Miracle-dz/UniFAR-FTUs huggingface dataset:
Geometric Multimodal Contrastive Representation Learning

Learning representations of multimodal data that are both informative and robust to missing modalities at test time remains a challenging problem due to the inherent heterogeneity of data obtained from different channels. To address it, we present a novel Geometric Multimodal Contrastive (GMC) representation learning method consisting of two main components: i) a two-level architecture consisting of modality-speciﬁc base encoders, allowing to process an arbitrary number of modalities to an intermediate representation of ﬁxed dimensionality, and a shared projection head, mapping the intermediate representations to a latent representation space; ii) a multimodal contrastive loss function that encourages the geometric alignment of the learned representations. We experimentally demonstrate that GMC representations are semantically rich and achieve state-of-the-art performance with missing modality information on three different learning problems including prediction and reinforcement learning tasks.
pathGCN: Learning General Graph Spatial Operators from Paths

Graph Convolutional Networks (GCNs), similarly to Convolutional Neural Networks (CNNs), are typically based on two main operations - spatial and point-wise convolutions. In the context of GCNs, differently from CNNs, a pre-determined spatial operator based on the graph Laplacian is often chosen, allowing only the point-wise operations to be learnt. However, learning a meaningful spatial operator is critical for developing more expressive GCNs for improved performance. In this paper we propose pathGCN, a novel approach to learn the spatial operator from random paths on the graph. We analyze the convergence of our method and its difference from existing GCNs. Furthermore, we discuss several options of combining our learnt spatial operator with point-wise convolutions. Our extensive experiments on numerous datasets suggest that by properly learning both the spatial and point-wise convolutions, phenomena like over-smoothing can be inherently avoided, and new state-of-the-art performance is achieved.
Multi-slots Online Matching with High Entropy

Online matching with diversity and fairness pursuit, a common building block in the recommendation and advertising, can be modeled as constrained convex programming with high entropy. While most existing approaches are based on the “single slot” assumption (i.e., assigning one item per iteration), they cannot be directly applied to cases with multiple slots, e.g., stock-aware top-N recommendation and advertising at multiple places. Particularly, the gradient computation and resource allocation are both challenging under this setting due to the absence of a closed-form solu-tion. To overcome these obstacles, we develop a novel algorithm named Online subGradient descent for Multi-slots Allocation (OG-MA). It uses an efficient pooling algorithm to compute closed-form of the gradient then performs a roulette swap-ping for allocation, yielding a sub-linear regret with linear cost per iteration. Extensive experiments on synthetic and industrial data sets demonstrate that OG-MA is a fast and promising method for multi-slots online matching.
Evofosfamide and Gemcitabine Act Synergistically in Pancreatic Cancer Xenografts by Dual Action on Tumor Vasculature and Inhibition of Homologous Recombination DNA Repair

Aims Pancreatic ductal adenocarcinomas (PDACs) form hypovascular and hypoxic tumors which are difficult to treat with current chemotherapy regimens. Gemcitabine (GEM) is often used as a first line treatment for PDACs, but has issues with chemoresistance and penetration in the interior of the tumor. Evofosfamide, a hypoxia activated prodrug, has been shown to be effective in combination with GEM, although the mechanism of each drug on the other has not been established. We used two mouse xenografts from two cell lines (MIA Paca-2 and SU 86.86) with different tumor microenvironmental characteristics to probe the action of each drug on the other. Results GEM treatment enhanced survival times in mice with SU.86.86 xenografts (HR =0.35, 95% CI=0.13 to 0.90 p=0.03) but had no effect on MIA Paca-2 mice (HR =0.91, 95% CI=0.37 to 2.25, p=0.84). Conversely, evofosfamide had no effect on SU86.86 mice and did not improve survival times to a statistically significant degree (HR=0.57, 95% CI=0.23 to 1.42, p=0.22). In MIA Paca-2 tumors, which were initially poorly perfused, electron paramagnetic resonance (EPR) imaging showed that oxygenation worsened when treated with GEM, providing a direct mechanism for the activation of evofosfamide by GEM and the effectiveness of evofosfamide and GEM combinations. Sublethal amounts of either treatment enhanced the toxicity of other treatment in vitro in Su86.86 but not in MIAPaca-2. Repair of double stranded DNA lesions was enhanced in the combination treatment in Su86.86 but not MIA Paca-2. Innovations A possible mechanism for the synergy between evofosfamide and GEM has been proposed. Conclusion The synergy between GEM and evofosfamide appears to stem from the dual action of GEM’s effect on tumor vasculature and the GEM inhibition of the homologous recombination DNA repair process. The relative importance of each pathway is dependent on the tumor microenvironment and merits further study.
Kinetics of radiation-induced DNA double-strand breaks through coarse-grained simulations

Double-strand breaks (DSBs), i.e. the covalent cut of the DNA backbone over both strands, are a detrimental outcome of cell irradiation, bearing chromosomal aberrations and leading to cell apoptosis. In the early stages of the evolution of a DSB, the disruption of the residual interactions between the DNA moieties drives the fracture of the helical layout; in spite of its biological significance, the details of this process are still largely uncertain. Here, we address the mechanical rupture of DNA by DSBs via coarse-grained molecular dynamics simulations: the setup involves a 3855-bp DNA filament and diverse DSB motifs, i.e. within a range of distances between strand breaks (or DSB distance). By employing a coarse-grained model of DNA, we access the molecular details and characteristic timescales of the rupturing process. A sequence-nonspecific, linear correlation is observed between the DSB distance and the internal energy contribution to the disruption of the residual (Watson-Crick and stacking) contacts between DNA moieties, which is seemingly driven by an abrupt, cooperative process. Moreover, we infer an exponential dependence of the characteristic rupture times on the DSB distances, which we associate to an Arrhenius law of thermally-activated processes. This work lays the foundations of a detailed, mechanistic assessment of DSBs in silico, as a benchmark to both numerical simulations and data from single molecule experiments.
Drug dependence in cancer is exploitable by optimally constructed treatment holidays

Cancers with acquired resistance to targeted therapy can become simultaneously dependent on the presence of the targeted therapy drug for survival, suggesting that intermittent therapy may slow resistance. However, relatively little is known about which tumours are likely to become dependent and how to schedule intermittent therapy optimally. Here we characterized drug dependence across a panel of over 75 MAPK-inhibitor-resistant BRAF ^V600E mutant melanoma models at the population and single-clone levels. Melanocytic differentiated models exhibited a much greater tendency to give rise to drug-dependent progeny than their dedifferentiated counterparts. Mechanistically, acquired loss of microphthalmia-associated transcription factor in differentiated melanoma models drives ERK–JunB–p21 signalling to enforce drug dependence. We identified the optimal scheduling of ‘drug holidays’ using simple mathematical models that we validated across short and long timescales. Without detailed knowledge of tumour characteristics, we found that a simple adaptive therapy protocol can produce near-optimal outcomes using only measurements of total population size. Finally, a spatial agent-based model showed that optimal schedules derived from exponentially growing cells in culture remain nearly optimal in the context of tumour cell turnover and limited environmental carrying capacity. These findings may guide the implementation of improved evolution-inspired treatment strategies for drug-dependent cancers. Melanoma cell lines are used to identify the tumour characteristics that increase the chances of drug dependency, and mathematical modelling shows that this can be exploited for treatment using drug holidays with only measurements of total population size required for near optimality.
Oligodendrocyte-mediated myelin plasticity and its role in neural synchronization

Temporal synchrony of signals arriving from different neurons or brain regions is essential for proper neural processing. Nevertheless, it is not well understood how such synchrony is achieved and maintained in a complex network of time-delayed neural interactions. Myelin plasticity has been suggested as an efficient mechanism for controlling timing in brain communications through adaptive changes of axonal conduction velocity and consequently conduction time delays, or latencies; however, local rules and feedback mechanisms that oligodendrocytes (OL) use to achieve synchronization are not known. We propose a mathematical model of oligodendrocyte-mediated myelin plasticity (OMP) in which OL play an active role in providing such feedback. This is achieved without using arrival times at the synapse or modulatory signaling from astrocytes; instead, it relies on the presence of global and transient OL responses to local action potentials in the axons they myelinate. While inspired by OL morphology, we provide the theoretical underpinnings that motivated the model and explore its performance for a wide range of its parameters. Our results indicate that when the characteristic time of OL’s spike responses is between 10 and 40 ms and the firing rates in individual axons are relatively low (⪅ 10 Hz), the OMP model efficiently synchronizes correlated and time-locked signals while latencies in axons carrying independent signals are unaffected. This suggests a novel form of selective synchronization in the CNS in which oligodendrocytes play an active role by modulating the conduction delays of correlated spike trains as they traverse to their targets. Significance Statement Synchronization of signals arriving from different neurons or brain regions is of great importance for proper brain function. In vertebrates, myelination provides an efficient way to achieve this by adjusting the conduction velocity and consequently the conduction delays. It is increasingly evident that myelination is an adaptive process and involved in learning; however, the local rules and the feedback that guide oligodendrocytes towards achieving desired delays are not known. Here we propose a simple and biologically plausible model of myelin plasticity in which oligodendrocytes play an active role in providing the needed feedback. We use theoretical arguments, mathematical modeling and computer simulations to show its robust synchronization capabilities which enable efficient communication between the brain regions.
The cost of information acquisition by natural selection

Natural selection enriches genotypes that are well-adapted to their environment. Over successive generations, these changes to the frequencies of types accumulate information about the selective conditions. Thus, we can think of selection as an algorithm by which populations acquire information about their environment. Kimura (1961) pointed out that every bit of information that the population gains this way comes with a minimum cost in terms of unrealized fitness (substitution load). Due to the gradual nature of selection and ongoing mismatch of types with the environment, a population that is still gaining information about the environment has lower mean fitness than a counter-factual population that already has this information. This has been an influential insight, but here we find that experimental evolution of Escherichia coli with mutations in a RNA polymerase gene (rpoB) violates Kimura’s basic theory. To overcome the restrictive assumptions of Kimura’s substitution load and develop a more robust measure for the cost of selection, we turn to ideas from computational learning theory. We reframe the ‘learning problem’ faced by an evolving population as a population versus environment (PvE) game, which can be applied to settings beyond Kimura’s theory – such as stochastic environments, frequency-dependent selection, and arbitrary environmental change. We show that the learning theoretic concept of ‘regret’ measures relative lineage fitness and rigorously captures the efficiency of selection as a learning process. This lets us establish general bounds on the cost of information acquisition by natural selection. We empirically validate these bounds in our experimental system, showing that computational learning theory can account for the observations that violate Kimura’s theory. Finally, we note that natural selection is a highly effective learning process in that selection is an asymptotically optimal algorithm for the problem faced by evolving populations, and no other algorithm can consistently outperform selection in general. Our results highlight the centrality of information to natural selection and the value of computational learning theory as a perspective on evolutionary biology.
Ubiquitous nonidentifiability of state-dependent diversification models (SSEs) is not problematic for phylogenetics

A recent study (Louca and Pennell, 2020) spotlighted the issue of model congruence, or asymptotic unidentifiability, in time-dependent birth-death models used for reconstructing species diversification histories on phylogenetic trees. The phenomenon of model congruence implies that any given timetree can be equally likely explained by various diversification scenarios. The present work investigates this issue in state-dependent speciation and extinction (SSE) models, which are widely employed to study trait-dependent diversification. We demonstrate that model unidentifiability is universal for SSEs, with every SSE belonging to an infinite class of congruent models. Importantly, we find that any trait-independent model is congruent with a trait-dependent model that possesses the same or fewer parameters, raising significant concerns for model selection and hypothesis testing. To address this challenge, we propose an analytical solution and discuss its implication for comparative phylogenetics and model development. Our results indicate that while SSE congruence must be carefully considered in diversification studies, it does not require a fundamental change in phylogenetic methodologies.
Fractal-Thermodynamic system analogy and complexity of plant leaves

More precise measurements of the complexity of leaf shapes can open new pathways to understanding plant adaptation and resilience in the face of global changes. We present a method to measure the complexity of plant leaf shapes by relating their fractal dimension to topological entropy. Our method relies on ‘segmental fractal complexity’ and stems from a fractal-thermodynamic system analogy. The complexity of plant leaf shapes is an algebraic combination of the fractal dimension of the components, leaf lamina, background, and leaf edge. We applied this method to leaf forms of 42 tropical plant species. Segmental fractal complexity is an improvement over the leaf dissection index because of its ability to capture the spatial positioning of a leaf’s lamina, edge, and background. It is also an advancement over conventional geometric and fractal dimension-based measures of leaf complexity because it does not entail information loss due to the pre-processing and is perceptibly simple.
Optimization of stored energy dispatch for concentrating solar power systems

Concentrating solar power (CSP) is an emerging technology capable of generating renewable, dispatchable power using cost-effective thermal energy storage. Dispatchability imparts significant value to a power generation technology, both expanding the applications in which it is useful and – perhaps more critically – enabling the proliferation of other nondispatchable renewable technologies by supplementing their performance during transient periods of low or variable production. Current CSP technology is functional but sub-optimal, especially with regard to solar field design and to strategies for optimally dispatching power. A comprehensive, immediate, and systematic optimization approach is needed to drive down technology costs, thus improving the competitive position of CSP in the marketplace. Several tools are a necessary part of this process, including those for solar field design and optical characterization, plant productivity simulation, optimal operations scheduling (i.e., dispatch optimization), cost assessment, and financial performance evaluation. However, advancement in each critical area has been uneven to-date, with solar field design and optical characterization and dispatch optimization less established than other areas. Herein lies the challenge undertaken in this thesis – namely, to identify mathematical models and computational techniques that advance these two aspects of CSP technology development. We present methods for improved solar field design, characterization, and optimization that advance previous work by extending an existing technique for analytical flux modeling to individual heliostat reflections, enabling rapid computational prototyping of a broader range of field designs. Once defined, a plant’s dispatch schedule can be optimized to maximize revenue from electricity sales, minimize costs due to subsystem start-up or change in production, and enforce contractual or technological constraints. We undertake this task by formulating a mixed-integer linear program that more realistically and accurately accounts
WiAdv: Practical and Robust Adversarial Attack against WiFi-based Gesture Recognition System

WiFi-based gesture recognition systems have attracted enormous interest owing to the non-intrusive of WiFi signals and the wide adoption of WiFi for communication. Despite boosted performance via integrating advanced deep neural network (DNN) classifiers, there lacks sufficient investigation on their security vulnerabilities, which are rooted in the open nature of the wireless medium and the inherent defects (e.g., adversarial attacks) of classifiers. To fill this gap, we aim to study adversarial attacks to DNN-powered WiFi-based gesture recognition to encourage proper countermeasures. We design WiAdv to construct physically realizable adversarial examples to fool these systems. WiAdv features a signal synthesis scheme to craft adversarial signals with desired motion features based on the fundamental principle of WiFi-based gesture recognition, and a black-box attack scheme to handle the inconsistency between the perturbation space and the input space of the classifier caused by the in-between non-differentiable processing modules. We realize and evaluate our attack strategies against a representative state-of-the-art system, Widar3.0 in realistic settings. The experimental results show that the adversarial wireless signals generated by WiAdv achieve over 70% attack success rate on average, and remain robust and effective across different physical settings. Our attack case study and analysis reveal the vulnerability of WiFi-based gesture recognition systems, and we hope WiAdv could help promote the improvement of the relevant systems.
3D Modelling, Animation and Simulation of Mammal’s Migration Across Roads

Abstract The presented article is dealing with the new methods which are designated for data collection of mammals migrating across traffic networks. Nowadays, road construction and securing of older roads is usually accompanied by finding new solutions. Because of lack of collected data we have new opportunities how we can collect this input. The article below describes the most efficient method which is suitable for the model creation, process of creation and issues which are connected with the creation of simulations.
Roll aerodynamic characteristics study of an unmanned aerial vehicle based on circulation control technology

To explore the control efficiency of circulation flow control technology, a circulation control actuator with an independent gas source has been designed and applied in roll attitude control of a small unmanned aerial vehicle. The circulation control devices are arranged at the two ends of the wing on an unmanned aerial vehicle scale model, the changes in aerodynamic force and aerodynamic moment caused by turning on the actuator are measured in a wind tunnel, and the flow field characteristics are analysed using particle image velocimetry technology. The flight control effect of the roll attitude is verified via a flight test. Experimental and flight test results show that the control of roll attitude can be achieved by turning on the circulation control actuator on one side, and the maximum efficiency that the circulation control generates is equivalent to 8° aileron deflection with production of a favorable yaw moment to achieve a coordinated turn. The circulation control actuator can increase lift and reduce drag when opened on both sides simultaneously. The maximum lift-to-drag ratio of the UAV increased from 5 to 9, and this approach can also suppress flow separation and delay stall at high angles of attack. The aileron or trailing edge flaps can be replaced with circulation control actuators, and the circulation control technology can also be applied to aerodynamic performance improvement and flight control in other types of aircraft.
An integrated architecture for implementing extended producer responsibility in the context of Industry 4.0

Extended producer responsibility (EPR) is a regulatory measure to enforce the life cycle management of electrical and electronic equipment, however, the implementation of EPR programmes is not as effective as expected. In the face of the fourth industrial revolution that commonly labelled as ‘Industry 4.0,’ this paper proposes an integrated architecture to achieve effective and efficient EPR from the manufacturer perspective, and attention is specifically paid on promoting information sharing. On the basis of the selected case study, a smart refrigerator plant of Haier, the architecture integrates information systems and facilitates life cycle management. Particularly, eco-design and end-of-life disposal, the two lasting problems in the current practises of implementing EPR, can be enforced based on product modularisation and high level of information availability that provided by the architecture. The outcomes of this study provide a valuable reference for other sectors that involve EPR or product life cycle management.
A 4-W 56-dB Gain Microstrip Amplifier at 15 GHz Utilizing GaAs FET's and IMPATT Diodes

Performance results and design considerations are presented for an all solid-state Ku-band power amplifier which is feasible for use in PM communication systems for airborne or spacecraft transmitter applications. Design emphasis is placed on high power, and high efficiency operation as well as on compact amplifier construction. A six-stage GaAs FET preamplifier and a driver and balanced power amplifier utilizing GaAs IMPATT diodes operating in the injection locked oscillator mode are discussed. For high power and efficiency Schottky-Read IMPATT's with Iow-high-low doping profiles are employed. For improved reliability the IMPATT's incorporate a TiW barrier metallization to retard degradation of the IMPATT's. Results of accelerated life testing of the IMPATT devices are also presented.