verify significant vulnerabilities in all seven examined tokens. Some of them are based on severe, previously undiscovered, vulnerabilities of two major microcontrollers which are used at a large scale in various products. Our findings clearly emphasize the significant threat from supply chain and evil maid scenarios since the attacks are practical and only require moderate attacker efforts. Fortunately, we are able to describe software-based countermeasures as effective improvements to retrofit the examined devices. To improve the security and trustworthiness of future security tokens, we also derive important general design recommendations.
Enhanced Audio Tagging via Multi- to Single-Modal Teacher-Student Mutual Learning

Recognizing ongoing events based on acoustic clues has been a critical yet challenging problem that has attracted significant research attention in recent years. Joint audio-visual analysis can improve the event detection accuracy but may not always be feasible as under many circumstances only audio recordings are available in real-world scenarios. To solve the challenges, we present a novel visual-assisted teacher-student mutual learning framework for robust sound event detection from audio recordings. Our model adopts a multi-modal teacher network based on both acoustic and visual clues, and a single-modal student network based on acoustic clues only. Conventional teacher-student learning performs unsatisfactorily for knowledge transfer from a multi-modality network to a single-modality network. We thus present a mutual learning framework by introducing a single-modal transfer loss and a cross-modal transfer loss to collaboratively learn the audio-visual correlations between the two networks. Our proposed solution takes the advantages of joint audio-visual analysis in training while maximizing the feasibility of the model in use cases. Our extensive experiments on the DCASE17 and the DCASE18 sound event detection datasets show that our proposed method outperforms the state-of-the-art audio tagging approaches.
Regret Bounds for Online Kernel Selection in Continuous Kernel Space

Regret bounds of online kernel selection in a finite kernel set have been well studied, having at least an order O( √ NT) of magnitude after T rounds, where N is the number of candidate kernels. But it is still an unsolved problem to achieve sublinear regret bounds of online kernel selection in a continuous kernel space under different learning frameworks. In this paper, to represent different learning frameworks of online kernel selection, we divide online kernel selection approaches in a continuous kernel space into two categories according to the order of selection and training at each round. Then we construct a surrogate hypothesis space that contains all the candidate kernels with bounded norms and inner products, representing the continuously varying hypothesis space. Finally, we decompose the regrets of the proposed online kernel selection categories into different types of instantaneous regrets in the surrogate hypothesis space, and derive optimal regret bounds of order O( √ T) of magnitude under mild assumptions, independent of the cardinality of the continuous kernel space. Empirical studies verified the correctness of the theoretical regret analyses.
PenDer: Incorporating Shape Constraints via Penalized Derivatives

When deploying machine learning models in the real-world, system designers may wish that models exhibit certain shape behavior, i.e., model outputs follow a particular shape with respect to input features. Trends such as monotonicity, convexity, diminishing or accelerating returns are some of the desired shapes. Presence of these shapes makes the model more interpretable for the system designers, and adequately fair for the customers. We notice that many such common shapes are related to derivatives, and propose a new approach, PenDer (Penalizing Derivatives), which incorporates these shape constraints by penalizing the derivatives. We further present an Augmented Lagrangian Method (ALM) to solve this constrained optimization problem. Experiments on three real-world datasets illustrate that even though both PenDer and state-of-the-art Lattice models achieve similar conformance to shape, PenDer captures better sensitivity of prediction with respect to intended features. We also demonstrate that PenDer achieves better test performance than Lattice while enforcing more desirable shape behavior.
NTRU leads to Anonymous, Robust Public-Key Encryption

This short note shows that NTRU in NIST PQC Round 3 finalist is anonymous in the QROM if the underlying NTRU PKE is strongly disjoint-simulatable and a hybrid PKE scheme constructed from NTRU as KEM and appropriate DEM is anonymous and robust. This solves the open problem to investigate the anonymity and robustness of NTRU posed by Grubbs, Maram, and Paterson (Cryptography ePrint Archive 2021/708).
Branch and Price for Bus Driver Scheduling with Complex Break Constraints

This paper presents a Branch and Price approach for a real-life Bus Driver Scheduling problem with a complex set of break constraints. The column generation uses a set partitioning model as master problem and a resource constrained shortest path problem as subproblem. Due to the complex constraints, the branch and price algorithm adopts several novel ideas to improve the column generation in the presence of a high-dimensional subproblem, including exponential arc throttling and a dedicated two-stage dominance algorithm. Evaluation on a publicly available set of benchmark instances shows that the approach provides the first provably optimal solutions for small instances, improving best-known solutions or proving them optimal for 48 out of 50 instances, and yielding an optimality gap of less than 1% for more than half the instances.
Towards Generalized Implementation of Wasserstein Distance in GANs

Wasserstein GANs (WGANs), built upon the Kantorovich-Rubinstein (KR) duality of Wasserstein distance, is one of the most theoretically sound GAN models. However, in practice it does not always outperform other variants of GANs. This is mostly due to the imperfect implementation of the Lipschitz condition required by the KR duality. Extensive work has been done in the community with different implementations of the Lipschitz constraint, which, however, is still hard to satisfy the restriction perfectly in practice. In this paper, we argue that the strong Lipschitz constraint might be unnecessary for optimization. Instead, we take a step back and try to relax the Lipschitz constraint. Theoretically, we first demonstrate a more general dual form of the Wasserstein distance called the Sobolev duality, which relaxes the Lipschitz constraint but still maintains the favorable gradient property of the Wasserstein distance. Moreover, we show that the KR duality is actually a special case of the Sobolev duality. Based on the relaxed duality, we further propose a generalized WGAN training scheme named Sobolev Wasserstein GAN, and empirically demonstrate the improvement over existing methods with extensive experiments.
Investigating energy transport in high density plasmas using buried layer targets

The work presented in this thesis investigates energy transport in laser irradiated solid targets containing a diagnostic buried iron layer. Energy transport in laser-plasmas is important to inertial confinement fusion and other applications, for example laser ablation, particle acceleration and x-ray production. The steep temperature and density gradients between the critical density (maximum penetration density for the laser) and ablation surface, plus the role of fast electron and radiation make energy transport in laser-plasmas a complex, non-linear issue. Laser energy can be transported into a solid target by thermal conduction, hot electron heating and radiation transport. To understand the in- terplay between these non-linear heating processes it is important to accurately characterise plasma conditions as the energy transport occurs. An experiment conducted at the Lawrence Livermore National Laboratory, USA irradiated buried iron layer targets using a 2 ps, 1017 Wcm−2 laser with the subsequent L-shell iron emission recorded using a high resolution (resolving power ≃ 500) grating spectrometer. The HYADES 1D hydrodynamic fluid code and the PrismSPECT collisional-radiative code were used to simulate the plasma conditions and the L-shell iron emission. A comparison between the simulated spectra and experi- mentally recorded L-shell emission suggests that the iron layer is heated instanta- neously by hot electrons and radiation transport and that this modifies thermal electron conduction. The thermal flux limiter and laser energy-hot electron conversion efficiency have been determined by comparing experimentally recorded L-shell emission to simulated synthetic spectra. As the iron layer expands and cools, the population of lower ionisation states increases. A novel technique has been developed to characterise the electron temperature and density from L-shell emission spectra using the Saha-Boltzmann equation and multiple line ratios of adjacent ionisation states. An experiment at the LASERIX facility, France used an extreme ultraviolet (EUV) laser as a back-lighter, to probe high density laser irradiated buried iron layer targets. The transmission through the iron layer was simulated using TOPS, PROPACEOS, IMP and HYADES opacity models. This investigation has found that higher opacities are required for plasmas at 20 eV and 0.3 gcm−3 in order to account for the drop in transmission at 20 ps after laser irradiation. Radiation transport dominates the heating of the buried iron layer when irradiated by a well defined prepulse. The expanding coronal preplasma efficiently produces hot electrons, however because of the larger stopping distance associated with ’superthermal’ electrons, the heating due to hot electrons is negligible compared to the radiation heating effect.
The Ethical Practices Among Tourism Workers in The First and Second Congressional Districts of Zamboanga Del Norte A Basis for Proposed Intervention Program for Local Tourism

The study aimed to determine the significant difference between the profile and the ethical practices of tourism workers in the first and second congressional districts of Zamboanga del Norte as basis for a proposed intervention program. To find out the significant difference between the profile of tourism workers of first and second congressional districts of Zamboanga del Norte and their ethical practices. Data were collected from (110) respondents from various cities and municipalities police stations in Zamboanga del Norte using purposive sampling, frequency count percentage, weighted mean, chi-square, and standardized Residual Analysis. On the other hand, based on the findings, the study established an intervention program for tourism workers in Zamboanga del Norte to maintain their practices of high ethical standards, and educational institutions may conduct appropriate pieces of training and programs on ethical principles. Furthermore, findings revealed that there was no significant difference when the demographic profile of tourism workers in the first and second congressional districts of Zamboanga del Norte and their ethical practices were analyzed, and no residual analysis of the study was discovered.
Cardiorespiratory Monitoring of Red Blood Cell Transfusion in Preterm Infants

Jarinda A. Poppe (  j.poppe@erasmusmc.nl ) Erasmus MC Sophia Children Hospital: Erasmus MC Sophia https://orcid.org/0000-0003-3573-5157 Tanja van Essen Erasmus MC Sophia Children Hospital: Erasmus MC Sophia Willem van Weteringen Erasmus MC Sophia Children Hospital: Erasmus MC Sophia Sten P. Willemsen Erasmus MC Sophia Children Hospital: Erasmus MC Sophia Irwin K.M. Reiss Erasmus MC Sophia Children Hospital: Erasmus MC Sophia Sinno H.P. Simons Erasmus MC Sophia Children Hospital: Erasmus MC Sophia Rogier C.J. de Jonge Erasmus MC Sophia Children Hospital: Erasmus MC Sophia