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We investigate the internal dynamics of hairy black holes in the Einstein-Maxwell-vector theory. The development of the charged vector hair necessarily removes the inner Cauchy horizon and results in an anisotropic black hole ending at a spacelike singularity. The far interior evolution is characterized by the chaotic alternations among different Kasner epochs. We show that those late interior time dynamics of bounces and epochs can be captured analytically. In particular, we obtain three distinct types of transformation laws for the alternation of Kasner epochs analytically, including the Kasner inversion, Kasner transition, and Kasner reflection. Our analytical approach is corroborated by numerical solutions to the full equations of motion. Moreover, we provide a clear explanation of the alternation of Kasner epochs found numerically in the literature., Comment: 21 pages, 3 figures

Fully printed flexible perovskite solar cells (f-PSCs) show great potential for the commercialization of perovskite photovoltaics owing to their compatibility with high-throughput roll-to-roll (R2R) production. However, the challenge remains in the deficiency in controlling interfacial recombination losses of the functional layer, causing remarkable loss of power conversion efficiency (PCE) in industrial production. Here, a fullerene-substituted alkylphosphonic acid dipole layer is introduced between the R2R-printed tin oxide electron transport layer and the perovskite active layer to reduce the energetic barrier and to suppress surface recombination at the buried interface. The resulting f-PSCs exhibit a PCE of 17.0% with negligible hysteresis, retain 95% of their initial PCE over 3000 bending cycles and achieve a T95 lifetime of 1200 h under 1 sun and 65 degreeC in nitrogen atmosphere. Moreover, the fully printed flexible perovskite solar mini-modules (f-PSMs) with a 20.25 cm2 aperture area achieve a PCE of 11.6%. The encapsulated f-PSMs retain 90% of their initial PCE after 500 h damp-heat testing at 65 degreeC and 85% relative humidity (ISOS-D3). This work marks an important progress toward the realization of efficient and stable flexible perovskite photovoltaics for commercialization.

We create a manually-operated mechanical digger able to move within 2D densely packed granular media, in a manner intrinsically different from the existing biomimic diggers. The characteristics of our design include that the average grain size is about one tenth as large as the digger, the area packing density $\phi$ is about $90\%$ of the jamming density $\phi_{c}$, and the value of the dimensionless inertial number $I$ is in the quasi-static regime. Unlike conventional autonomous diggers that move by fluidizing the surrounding granular media with a much finer grain size, our digger swiftly bypasses this mobility mechanism as coarse grains with interparticle friction are hard to be fluidized. To achieve this, the digger has a circular shape and singly captures granular particles near the front entrance of the recess formed by the center unit running across its body using the equipped vibrating claw and then ejects the captured particles backwards. We validate this moving strategy in both experiments and numerical simulations, and demonstrate that the moving efficiency can be enhanced by the judgement of the human operator but the effectiveness is not significantly influenced by the shape of granular particles. In addition, localized vibration from the claw is needed to degrade friction between interlocked irregular-shaped particles., Comment: 9 pages, 8 figures

Large-scale vision-language models (VLMs) have shown a strong zero-shot generalization capability on unseen-domain data. However, adapting pre-trained VLMs to a sequence of downstream tasks often leads to the forgetting of previously learned knowledge and a reduction in zero-shot classification performance. To tackle this problem, we propose a unique Selective Dual-Teacher Knowledge Transfer framework that leverages the most recent fine-tuned and the original pre-trained VLMs as dual teachers to preserve the previously learned knowledge and zero-shot capabilities, respectively. With only access to an unlabeled reference dataset, our proposed framework performs a selective knowledge distillation mechanism by measuring the feature discrepancy from the dual-teacher VLMs. Consequently, our selective dual-teacher knowledge distillation mitigates catastrophic forgetting of previously learned knowledge while preserving the zero-shot capabilities of pre-trained VLMs. Extensive experiments on benchmark datasets demonstrate that our framework is favorable against state-of-the-art continual learning approaches for preventing catastrophic forgetting and zero-shot degradation. Project page: https://chuyu.org/research/snd, Comment: Accepted to ECCV 2024. Project page: https://chuyu.org/research/snd

The study of the interior of hairy black holes has received significant attention recently. This paper builds upon our recent analytical approach to investigate the internal dynamics of charged black holes with scalar hair in general spacetime dimensions. The geometries of these hairy balck holes end at a spacelike singularity. We investigate the alternation of Kasner epoch at later interior times and obtain the analytic expression for two kinds of transformation, namely Kasner inversion and Kasner transition. Moreover, we classify three different types of Kasner alternations for a large class of Einstein-Maxwell-scalar theory. Our analytical results are corroborated by numerical solutions to the full equations of motion, including a top-down model from supergravity. For general interactions, more complicated behaviors beyond our analytical description are also found and discussed, including the presence of non-Kasner epochs and the random change of the amplitude of the Kasner exponent at late interior times., Comment: Published version

Federated Learning (FL) is an emerging paradigm that enables multiple users to collaboratively train a robust model in a privacy-preserving manner without sharing their private data. Most existing approaches of FL only consider traditional single-label image classification, ignoring the impact when transferring the task to multi-label image classification. Nevertheless, it is still challenging for FL to deal with user heterogeneity in their local data distribution in the real-world FL scenario, and this issue becomes even more severe in multi-label image classification. Inspired by the recent success of Transformers in centralized settings, we propose a novel FL framework for multi-label classification. Since partial label correlation may be observed by local clients during training, direct aggregation of locally updated models would not produce satisfactory performances. Thus, we propose a novel FL framework of Language-Guided Transformer (FedLGT) to tackle this challenging task, which aims to exploit and transfer knowledge across different clients for learning a robust global model. Through extensive experiments on various multi-label datasets (e.g., FLAIR, MS-COCO, etc.), we show that our FedLGT is able to achieve satisfactory performance and outperforms standard FL techniques under multi-label FL scenarios. Code is available at https://github.com/Jack24658735/FedLGT., Comment: Accepted by AAAI 2024

Concept erasure in text-to-image diffusion models aims to disable pre-trained diffusion models from generating images related to a target concept. To perform reliable concept erasure, the properties of robustness and locality are desirable. The former refrains the model from producing images associated with the target concept for any paraphrased or learned prompts, while the latter preserves its ability in generating images with non-target concepts. In this paper, we propose Reliable Concept Erasing via Lightweight Erasers (Receler). It learns a lightweight Eraser to perform concept erasing while satisfying the above desirable properties through the proposed concept-localized regularization and adversarial prompt learning scheme. Experiments with various concepts verify the superiority of Receler over previous methods., Comment: ECCV 2024. Project page: https://jasper0314-huang.github.io/receler-concept-erasing/

Photocurrent matching in conventional monolithic tandem solar cells is achieved by choosing semiconductors with complementary absorption spectra and by carefully adjusting the optical properties of the complete top and bottom stacks. However, for thin film photovoltaic technologies at the module level, another design variable significantly alleviates the task of photocurrent matching, namely the cell width, whose modification can be readily realized by the adjustment of the module layout. Herein we demonstrate this concept at the experimental level for the first time for a 2T-mechanically stacked perovskite (FAPbBr3)/organic (PM6:Y6:PCBM) tandem mini-module, an unprecedented approach for these emergent photovoltaic technologies fabricated in an independent manner. An excellent Isc matching is achieved by tuning the cell widths of the perovskite and organic modules to 7.22 mm (PCEPVKT-mod= 6.69%) and 3.19 mm (PCEOPV-mod= 12.46%), respectively, leading to a champion efficiency of 14.94% for the tandem module interconnected in series with an aperture area of 20.25 cm2. Rather than demonstrating high efficiencies at the level of small lab cells, our successful experimental proof-of-concept at the module level proves to be particularly useful to couple devices with non-complementary semiconductors, either in series or in parallel electrical connection, hence overcoming the limitations imposed by the monolithic structure.

Federated learning (FL) emerges as a decentralized learning framework which trains models from multiple distributed clients without sharing their data to preserve privacy. Recently, large-scale pre-trained models (e.g., Vision Transformer) have shown a strong capability of deriving robust representations. However, the data heterogeneity among clients, the limited computation resources, and the communication bandwidth restrict the deployment of large-scale models in FL frameworks. To leverage robust representations from large-scale models while enabling efficient model personalization for heterogeneous clients, we propose a novel personalized FL framework of client-specific Prompt Generation (pFedPG), which learns to deploy a personalized prompt generator at the server for producing client-specific visual prompts that efficiently adapts frozen backbones to local data distributions. Our proposed framework jointly optimizes the stages of personalized prompt adaptation locally and personalized prompt generation globally. The former aims to train visual prompts that adapt foundation models to each client, while the latter observes local optimization directions to generate personalized prompts for all clients. Through extensive experiments on benchmark datasets, we show that our pFedPG is favorable against state-of-the-art personalized FL methods under various types of data heterogeneity, allowing computation and communication efficient model personalization., Comment: Accepted by ICCV 2023

To understand how deep neural networks perform classification predictions, recent research attention has been focusing on developing techniques to offer desirable explanations. However, most existing methods cannot be easily applied for semantic segmentation; moreover, they are not designed to offer interpretability under the multi-annotator setting. Instead of viewing ground-truth pixel-level labels annotated by a single annotator with consistent labeling tendency, we aim at providing interpretable semantic segmentation and answer two critical yet practical questions: "who" contributes to the resulting segmentation, and "why" such an assignment is determined. In this paper, we present a learning framework of Tendency-and-Assignment Explainer (TAX), designed to offer interpretability at the annotator and assignment levels. More specifically, we learn convolution kernel subsets for modeling labeling tendencies of each type of annotation, while a prototype bank is jointly observed to offer visual guidance for learning the above kernels. For evaluation, we consider both synthetic and real-world datasets with multi-annotators. We show that our TAX can be applied to state-of-the-art network architectures with comparable performances, while segmentation interpretability at both levels can be offered accordingly., Comment: 10 pages, 5 figures

While self-supervised learning has been shown to benefit a number of vision tasks, existing techniques mainly focus on image-level manipulation, which may not generalize well to downstream tasks at patch or pixel levels. Moreover, existing SSL methods might not sufficiently describe and associate the above representations within and across image scales. In this paper, we propose a Self-Supervised Pyramid Representation Learning (SS-PRL) framework. The proposed SS-PRL is designed to derive pyramid representations at patch levels via learning proper prototypes, with additional learners to observe and relate inherent semantic information within an image. In particular, we present a cross-scale patch-level correlation learning in SS-PRL, which allows the model to aggregate and associate information learned across patch scales. We show that, with our proposed SS-PRL for model pre-training, one can easily adapt and fine-tune the models for a variety of applications including multi-label classification, object detection, and instance segmentation., Comment: IEEE WACV 2023, Github: https://github.com/WesleyHsieh0806/SS-PRL

A vocoder is a conditional audio generation model that converts acoustic features such as mel-spectrograms into waveforms. Taking inspiration from Differentiable Digital Signal Processing (DDSP), we propose a new vocoder named SawSing for singing voices. SawSing synthesizes the harmonic part of singing voices by filtering a sawtooth source signal with a linear time-variant finite impulse response filter whose coefficients are estimated from the input mel-spectrogram by a neural network. As this approach enforces phase continuity, SawSing can generate singing voices without the phase-discontinuity glitch of many existing vocoders. Moreover, the source-filter assumption provides an inductive bias that allows SawSing to be trained on a small amount of data. Our experiments show that SawSing converges much faster and outperforms state-of-the-art generative adversarial network and diffusion-based vocoders in a resource-limited scenario with only 3 training recordings and a 3-hour training time., Comment: Accepted at ISMIR 2022

By blowing up the ambient space along the curve wrapped by B-branes, we study the brane superpotentials and Ooguri-Vafa invariants on complete intersections Calabi-Yau threefolds. On the topological B-model side, B-brane superpotentials are expressed in terms of the period integral of the blow-up manifolds. By mirror maps, the superpotentials are generating functions of Ooguri-Vafa invariants counting holomorphic disks on the topological A-model side., Comment: 29 pages

In this paper, we study the open topological string amplitudes on Calabi-Yau threefolds by the extended holomorphic anomaly equation. The disk two-point function determined by the domainwall tension, together with the Yukawa couplings, solves the amplitudes with high genus and boundaries recursively. The BPS invariants encoded in the amplitudes are extracted by mirror symmetry., Comment: 29 pages

Open topological string partition function on compact Calabi-Yau threefolds satisfies the extended holomorphic anomaly equation. By direct integration, we solve these equations and obtain partition functions for first several genus and boundaries on complete intersection Calabi-Yau threefolds. Complemented by the unoriented worldsheet contribution, the annulus functions encode the genus one BPS invariants., Comment: 39pages

We study the interior dynamics of a top-down holographic superconductor from M-theory. The condense of the charged scalar hair necessarily removes the inner Cauchy horizon and the spacetime ends at a spacelike singularity. Although there is a smooth superconducting phase transition at the critical temperature, the onset of superconductivity is accompanied by intricate interior dynamics, including the collapse of the Einstein-Rosen bridge, the Josephson oscillations of the condensate, and the final Kasner singularity. We obtain analytically the transformation rule for the alternation of different Kasner epochs. Thanks to the nonlinear couplings of the top-down theory, there is generically a never-ending chaotic alternation of Kasner epochs towards the singularity. We compute the holographic complexity using both the complexity-action and the complexity-volume dualities. In contrast to the latter, the complexity growth rate from the complexity-action duality has a discontinuity at the critical temperature, characterizing the sudden change of the internal structure before and after the superconducting phase transition., Comment: 40 pages, 20 figures. typos corrected

The number of rational points in toric data are given for two-parameter Calabi-Yau $n$-folds as toric hypersurfaces over finite fields $\mathbb F_p$ . We find that the fundamental period is equal to the number of rational points of the Calabi-Yau $n$-folds in zeroth order $p$-adic expansion. By analyzing the solution set of the GKZ-system given by the enhanced polyhedron, we deduce that under type II/F-theory duality the 3D and 4D Calabi-Yau manifolds have the same number of rational points in zeroth order. Taking the quintic and its duality as an example, the number of rational points in some specific complex moduli are given by numerical calculation to support our results., Comment: 18 pages

We study B-brane superpotentials depending on several closed- and open- moduli on Calabi-Yau hypersurfaces and complete intersections. By blowing up the ambient space along a curve wrapped by B-branes in a Calabi-Yau manifold, we obtain a blow-up new manifold and the period integral satisfying the GKZ-system. Via mirror symmetry to A-model, we calculate the superpotentials and extract Ooguri-Vafa invariants for concrete examples of several open-closed moduli in Calabi-Yau manifolds.

Few-shot classification aims to carry out classification given only few labeled examples for the categories of interest. Though several approaches have been proposed, most existing few-shot learning (FSL) models assume that base and novel classes are drawn from the same data domain. When it comes to recognizing novel-class data in an unseen domain, this becomes an even more challenging task of domain generalized few-shot classification. In this paper, we present a unique learning framework for domain-generalized few-shot classification, where base classes are from homogeneous multiple source domains, while novel classes to be recognized are from target domains which are not seen during training. By advancing meta-learning strategies, our learning framework exploits data across multiple source domains to capture domain-invariant features, with FSL ability introduced by metric-learning based mechanisms across support and query data. We conduct extensive experiments to verify the effectiveness of our proposed learning framework and show learning from small yet homogeneous source data is able to perform preferably against learning from large-scale one. Moreover, we provide insights into choices of backbone models for domain-generalized few-shot classification., Comment: Accepted by ICIP 2021

Few-shot semantic segmentation addresses the learning task in which only few images with ground truth pixel-level labels are available for the novel classes of interest. One is typically required to collect a large mount of data (i.e., base classes) with such ground truth information, followed by meta-learning strategies to address the above learning task. When only image-level semantic labels can be observed during both training and testing, it is considered as an even more challenging task of weakly supervised few-shot semantic segmentation. To address this problem, we propose a novel meta-learning framework, which predicts pseudo pixel-level segmentation masks from a limited amount of data and their semantic labels. More importantly, our learning scheme further exploits the produced pixel-level information for query image inputs with segmentation guarantees. Thus, our proposed learning model can be viewed as a pixel-level meta-learner. Through extensive experiments on benchmark datasets, we show that our model achieves satisfactory performances under fully supervised settings, yet performs favorably against state-of-the-art methods under weakly supervised settings., Comment: Accepted to WACV 2022

Singing voice synthesis (SVS) is a task that aims to generate audio signals according to musical scores and lyrics. With its multifaceted nature concerning music and language, producing singing voices indistinguishable from that of human singers has always remained an unfulfilled pursuit. Nonetheless, the advancements of deep learning techniques have brought about a substantial leap in the quality and naturalness of synthesized singing voice. This paper aims to review some of the state-of-the-art deep learning-driven SVS systems. We intend to summarize their deployed model architectures and identify the strengths and limitations for each of the introduced systems. Thereby, we picture the recent advancement trajectory of this field and conclude the challenges left to be resolved both in commercial applications and academic research.

We prove a no Cauchy horizon theorem for general nonlinear electrodynamics black holes with charged scalar hairs. By constructing a radially conserved charged, we show that there is no inner Cauchy horizon for both spherical and planar symmetric cases, independent of the form of scalar potential and nonlinear electrodynamics. After imposing the null energy condition, we are also able to rule out the existence of the Cauchy horizon for the hyperbolic black holes. We take the Born-Infeld black hole as a concrete example to study the interior dynamics beyond the event horizon. When the contribution from the scalar potential can be neglected, the asymptotic near-singularity takes a universal Kasner form. We also confirm that the intricate interior dynamics is closely associated with the instability of the inner Cauchy horizon triggered by scalar hairs., Comment: 12 pages, 6 figures. Comments are welcomed

Generating videos with content and motion variations is a challenging task in computer vision. While the recent development of GAN allows video generation from latent representations, it is not easy to produce videos with particular content of motion patterns of interest. In this paper, we propose Dual Motion Transfer GAN (Dual-MTGAN), which takes image and video data as inputs while learning disentangled content and motion representations. Our Dual-MTGAN is able to perform deterministic motion transfer and stochastic motion generation. Based on a given image, the former preserves the input content and transfers motion patterns observed from another video sequence, and the latter directly produces videos with plausible yet diverse motion patterns based on the input image. The proposed model is trained in an end-to-end manner, without the need to utilize pre-defined motion features like pose or facial landmarks. Our quantitative and qualitative results would confirm the effectiveness and robustness of our model in addressing such conditioned image-to-video tasks., Comment: Accepted to ICPR 2020

Learning interpretable and interpolatable latent representations has been an emerging research direction, allowing researchers to understand and utilize the derived latent space for further applications such as visual synthesis or recognition. While most existing approaches derive an interpolatable latent space and induces smooth transition in image appearance, it is still not clear how to observe desirable representations which would contain semantic information of interest. In this paper, we aim to learn meaningful representations and simultaneously perform semantic-oriented and visually-smooth interpolation. To this end, we propose an angular triplet-neighbor loss (ATNL) that enables learning a latent representation whose distribution matches the semantic information of interest. With the latent space guided by ATNL, we further utilize spherical semantic interpolation for generating semantic warping of images, allowing synthesis of desirable visual data. Experiments on MNIST and CMU Multi-PIE datasets qualitatively and quantitatively verify the effectiveness of our method., Comment: ICPR 2020

We apply a holistic 2D Tetris-like model, where particles move based on prescribed rules, to investigate the flow rate enhancement from a hopper. This phenomenon was originally reported in the literature as a feature of placing an obstacle at an optimal location near the exit of a hopper discharging athermal granular particles under gravity. We find that this phenomenon is limited to a system of sufficiently many particles. In addition to the waiting room effect, another mechanism able to explain and create the flow rate enhancement is the concentration mechanism of particles on their way to reaching the hopper exit after passing the obstacle. We elucidate the concentration mechanism by decomposing the flow rate into its constituent variables: the local area packing fraction $\phi_l^E$ and the averaged particle velocity $v_y^E$ at the hopper exit. In comparison to the case without an obstacle, our results show that an optimally placed obstacle can create a net flow rate enhancement of relatively weakly driven particles, caused by the exit-bottleneck coupling if $\phi_l^E > \phi_o^c$, where $\phi_o^c$ is a characteristic area packing fraction marking a transition from fast to slow flow regimes of Tetris particles. Utilizing the concentration mechanism by artificially guiding particles into the central sparse space under the obstacle or narrowing the hopper exit angle under the obstacle, we can create a man-made flow rate peak of relatively strongly-driven particles that initially exhibit no flow rate peak. Additionally, the enhanced flow rate can be maximized by an optimal obstacle shape, particle acceleration rate towards the hopper exit, or exit geometry of the hopper., Comment: 15 pages, 15 figures

For D-brane system with three D-branes on compact Calabi-Yau threefolds, the dual F-theory fourfolds for parallel/complete coincident/part coincident D-brane system is constructed by the type II/F-theory duality. Complete coincident means that the three D-branes coincide and part coincident represents the coincident of two of the three D branes. The low energy effective superpotentials are calculated by mirror symmetry, GKZ-system method and the type II/F-theory duality on the B-model side, respectively. Using the mirror symmetry, A-model superpotentials and the Ooguri-Vafa invariants are obtained from the B-model side. These results indicate that the superpotential contributed by one of three parallel D-branes is identical with the D-brane system with only one D-brane, which is a signal of decoupling of the parallel topological D-branes. However, the superpotential and Oogrui-Vafa invariants are different among parallel, complete coincident and part coincident D-brane system, which show the evidence of the phase transition due to the enhanced gauge symmetry in the low energy theories and the geometrical singularity., Comment: 12 pages,4 tables. v2:References added, typos corrected and summary improved

Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras. To address this task, one typically requires a large amount labeled data for training an effective Re-ID model, which might not be practical for real-world applications. To alleviate this limitation, we choose to exploit a sufficient amount of pre-existing labeled data from a different (auxiliary) dataset. By jointly considering such an auxiliary dataset and the dataset of interest (but without label information), our proposed adaptation and re-identification network (ARN) performs unsupervised domain adaptation, which leverages information across datasets and derives domain-invariant features for Re-ID purposes. In our experiments, we verify that our network performs favorably against state-of-the-art unsupervised Re-ID approaches, and even outperforms a number of baseline Re-ID methods which require fully supervised data for training., Comment: 7 pages, 3 figures. CVPR 2018 workshop paper

The phase transitions are studied for the D-brane systems with multiple open-string moduli in terms of toric geometry: between the parallel D-brane phase corresponding to the Coulomb branch and the coincident phase corresponding to the Higgs branch. The two separated D-branes on compact Calabi-Yau 3-fold coincide developing the geometric singularity in the corresponding F-theory Calabi-Yau 4-fold in terms of TypeII/F theory duality, and the enhancement of gauge group $U(1)\times U(1)\rightarrow SU(2)$ in terms of gauge theory. For several D-brane system with various closed-string moduli, using the mirror symmetry and the typeII/F theory duality the A-model superpotentials are obtained from the B-model side for the two phases, and the Ooguri-Vafa invariants are extracted from the A-model superpotential. We find the discrete $Z_2$ symmetry of superpotentials in the two parallel D-branes phase of all the models which is a signal of decoupling of the parallel topological D-branes. Furthermore, the \emph{$U(1)$ Ooguri-Vafa invariants} for one of the two parallel D-branes are the same as the invariants for the D-brane system with only one D-brane. However they are different from the \emph{$SU(2)$ Ooguri-Vafa invariants} corresponding to the two D-branes coincident phase. We present these Ooguri-Vafa invariants for two phases with figures to observe the difference, and find that in two phases the points for invariants form a wave-packet respectively. The wave-packets for coincident phase are higher and wider than ones for parallel phase which means more complicate spectrum structure. This is an evidence of the phase transition between the Coulomb branch and the Higgs branch.

In this paper, we extend the GKZ-system method to the more general case: compact Complete Intersection Calabi-Yau manifolds (CICY). For several one-deformation modulus compact CICYs with D-branes, the on-shell superpotentials in this paper from the extended GKZ-system method are exactly consistent with published results obtained from other methods. We further compute the off-shell superpotentials of these models. Then we obtain both the on-shell and off-shell superpotentials for sev- eral two-deformation moduli compact CICYs with D-branes by using the extended GKZ-system method. The discrete symmetrical groups, Z2, Z3 and Z4, of the holo- morphic curves wrapped by D-branes play the important roles in computing the superpotentials, in some sense, they are the quantum symmetries of these models. Furthermore, through the mirror symmetry, the Ooguri-Vafa invariants are extracted from the A-model instanton expansion.

The D-brane superpotential is very important in the low energy effective theory. As the generating function of all disk instantons from the worldsheet point of view, it plays a crucial role in deriving some important properties of the compact Calabi-Yau manifolds. By using the GKZ-generalized hypergeometric system, we will calculate the B-brane superpotentials of two non-fermat type compact Calabi-Yau hypersurfaces in toric varieties,respectively. Then according to the mirror symmetry, we obtain the A-model superpotentials and the Ooguri-Vafa invariants for the mirror Calabi-Yau manifolds., Comment: 17 pages,4 tables

Free-space quantum communication with satellites opens a promising avenue for global secure quantum network and large-scale test of quantum foundations. Recently, numerous experimental efforts have been carried out towards this ambitious goal. However, one essential step - transmitting single photons from the satellite to the ground with high signal-to-noise ratio (SNR) at realistic environments - remains experimental challenging. Here, we report a direct experimental demonstration of the satellite-ground transmission of a quasi-single-photon source. In the experiment, single photons (~0.85 photon per pulse) are generated by reflecting weak laser pulses back to earth with a cube-corner retro-reflector on the satellite Champ, collected by a 600-mm diameter telescope at the ground station, and finally detected by single-photon counting modules (SPCMs) after 400-km free-space link transmission. With the help of high accuracy time synchronization, narrow receiver field-of-view (FOV) and high-repetition-rate pulses (76 MHz), a SNR of better than 16:1 is obtained, which is sufficient for a secure quantum key distribution. Our experimental results represent an important step towards satellite-ground quantum communication.

We calculate the D-brane superpotentials for two non-Fermat type compact Calabi-Yau manifolds which are the hypersurfaces of the weighed projective spaces in type II string theory. By constructing the open-closed mirror maps, we also compute the Ooguri-Vafa invariants, which are related to the open Gromov-Witten invariants., Comment: 19 pages, no figures. arXiv admin note: substantial text overlap with arXiv:1206.0445

In this paper, we make a further step of \cite{Feng} and calculate off-shell superpotential of two Calabi-Yau manifold with three parameters by integrating the period of subsystem. We also obtain the Ooguri-Vafa invariants with open mirror symmetry., Comment: 12 pages. arXiv admin note: substantial text overlap with arXiv:1206.0445

According to the generalized GKZ system for CY manifold with brane and the open-closed Mirror map, We calculate the off-shell D-Brane/F-Theory effective superpotentials and Ooguri-Vafa invariants of several compact Calabi-Yau manifolds ., Comment: 21 pages

We calculate the D-brane superpotentials for two Calabi-Yau manifolds with three deformations by the generalized hypergeometric GKZ systems, which give rise to the flux superpotentials $\mathcal{W}_{GVW}$ of the dual F-theory compactification on the relevant Calabi-Yau fourfolds in the weak decoupling limit. We also compute the Ooguri-Vafa invariants from A-model expansion with mirror symmetry, which are related to the open Gromov-Witten invariants., Comment: 22 pages; v2: supplemental information included, references added, typos corrected

In this paper we study the swap operation in a two-qubit anisotropic XXZ model in the presence of an inhomogeneous magnetic field. We establish the range of anisotropic parameter within which the swap operation is feasible. The swap errors caused by the inhomogeneous field are evaluated.

The geometrical description of BPS 3-string junction in the F-theory background is given by lifting a string junction in IIB into F-theory and constructing a holomorphic curve in K3 with respect to a special complex structure of K3. The holomorphic curve is fibration of 1-cycles of the elliptic fiber over the geodesic string junction. The F-theory picture in this paper provides an unifying description of both string and string junction, and is advantageous over the M-theory picture of them., Comment: Latex, 15 pages. Reference Added

The optimal entanglement manipulation for a single copy of mixed states of two qubits is to transform it to a Bell diagonal state. In this paper we derive an explicit form of the local operation that can realize such a transformation. The result obtained is universal for arbitrary entangled two-qubit states and it discloses that the corresponding local filter is not unique for density matrices with rank $n=2$ and can be exclusively determined for that with $n=3$ and 4. As illustrations, a four-parameters family of mixed states are explored, the local filter as well as the transformation probability are given explicitly, which verify the validity of the general result., Comment: 5 pages, to be published in Phys. Rev. A

This study investigated the effects of augmenting and deleting elaborations in an existing self-instructional text for a micro-computer database application, "Microsoft Works User's Manual." A total of 60 undergraduate students were randomly assigned to the original, elaborated, or unelaborated text versions. The elaborated version included: (1) reorganizing content by separating conceptual and procedural information; (2) adding examples, restatements, explanations, an introduction and summary; (3) adding text-to-graphic references to support both conceptual and procedural information; (4) adding several examples of using different combinations of connectors to link selection rules; and (5) removing side-margin headings. Changes in the unelaborated version included: (1) identifying statements of procures and rules; (2) deleting all elaboration such as examples, restatements, and descriptions of what the software does as a result of procedures; (3) deleting unnecessary words; (4) deleting introductory text; and (5) deleting side-margin subheadings. Students completed six tasks using one of the three instructional texts, and then completed a six-item attitude survey. The absence of a significant difference in the task performance under the three text versions suggests that not all people require elaborations to develop a good understanding of procedural learning. Background information on elaboration includes a discussion of learner-generated versus author-provided elaboration, and the minimalist versus maximalist approach to training users in the use of computer programs. Six tables summarize findings. (Contains 67 references.) (MAS)

The topics covered include the following: improvement of the system hardware; upgrading of the software; the observation status; preliminary daylight tracking capability; testing the new type of laser; and future plans.

Simultaneous compression of the passively mode-locked pulse width and pulse train have been achieved by using a plano-convex unstable resonator hybrided by a nonlinear Sagnac ring interferometer. The greater than 30 mJ single pulse energy of a lone oscillator and less than or equal to 10 ps pulsewidth have been obtained. Using this system, the LAGEOS and ETALON satellites' laser ranging have been performed successfully.