Miracle-dz/UniFAR-FTUs huggingface dataset:
Exact Gradients and Hessians for Quantum Optimal Control and Applications in Many-Body Matrix Product States

The demand for precise control in high-fidelity regimes places increasing emphasis on the role of optimization methodologies and their performance capacities. We discuss the importance of accurate derivatives for efficiently locally traversing and converging in optimization landscapes. We find the feasibility of meeting this central requirement critically depends on the choice of propagation scheme by deriving analytically exact control derivatives. Even when exact propagation is sufficiently cheap, we find, perhaps surprisingly, that it is always more efficient to optimize the approximate propagators: approximate dynamics is traded off for complexity reductions in the exact derivative calculations. We quantitatively verify these claims for a concrete problem and find that the best schemes (Trotterization with a certain problem structure) obtain unit fidelity to machine precision, but the results from exact propagator schemes are separated consistently at least by an order of magnitude in computation time and in worst case 10 orders of magnitude in achievable fidelity. We then discuss a many-body Bose-Hubbard model in a form compliant with the best scheme in the matrix product state ansatz. With the established concepts we optimize the superfluid-Mott transition to fidelities above 0.99 and find quantum speed limit estimates beyond exact diagonalization approaches. Compared to earlier gradient-free work on similar problems, we show the simpler optimization objectives employed there are not consistent with high-fidelity requirements, and find substantial qualitative differences (bang structures) and quantitative performance improvements (orders of magnitude) and transformation times (factor three) depending on the comparative measure. We attribute the success and efficiency of our methodology to the exact derivatives, which are immediately applicable to all pure state-transfers.
The Virtual Block Interface: A Flexible Alternative to the Conventional Virtual Memory Framework

Computers continue to diversify with respect to system designs, emerging memory technologies, and application memory demands. Unfortunately, continually adapting the conventional virtual memory framework to each possible system configuration is challenging, and often results in performance loss or requires non-trivial workarounds. To address these challenges, we propose a new virtual memory framework, the Virtual Block Interface (VBI). We design VBI based on the key idea that delegating memory management duties to hardware can reduce the overheads and software complexity associated with virtual memory. VBI introduces a set of variable-sized virtual blocks (VBs) to applications. Each VB is a contiguous region of the globally-visible VBI address space, and an application can allocate each semantically meaningful unit of information (e.g., a data structure) in a separate VB. VBI decouples access protection from memory allocation and address translation. While the OS controls which programs have access to which VBs, dedicated hardware in the memory controller manages the physical memory allocation and address translation of the VBs. This approach enables several architectural optimizations to (1) efficiently and flexibly cater to different and increasingly diverse system configurations, and (2) eliminate key inefficiencies of conventional virtual memory. We demonstrate the benefits of VBI with two important use cases: (1) reducing the overheads of address translation (for both native execution and virtual machine environments), as VBI reduces the number of translation requests and associated memory accesses; and (2) two heterogeneous main memory architectures, where VBI increases the effectiveness of managing fast memory regions. For both cases, VBI significantly improves performance over conventional virtual memory.
Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting

Recently data-driven image inpainting methods have made inspiring progress, impacting fundamental image editing tasks such as object removal and damaged image repairing. These methods are more effective than classic approaches, however, due to memory limitations they can only handle low-resolution inputs, typically smaller than 1K. Meanwhile, the resolution of photos captured with mobile devices increases up to 8K. Naive up-sampling of the low-resolution inpainted result can merely yield a large yet blurry result. Whereas, adding a high-frequency residual image onto the large blurry image can generate a sharp result, rich in details and textures. Motivated by this, we propose a Contextual Residual Aggregation (CRA) mechanism that can produce high-frequency residuals for missing contents by weighted aggregating residuals from contextual patches, thus only requiring a low-resolution prediction from the network. Since convolutional layers of the neural network only need to operate on low-resolution inputs and outputs, the cost of memory and computing power is thus well suppressed. Moreover, the need for high-resolution training datasets is alleviated. In our experiments, we train the proposed model on small images with resolutions 512 × 512 and perform inference on high-resolution images, achieving compelling inpainting quality. Our model can inpaint images as large as 8K with considerable hole sizes, which is intractable with previous learning-based approaches. We further elaborate on the light-weight design of the network architecture, achieving real-time performance on 2K images on a GTX 1080 Ti GPU. Codes are available at: https://github. com/Ascend-Huawei/Ascend-Canada/tree/ master/Models/Research_HiFIll_Model
CM1 Ligation Induces Apoptosis via Fas-FasL Interaction in Ramos Cells, but via Down-regulation of Bcl-2 and Subsequent Decrease of Mitochondrial Membrane Potential in Raji Cells

CM1 (Centrocyte/-blast Marker I) defined by a mAb developed against concanavalin-A activated PBMC, is expressed specifically on a subpopulation of centroblasts and centrocytes of human germinal center (GC) B cells. Burkitt lymphoma (BL) is a tumor consisting of tumor cells with the characteristics of GC B cell. Previously we reported that CM1 ligation with anti-CM1 mAb induced apoptosis in Ramos (IgM high ) and Raji IgM low cells. Methods & Results: In the present study, we observed that CM1 ligation with anti-CM1 mAb induced Fas ligand and Fas expression in Ramos cells, but not in Raji cells. Furthermore, anti-Fas blocking antibody, ZB4, blocked CM1-mediated apoptosis effectively in Ramos cells, but not in Raji cells. Increased mitochondrial membrane permeabilization, which was measured by DiOC 6 , was observed only in Raji cells. In contrast to no significant change of Bax known as pro-apoptotic protein, anti-apoptotic protein Bcl-2 was significantly decreased in Raji cells. In addition, we observed that CM1 ligation increased release of mitochondrial cytochrome c and upregulated caspase-9 activity in Raji cells. Conclusion: These results suggest that apoptosis induced by CM1-ligation is mediated by Fas-Fas ligand interaction in Ramos cells, whereas apoptosis is mediated by down-regulation of Bcl-2 and subsequent decrease of mitochondrial membrane potential in Raji cells.
Impact of systematics on cosmological parameters from future galaxy cluster surveys

Galaxy clusters are a recent cosmological probe. The precision and accuracy of the cosmological parameters inferred from these objects are affected by the knowledge of cluster physics, entering the analysis through the mass-observable scaling relations, and the theoretical description of their mass and redshift distribution, modelled by the mass function. In this work we forecast the impact of different modelling of these ingredients for clusters detected by future optical and near-IR surveys. We consider the standard cosmological scenario and the case with a time-dependent equation of state for dark energy. We analyse the effect of increasing precision on the scaling relation calibration, finding improved constraints on the cosmological parameters. This higher precision exposes the impact of the mass function evaluation, which is a subdominant source of systematics for current data. We compare two different evaluations for the mass function. In both cosmological scenarios the use of different mass functions leads to biases in the parameter constraints. For the ΛCDM model, we find a 1. 6σ shift in the (Ωm, σ8) parameter plane and a discrepancy of ∼7σ for the redshift evolution of the scatter of the scaling relations. For the scenario with a time-evolving dark energy equation of state, the assumption of different mass functions results in a ∼8σ tension in the w0 parameter. These results show the impact, and the necessity for a precise modelling, of the interplay between the redshift evolution of the mass function and of the scaling relations in the cosmological analysis of galaxy clusters.
Purification and properties of crustacyanin

Crustacyanin, the blue carapace pigment of the common lobster Homarus gammarus (L.), has been purified and crystallized. This chromoprotein has a minimum molecular weight of 36 000 based on the content of the carotenoid prosthetic group astaxanthin. The molecular weight in gel filtration measurements is about 650 000, corresponding to some 18 molecules of astaxanthin per molecule of protein. Crustacyanin, on dialysis against water, dissociates into particles of about 35 000 molecular weight, each apparently bearing one molecule of carotenoid. The dissociation is accompanied by a shift in the principal maximum of the absorption spectrum from 633 to 595 nm and is reversed upon addition of salt. Reversible dissociation also occurs in the presence of 3 M urea, 1 M potassium thiocyanate, 10% (v/v) dioxan or 10% (v/v) acetone. When the carotenoid is removed from crustacyanin with acetone, the resultant apoprotein has a mean molecular weight of about 20 000. It may be resolved by starch gel electrophoresis into several components of which two predominate. Crustacyanin, indistinguishable from the native material, can be reconstituted from apoprotein and carotenoid. Evidence from the behaviour of crustacyanin and its apoprotein at surfaces indicates that the tertiary and quaternary structures of the native protein are stabilized by the carotenoid. It is suggested that the quaternary structure of crustacyanin is induced by an interaction of the carotenoid molecules of the subunits, which in turn causes a change in configuration of the protein favourable to aggregation. The result is a micelle-like structure with a hydrophobic carotenoid core.
Representation Learning with Fine-grained Patterns

With the development of computational power and techniques for data collection, deep learning demonstrates a superior performance over most of existing algorithms on benchmark data sets. Many efforts have been devoted to studying the mechanism of deep learning. One important observation is that deep learning can learn the discriminative patterns from raw materials directly in a task-dependent manner. Therefore, the representations obtained by deep learning outperform hand-crafted features significantly. However, those patterns are often learned from super-class labels due to a limited availability of fine-grained labels, while fine-grained patterns are desired in many real-world applications such as visual search in online shopping. To mitigate the challenge, we propose an algorithm to learn the fine-grained patterns sufficiently when only super-class labels are available. The effectiveness of our method can be guaranteed with the theoretical analysis. Extensive experiments on real-world data sets demonstrate that the proposed method can significantly improve the performance on target tasks corresponding to fine-grained classes, when only super-class information is available for training.
Epizoochorous dispersal of bryophyte stem fragments by roe deer (Capreolus capreolus) and wild boar (Sus scrofa)

Abstract Epizoochory of bryophyte stem fragments by roe deer and wild boar was studied in deciduous forests in Germany. Twenty-five shot roe deer and nine wild boar were investigated. In total, the animals transported 106 fragments (102 stem fragments and four leaves) in their coats and hooves. The fragments came from 12, mainly terricolous, bryophyte species and had a mean length of 3.6 mm (0.5–35 mm). The most abundant species were Brachythecium velutinum, Hypnum cupressiforme and Eurhynchium hians, for which epizoochorous dispersal of stem fragments was previously unknown. While slender pleurocarpous species with erect and acute leaves growing in wefts were commonly found on large mammals, robust acrocarpic species growing in tall turfs were extensively excluded because of their morphological characters. This was also shown by a roe deer dummy, which was placed 300 times on the forest floor. As the transported fragments can function as diaspores, epizoochorous transport of unspecialized gametophyte fragments may play a significant, but so far under-estimated role in the dispersal of bryophyte species, especially those without specialized asexual propagules and with rare sexual reproduction.
NOMA for Energy-Efficient LiFi-Enabled Bidirectional IoT Communication

In this paper, we consider a light fidelity (LiFi)-enabled bidirectional Internet of Things (IoT) communication system, where visible light and infrared light are used in the downlink and uplink, respectively. In order to efficiently improve the energy efficiency (EE) of the bidirectional LiFi-IoT system, non-orthogonal multiple access (NOMA) with a quality-of-service (QoS)-guaranteed optimal power allocation (OPA) strategy is applied to maximize the EE of both downlink and uplink channels. We derive closed-form OPA sets based on the identification of the optimal decoding orders in both downlink and uplink channels, which can enable low-complexity power allocation. Moreover, we propose an adaptive channel and QoS-based user pairing approach by jointly considering users’ channel gains and QoS requirements. We further analyze the EE and the user outage probability (UOP) performance of both downlink and uplink channels in the bidirectional LiFi-IoT system. Extensive analytical and simulation results demonstrate the superiority of NOMA with OPA in comparison to orthogonal multiple access (OMA) and NOMA with typical channel-based power allocation strategies. It is also shown that the proposed adaptive channel and QoS-based user pairing approach greatly outperforms individual channel/QoS-based approaches, especially when users have diverse QoS requirements.
Every Colour You Are: Stance Prediction and Turnaround in Controversial Issues

Web platforms have allowed political manifestation and debate for decades. Technology changes have brought new opportunities for expression, and the availability of longitudinal data of these debates entice new questions regarding who participates, and who updates their opinion. The aim of this work is to provide a methodology to measure these phenomena, and to test this methodology on a specific topic, abortion, as observed on one of the most popular micro-blogging platforms. To do so, we followed the discussion on Twitter about abortion in two Spanish-speaking countries from 2015 to 2018. Our main insights are two fold. On the one hand, people adopted new technologies to express their stances, particularly colored variations of heart emojis ( & ) in a way that mirrored physical manifestations on abortion. On the other hand, even on issues with strong opinions, opinions can change, and these changes show differences in demographic groups. These findings imply that debate on the Web embraces new ways of stance adherence, and that changes of opinion can be measured and characterized.
Differential mapping spiking neural network for sensor-based robot control

In this work, a spiking neural network (SNN) is proposed for approximating differential sensorimotor maps of robotic systems. The computed model is used as a local Jacobian-like projection that relates changes in sensor space to changes in motor space. The SNN consists of an input (sensory) layer and an output (motor) layer connected through plastic synapses, with inter-inhibitory connections at the output layer. Spiking neurons are modeled as Izhikevich neurons with a synaptic learning rule based on spike timing-dependent plasticity. Feedback data from proprioceptive and exteroceptive sensors are encoded and fed into the input layer through a motor babbling process. A guideline for tuning the network parameters is proposed and applied along with the particle swarm optimization technique. Our proposed control architecture takes advantage of biologically plausible tools of an SNN to achieve the target reaching task while minimizing deviations from the desired path, and consequently minimizing the execution time. Thanks to the chosen architecture and optimization of the parameters, the number of neurons and the amount of data required for training are considerably low. The SNN is capable of handling noisy sensor readings to guide the robot movements in real-time. Experimental results are presented to validate the control methodology with a vision-guided robot.
Isospectral and square-root Cholesky photonic lattices

Cholesky factorization provides photonic lattices that are the isospectral partners or the square root of other arrays of coupled waveguides. The procedure is similar to that used in supersymmetric quantum mechanics. However, Cholesky decomposition requires initial positive definite mode coupling matrices and the resulting supersymmetry is always broken. That is, the isospectral partner has the same range than the initial mode coupling matrix. It is possible to force a decomposition where the range of the partner is reduced but the characteristic supersymmetric intertwining is lost. As an example, we construct the Cholesky isospectral partner and the square root of a waveguide necklace with cyclic symmetry. We use experimental parameters from telecommunication C-band to construct a finite element model of these Cholesky photonics lattices to good agreement with our analytic prediction.
Enhancing Word Embeddings with Knowledge Extracted from Lexical Resources

In this work, we present an effective method for semantic specialization of word vector representations. To this end, we use traditional word embeddings and apply specialization methods to better capture semantic relations between words. In our approach, we leverage external knowledge from rich lexical resources such as BabelNet. We also show that our proposed post-specialization method based on an adversarial neural network with the Wasserstein distance allows to gain improvements over state-of-the-art methods on two tasks: word similarity and dialog state tracking.
MmWave Beam Management in Urban Vehicular Networks

Millimeter-wave (mmWave) communication represents a potential solution to capacity shortage in vehicular networks. However, effective beam alignment between senders and receivers requires accurate knowledge of the vehicles’ position for fast beam steering, which is often impractical to obtain in real time. We address this problem by leveraging the traffic signals regulating vehicular mobility: as an example, we may coordinate beams with red traffic lights, as they correspond to higher vehicle densities and lower speeds. To evaluate our intuition, we propose a tractable, yet accurate, mmWave communication model accounting for both the distance and the heading of vehicles being served. Using such a model, we optimize the beam design and define a low complexity, heuristic strategy. For increased realism, we consider as reference scenario a large-scale, real-world mobility trace of vehicles in Luxembourg. The results show that our approach closely matches the optimum and always outperforms static beam design based on road topology alone. Remarkably, it also yields better performance than solutions based on real-time mobility information.
Lactation curves for several US sheep breeds

Lactation curves for several dairy traits in sheep were studied. Data were collected from seven pure breeds, three synthetic breeds and twelve single breed crosses in a 5-year milking trial. Wood's lactation model (y n = an b e -cn ) was used to describe the lactation curves. Actual and fitted lactation curves had the same shape within each trait except for proportion of lactose. Correlations between actual and fitted lactation curves ranged from 0·92 to 0·99 for all traits except lactose (0·24). There was no apparent explanation for the low correlation for lactose content. It was concluded that the algebraic formula can be used to obtain estimates of parameters for sheep lactation curves. However, some caution should be exercised in extending results to proportion of lactose in the milk. Crossbred groups had higher total milk production than pure and synthetic breeds. Lactation curves for crossbred ewes, grouped by breed of sire, had a similar pattern. Finnsheep and Rambouillet-sired crossbred ewes produced proportionately 0·23 more milk than ewes sired by Dorset and Lincoln rams. Breed differences in total milk production were observed among the pure breeds. The Suffolk breed was superior in milk production. All breeds, except the Romanov, followed a similar pattern for lactation curves. The Romanov breed produced milk at a relatively low but consistent level throughout the lactation trial.
Pyrosequencing of the adult tarnished plant bug, Lygus lineolaris, and characterization of messages important in metabolism and development

The adoption of Bt transgenic cotton has practically eliminated lepidopteran pests from this crop and has produced a secondary pest problem, with pierce‐sucking insects such as the tarnished plant bug, Lygus lineolaris (Palisot de Beauvois) (Hemiptera: Miridae). The future of cotton genetic pest management is threatened by these insects and their development of resistance to chemical insecticides. Lygus lineolaris is also a pest of more than 100 other crops. The development of transcriptome data for this insect should be transformative in essentially all aspects of research on plant bug biology and the development of control strategies. The first 454 tarnished plant bug whole body (WB) and gut (G) transcriptomes were constructed (half plate for each). A total of 116 163 527 bases were obtained, representing 262 555 WB and 229 919 G reads (SRA048217) of which 232 058 (SRS280903) and 168 069 (SRS280894) reads, respectively, were available for assembly. The average read length was 233.1 and 208.5 bp for WB and G, respectively. The whole body and gut reads were assembled together (WB‐G) to produce the most complete transcriptome possible from our sequencing effort and resulted in 6 970 contigs with an average length of 393 bp. The gut transcriptome alone was assembled into 3 549 contigs with an average length of 349 bp. The smallest contig was 55 bp and the largest was 3 466 bp, and there were 62 484 sequences that could not be assembled (singletons) among both transcriptomes. Overall transcriptome analysis was organized according to the Gene Ontology consortium, enzyme commission, and InerPro using the Blast2GO® program. We further characterized metabolic systems and messages associated with development.
Long Tongues and Loose Niches: Evolution of Euglossine Bees and Their Nectar Flowers 1

I examined relationships between tongue length of orchid bees (Apidae: Euglossini) and nectar spur length of their flowers in the genera Calathea, Costus, and Dimerocostus using phylogenetically independent contrasts. Long‐tubed flowers have specialized on one or several species of long‐tongued euglossine bees, but long‐tongued bees have not specialized on long‐tubed flowers. Whereas long tongues may have evolved to provide access to a wider variety of nectar resources, long nectar spurs may be a mechanism for flowers to conserve nectar resources while remaining attractive to traplining bee visitors.