Miracle-dz/UniFAR-FTUs huggingface dataset:
Resonance Structure of 8Be within the Two-Cluster Resonating Group Method

A microscopic two-cluster model is applied to study the elastic alpha-alpha scattering and the resonance structure of 8Be. The model is an algebraic version of the Resonating Group Method (RGM), which involves the complete set of oscillator functions to expand the wave function of a two-cluster system. The interaction of nucleons inside each cluster and the interaction between clusters are determined by the well-known semirealistic nucleon-nucleon potentials which are employed in calculations. They differ by a size of the core at small distances between nucleons and realize the strong, moderate, and weak cores. They allow us to study dependence of calculated quantities on the shape of a nucleon-nucleon potential. The detailed analysis of resonance wave functions is carried out in the oscillator, coordinate, and momentum spaces. Effects of the Pauli principle on the wave functions of the 8Be continuous spectrum states are thoroughly studied.
Effects of Polymerization and Nucleotide Identity on the Conformational Dynamics of the Bacterial Actin Homolog MreB

853-Pos Board B608 CaMKII Actomyosin Interactions in Live Cells Shahid M. Khan1, Ianina Conte2, Tom Carter2, Justin E. Molloy2. Molecular Biology Consortium, Berkeley, CA, USA, Physical Biochemistry. MRC National Institute for Medical Research, London, United Kingdom. Synaptic stimulation leads to expansion of the dendritic spine caused by rearrangement of the actin cytoskeleton triggered by phosphorylation of the calcium calmodulin dependent kinase (CaMKII) and by myosin II activity. To study CaMKII-actomyosin interactions we have visualized single GFP-tagged CaMKII holoenzymes bound to tagRFP-tagged cortical actin in human endothelial cells by total internal reflection fluorescencemicroscopy. CaMKII phosphorylationmutants were used to examinewhether release of CaMKII sequestered actin andmyosin II inducedF-actin fragmentation can explain the timing and extent of spine expansion. Thus far, we have found (i) Single GFP-CaMKII holoenzymes were resolved; in contrast to the less intense and rapidlymobile single GFPmolecules. (ii) The holoenzymes have unimodal intensity distributions with< 30% standard deviation. The constitutively-active CaMKII T286D mutant, the sole exception, gave bimodal distributions with the higher mode six times as intense as the lower one. Subunit stoichiometries were determined by comparison against immobilized GFP single molecule intensities. (iii) CaMKII dwell-time distributions on stress fibers and filopodial structures were characterized. Dissociation rates were also measured by evanescent irradiation of photo-activatable GFP-CaMKII constructs bound to cortical actin (iv) The green / red fluorescence ratio of the actin structures relative to background gave an equilibrium measure of CaMKII binding. Affinity differences for different CaMKII isoforms and different phosphorylation states were estimated from the fluorescence ratios. In addition, single particle tracking was used to measure transient binding to the actin cytoskeleton. (v) We are currently measuring spatial co-operativity in the F-actin CaMKII binding sites and extending the methodology to analyse CaMKII diffusion along dendrites and spines in hippocampal neuron cultures. Our single molecule imaging data reveal spatial and macromolecular heterogeneities that may underlie aspects of the signal transduction response and cytoskeletal dynamics that are important for synaptic plasticity.
Economics of reducing fungicide use by weather‐based disease forecasts for control of Venturia inaequalis in apples

Abstract In seven field trials conducted over four seasons, black spot incidence was consistently greater in treatments where fungicide use was reduced by timing sprays with weather information compared to standard calendar‐based treatments. Fungicide use was measured as the total number of label‐rate applications of fungicides with activity against black spot and the individual components of mixed applications of fungicides were counted separately. Over all trials, the mean disease incidence at harvest increased logarithmically as the number of fungicide applications decreased. A regression equation describing disease increase in terms of number of fungicides and number of infection periods was used in an economic analysis of apple production. When the savings from reduced fungicide use were weighed against the increased harvesting and grading costs and revenue losses from increased disease, there appeared to be scope to reduce fungicide use by up to 25% before increased disease adversely affected profit.
Recent advances in the use of protein transduction domains for the delivery of peptides, proteins and nucleic acids invivo

Protein transduction domains (PTDs) are small cationic peptides that can facilitate the uptake of large, biologically active molecules into mammalian cells. Recent reports have shown that PTDs can mediate the delivery of cargo to tissues throughout a living organism. Such technology could eliminate the size restrictions on usable drugs, so enabling previously unavailable large molecules to modulate invivo biology and alleviate disease. This article will review the evidence that PTDs can be used both to deliver active molecules to pathological tissue invivo and to treat models of disease such as cancer, ischaemia and inflammation.
Learning Inconsistent Preferences with Kernel Methods

We propose a probabilistic kernel approach for preferential learning from pairwise duelling data using Gaussian Processes. Different from previous methods, we do not impose a total order on the item space, hence can capture more expressive latent preferential structures such as inconsistent preferences and clusters of comparable items. Furthermore, we prove the universality of the proposed kernels, i.e. that the corresponding reproducing kernel Hilbert Space (RKHS) is dense in the space of skew-symmetric preference functions. To conclude the paper, we provide an extensive set of numerical experiments on simulated and real-world datasets showcasing the competitiveness of our proposed method with state-of-the-art.
AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference

Hybrid Privacy-Preserving Neural Network (HPPNN) implementing linear layers by Homomorphic Encryption (HE) and nonlinear layers by Garbled Circuit (GC) is one of the most promising secure solutions to emerging Machine Learning as a Service (MLaaS). Unfortunately, a HPPNN suffers from long inference latency, e.g., $\sim100$ seconds per image, which makes MLaaS unsatisfactory. Because HE-based linear layers of a HPPNN cost $93\%$ inference latency, it is critical to select a set of HE parameters to minimize computational overhead of linear layers. Prior HPPNNs over-pessimistically select huge HE parameters to maintain large noise budgets, since they use the same set of HE parameters for an entire network and ignore the error tolerance capability of a network. In this paper, for fast and accurate secure neural network inference, we propose an automated layer-wise parameter selector, AutoPrivacy, that leverages deep reinforcement learning to automatically determine a set of HE parameters for each linear layer in a HPPNN. The learning-based HE parameter selection policy outperforms conventional rule-based HE parameter selection policy. Compared to prior HPPNNs, AutoPrivacy-optimized HPPNNs reduce inference latency by $53\%\sim70\%$ with negligible loss of accuracy.
Cracking the Black Box: Distilling Deep Sports Analytics

This paper addresses the trade-off between Accuracy and Transparency for deep learning applied to sports analytics. Neural nets achieve great predictive accuracy through deep learning, and are popular in sports analytics. But it is hard to interpret a neural net model and harder still to extract actionable insights from the knowledge implicit in it. Therefore, we built a simple and transparent model that mimics the output of the original deep learning model and represents the learned knowledge in an explicit interpretable way. Our mimic model is a linear model tree, which combines a collection of linear models with a regression-tree structure. The tree version of a neural network achieves high fidelity, explains itself, and produces insights for expert stakeholders such as athletes and coaches. We propose and compare several scalable model tree learning heuristics to address the computational challenge from datasets with millions of data points.
Provably Stable Interpretable Encodings of Context Free Grammars in RNNs with a Differentiable Stack

Given a collection of strings belonging to a context free grammar (CFG) and another collection of strings not belonging to the CFG, how might one infer the grammar? This is the problem of grammatical inference. Since CFGs are the languages recognized by pushdown automata (PDA), it suffices to determine the state transition rules and stack action rules of the corresponding PDA. An approach would be to train a recurrent neural network (RNN) to classify the sample data and attempt to extract these PDA rules. But neural networks are not a priori aware of the structure of a PDA and would likely require many samples to infer this structure. Furthermore, extracting the PDA rules from the RNN is nontrivial. We build a RNN specifically structured like a PDA, where weights correspond directly to the PDA rules. This requires a stack architecture that is somehow differentiable (to enable gradient-based learning) and stable (an unstable stack will show deteriorating performance with longer strings). We propose a stack architecture that is differentiable and that provably exhibits orbital stability. Using this stack, we construct a neural network that provably approximates a PDA for strings of arbitrary length. Moreover, our model and method of proof can easily be generalized to other state machines, such as a Turing Machine.
Nitrate entry and nitrite formation in the infected region of soybean nodules

The entry of nitrate into the infected region of soybean nodules and the possibility of a subsequent nitrite accumulation was studied. Nitrate was observed to gain access to the infected region in the short-term and significant amounts could be measured within 2 d of nitrate supply. The availability of nitrate in the bacteroid-containing region did not cause free-nitrite accumulation for at least 8 d. However, to avoid the artefactual production of nitrite during extraction it was necessary to disrupt nodules in the presence of zinc acetate and ethanol, to prevent bacteroid nitrate reductase activity. Nitrite rapidly accumulated if nodules were extracted without prior enzyme-inactivation, or if bacteroids were allowed access to nitrate, or, more significantly, if nodules were not extracted immediately following detachment. Nitrate accumulation in detached nodules was mediated by oxygen concentration within the nodule; in the presence of pure N 2 gas, nitrite accumulation was three times greater than in air and, conversely, it was prevented by exposure to pure O 2 . Furthermore, nitrite produced in detached nodules under atmospheric conditions was scavenged by transfering these nodules into 100% oxygen. However, measurements of apparent functional leghaemoglobin, using a nodule oximeter, suggested that after 8 d nitrate exposure up to 83% of Lb activity was lost, possibly due to interactions with nitrite produced in the nodule interior leading to the formation of nitrosylleghaemoglobin.
IMPLICATIONS OF ENDEMICS-AREA RELATIONSHIPS FOR ESTIMATES OF SPECIES EXTINCTIONS

Species are disappearing at unprecedented rates because of habitat destruc- tion. Lack of detailed knowledge about total numbers of species and their global or regional distributions, however, makes it difficult to quantify extinction rates precisely. Current estimates of extinction rates attributed to habitat destruction generally rely on species-area relationships. Many of the predictions based on species-area relationships, however, appear to overestimate the extent of current species extinction. In a previous paper, we used the species-area relationship as our starting point to derive a relationship for the areal distri- bution of endemics within a habitat, or an "endemics-area relationship. " The endemics- area relationship is logically and mathematically consistent with the species-area relation- ship, but it provides additional information about the distribution of species within a biome. In this paper, we use the endemics-area relationship to improve estimates of species ex- tinction rates attributed to habitat destruction or conversion. At low levels of habitat de- struction, estimates of species loss using the endemics-area relationship are significantly lower than existing estimates, but a rapid rise in predicted species loss when a threshold of habitat loss is exceeded suggests that extrapolation of recent rates of species loss may underestimate future species extinctions under continued land clearing.
Odour conditioning of ovipositor probing in a parasitic wasp

We investigated the occurrence of odour learning in the ovipositor probing activity of Leptopilina boulardi, a parasitoid of Drosophila larvae. A device was set up to observe the probing behaviour in response to an odour. Systematic and persistent probing in response to the odour (banana) was observed after associative conditioning, i.e., after the odour had been experienced while ovipositing. The percentage of females responding to the odour was also increased after oviposition experience alone or preceded by odour exposure. The induced responses were then less persistent than after associative conditioning, and also less selective because probing was triggered as a consequence of oviposition experience and not only upon odour delivery. This is the first demonstration of the role and nature of odour learning in ovipositor probing behaviour, the final step of host location by Leptopilina sp. , and this activity appears to be much modulated by the wasps experience.
Simple Primary Colour Editing for Consumer Product Images

We present a simple primary colour editing method for consumer product images. We show that by using colour correction and colour blending, we can automate the pain-staking colour editing task and save time for consumer colour preference researchers. To improve the colour harmony between the primary colour and its complementary colours, our algorithm also tunes the other colours in the image. A preliminary experiment has shown some promising results compared with a state-of-the-art method and human editing.
Responsive Web User Interface to Recover Training Data from User Gradients in Federated Learning

Local differential privacy (LDP) is an emerging privacy standard to protect individual user data. One scenario where LDP can be applied is federated learning, where each user sends in his/her user gradients to an aggregator who uses these gradients to perform stochastic gradient descent. In a case where the aggregator is untrusted and LDP is not applied to each user gradient, the aggregator can recover sensitive user data from these gradients. In this paper, we present a new interactive web demo showcasing the power of local differential privacy by visualizing federated learning with local differential privacy. Moreover, the live demo shows how LDP can prevent untrusted aggregators from recovering sensitive training data. A measure called the exp-hamming recovery is also created to show the extent of how much data the aggregator can recover.
The reproductive organs of the prosobranch mollusk Crepidula onyx and their transformation during the change from male to female phase

In previous papers the writer (Coc, '35, '36, '38 a, '38 b) and othcrs (Iilcinsteubcr, '13 : Giese, '15; Gould, '17; Ishiki, '36, '39 ; Noritz, '38) have described the gonads and associated sexual organs in several species of this and related genera, including thc transformations which occur during the change of sexual phase. Yet the morphological and physiological changes which occur during the transformation of sex in thcse mollusks are so profound and so ~iiiique that it rriay be of interest to subject them to more thorough examination. Cerpidula onyx Sowerby and its deviating habitat-form C. rugosa Nuttall are abundant along the coast of Southern California. Thc former is usually found lying on muddy bottoms in pyramidal groups of thrce to sixteen superimposed individuals, while the latter occurs singly or in groups of two to four attached to large rnollusk shells and rocks (Coe, '38 b). I n this species as in several othcrs of the genus, the cggs are laid in gelatinous capsules and incubated for a period of about 2 to 3 weeks 'ncneath tlic body of the parent. The
Finger Texture Biometric Characteristic: a Survey

\begin{abstract} In recent years, the Finger Texture (FT) has attracted considerable attention as a biometric characteristic. It can provide efficient human recognition performance, because it has different human-specific features of apparent lines, wrinkles and ridges distributed along the inner surface of all fingers. Also, such pattern structures are reliable, unique and remain stable throughout a human's life. Efficient biometric systems can be established based only on FTs. In this paper, a comprehensive survey of the relevant FT studies is presented. We also summarise the main drawbacks and obstacles of employing the FT as a biometric characteristic, and provide useful suggestions to further improve the work on FT. \end{abstract}
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton

In recent years, a variety of gradient-based first-order methods have been developed to solve bi-level optimization problems for learning applications. However, theoretical guarantees of these existing approaches heavily rely on the simplification that for each fixed upper-level variable, the lower-level solution must be a singleton (a.k.a., Lower-Level Singleton, LLS). In this work, we first design a counter-example to illustrate the invalidation of such LLS condition. Then by formulating BLPs from the view point of optimistic bi-level and aggregating hierarchical objective information, we establish Bi-level Descent Aggregation (BDA), a flexible and modularized algorithmic framework for generic bi-level optimization. Theoretically, we derive a new methodology to prove the convergence of BDA without the LLS condition. Our investigations also demonstrate that BDA is indeed compatible to a verify of particular first-order computation modules. Additionally, as an interesting byproduct, we also improve these conventional first-order bi-level schemes (under the LLS simplification). Particularly, we establish their convergences with weaker assumptions. Extensive experiments justify our theoretical results and demonstrate the superiority of the proposed BDA for different tasks, including hyper-parameter optimization and meta learning.
Learning Illumination Patterns for Coded Diffraction Phase Retrieval.

Signal recovery from nonlinear measurements involves solving an iterative optimization problem. In this paper, we present a framework to optimize the sensing parameters to improve the quality of the signal recovered by the given iterative method. In particular, we learn illumination patterns to recover signals from coded diffraction patterns using a fixed-cost alternating minimization-based phase retrieval method. Coded diffraction phase retrieval is a physically realistic system in which the signal is first modulated by a sequence of codes before the sensor records its Fourier amplitude. We represent the phase retrieval method as an unrolled network with a fixed number of layers and minimize the recovery error by optimizing over the measurement parameters. Since the number of iterations/layers are fixed, the recovery incurs a fixed cost. We present extensive simulation results on a variety of datasets under different conditions and a comparison with existing methods. Our results demonstrate that the proposed method provides near-perfect reconstruction using patterns learned with a small number of training images. Our proposed method provides significant improvements over existing methods both in terms of accuracy and speed.
Multi-Task Reinforcement Learning based Mobile Manipulation Control for Dynamic Object Tracking and Grasping

Agile control of mobile manipulator is challenging because of the high complexity coupled by the robotic system and the unstructured working environment. Tracking and grasping a dynamic object with a random trajectory is even harder. In this paper, a multi-task reinforcement learning-based mobile manipulation control framework is proposed to achieve general dynamic object tracking and grasping. Several basic types of dynamic trajectories are chosen as the training set for the task. To improve policy generalization in practice, random noise and dynamics randomization are introduced during the training process. Extensive experiments show that our trained policy can adapt to unseen random dynamic trajectories with about 0.1 m tracking error and 75% grasping success rate for dynamic objects. The trained policy can also be successfully deployed on a real mobile manipulator.
Online Advertising Security: Issues, Taxonomy, and Future Directions

Online advertising has become the backbone of the Internet economy by revolutionizing business marketing. It provides a simple and efficient way for advertisers to display their advertisements to specific individual users, and over the last couple of years has contributed to an explosion in the income stream for several Web-based businesses. For example, Google’s income from advertising grew 51.6% between 2016 and 2018, to $\$ $ 136.8 billion. This exponential growth in advertising revenue has motivated fraudsters to exploit the weaknesses of the online advertising model to make money, and researchers to discover new security vulnerabilities in the model, to propose countermeasures and to forecast future trends in research. Motivated by these considerations, this paper presents a comprehensive review of the security threats to online advertising systems. We begin by introducing the motivation for online advertising system, explain how it differs from traditional advertising networks, introduce terminology, and define the current online advertising architecture. We then devise a comprehensive taxonomy of attacks on online advertising to raise awareness among researchers about the vulnerabilities of online advertising ecosystem. We discuss the limitations and effectiveness of the countermeasures that have been developed to secure entities in the advertising ecosystem against these attacks. To complete our work, we identify some open issues and outline some possible directions for future research towards improving security methods for online advertising systems.
Global Robustness Verification Networks

The wide deployment of deep neural networks, though achieving great success in many domains, has severe safety and reliability concerns. Existing adversarial attack generation and automatic verification techniques cannot formally verify whether a network is globally robust, i.e., the absence or not of adversarial examples in the input space. To address this problem, we develop a global robustness verification framework with three components: 1) a novel rule-based ``back-propagation'' finding which input region is responsible for the class assignment by logic reasoning; 2) a new network architecture Sliding Door Network (SDN) enabling feasible rule-based ``back-propagation''; 3) a region-based global robustness verification (RGRV) approach. Moreover, we demonstrate the effectiveness of our approach on both synthetic and real datasets.