arXiv subjects

Xi Chen

Publications and source records attributed to Xi Chen.

At least 811 records · Page 45Linked to original sources

arXiv2021

Quantum Stream Learning

The exotic nature of quantum mechanics makes machine learning (ML) be different in the quantum realm compared to classical applications. ML can be used for knowledge discovery using information continuously extracted from a quantum system in a broad range of tasks. The model receives streaming quantum information for learning and decision-making, resulting in instant feedback on the quantum system. As a stream learning approach, we present a deep reinforcement learning on streaming data from a continuously measured qubit at the presence of detuning, dephasing, and relaxation. We also investigate how the agent adapts to another quantum noise pattern by transfer learning. Stream learning provides a better understanding of closed-loop quantum control, which may pave the way for advanced quantum technologies.

quant-ph↗

arXiv2021

Entangled Quantum Memristors

We propose the interaction of two quantum memristors via capacitive and inductive coupling in feasible superconducting circuit architectures. In this composed system the input gets correlated in time, which changes the dynamic response of each quantum memristor in terms of its pinched hysteresis curve and their nontrivial entanglement. In this sense, the concurrence and memristive dynamics follow an inverse behavior, showing maximal values of entanglement when the hysteresis curve is minimal and vice versa. Moreover, the direction followed in time by the hysteresis curve is reversed whenever the quantum memristor entanglement is maximal. The study of composed quantum memristors paves the way for developing neuromorphic quantum computers and native quantum neural networks, on the path towards quantum advantage with current NISQ technologies.

quant-ph↗

arXiv2021

Abnormal heavy-flux flow in cuprate superconductors

Dynamic charge transfers, or charge fluxes, generated by anharmonic phonon couplings have previously shown some fundamental connections with the pseudogap phase and other important phases in cuprate superconductors. The anharmonicity here suggests the importance of nonadiabatic behaviors of charge fluxes in cuprates. These charge fluxes will result in the dynamic charge transfer that can be directly calculated by the charge difference induced by phonons in density functional theory simulations. However, retrieving the flux field from the calculated charge transfer is nontrivial. In this article, a new method is developed to solve these fluxes, which help identify anharmonically coupled phonons as the generator of nonlocal flux flows beyond local oscillations in cuprates, especially at high frequencies. A strength metric of such nonlocal fluxes shows a strong correlation to the maximum superconducting transition temperature T_(c,max) of different cuprate families. A unique picture is thus proposed where heavy fluxes tunnel through a dynamic energy band near the Fermi level, providing the attraction force through a Bernoulli-like mechanism for the formation of Cooper pairs. Adding the dynamic band to our band simulation also reproduces the flat band feature that appears below the superconducting transition temperature Tc in previous experimental measurements.

cond-mat.supr-con↗

arXiv2021

Interval-valued q-Rung Orthopair Fuzzy Choquet Integral Operators and Its Application in Group Decision Making

It is more flexible for decision makers to evaluate by interval-valued q-rung orthopair fuzzy set (IVq-ROFS),which offers fuzzy decision-making more applicational space. Meanwhile, Choquet integralses non-additive set function (fuzzy measure) to describe the interaction between attributes directly.In particular, there are a large number of practical issues that have relevance between attributes.Therefore,this paper proposes the correlation operator and group decision-making method based on the interval-valued q-rung orthopair fuzzy set Choquet integral.First,interval-valued q-rung orthopair fuzzy Choquet integral average operator (IVq-ROFCA) and interval-valued q-rung orthopair fuzzy Choquet integral geometric operator (IVq-ROFCG) are inves-tigated,and their basic properties are proved.Furthermore, several operators based on IVq-ROFCA and IVq-ROFCG are developed. Then, a group decision-making method based on IVq-ROFCA is developed,which can solve the decision making problems with interaction between attributes.Finally,through the implementation of the warning management system for hypertension,it is shown that the operator and group decision-making method proposed in this paper can handle complex decision-making cases in reality, and the decision result is consistent with the doctor's diagnosis result.Moreover,the comparison with the results of other operators shows that the proposed operators and group decision-making method are correct and effective,and the decision result will not be affected by the change of q value.

cs.AI↗

arXiv2021

Brain-inspired feature exaggeration in generative replay for continual learning

The catastrophic forgetting of previously learnt classes is one of the main obstacles to the successful development of a reliable and accurate generative continual learning model. When learning new classes, the internal representation of previously learnt ones can often be overwritten, resulting in the model's "memory" of earlier classes being lost over time. Recent developments in neuroscience have uncovered a method through which the brain avoids its own form of memory interference. Applying a targeted exaggeration of the differences between features of similar, yet competing memories, the brain can more easily distinguish and recall them. In this paper, the application of such exaggeration, via the repulsion of replayed samples belonging to competing classes, is explored. Through the development of a 'reconstruction repulsion' loss, this paper presents a new state-of-the-art performance on the classification of early classes in the class-incremental learning dataset CIFAR100.

cs.LG↗

arXiv2021

Digitized Adiabatic Quantum Factorization

Quantum integer factorization is a potential quantum computing solution that may revolutionize cryptography. Nevertheless, a scalable and efficient quantum algorithm for noisy intermediate-scale quantum computers looks far-fetched. We propose an alternative factorization method, within the digitized-adiabatic quantum computing paradigm, by digitizing an adiabatic quantum factorization algorithm enhanced by shortcuts to adiabaticity techniques. We find that this fast factorization algorithm is suitable for available gate-based quantum computers. We test our quantum algorithm in an IBM quantum computer with up to six qubits, surpassing the performance of the more commonly used factorization algorithms on the long way towards quantum advantage.

quant-ph↗

arXiv2021

New Streaming Algorithms for High Dimensional EMD and MST

We study streaming algorithms for two fundamental geometric problems: computing the cost of a Minimum Spanning Tree (MST) of an $n$-point set $X \subset \{1,2,\dots,Δ\}^d$, and computing the Earth Mover Distance (EMD) between two multi-sets $A,B \subset \{1,2,\dots,Δ\}^d$ of size $n$. We consider the turnstile model, where points can be added and removed. We give a one-pass streaming algorithm for MST and a two-pass streaming algorithm for EMD, both achieving an approximation factor of $\tilde{O}(\log n)$ and using polylog$(n,d,Δ)$-space only. Furthermore, our algorithm for EMD can be compressed to a single pass with a small additive error. Previously, the best known sublinear-space streaming algorithms for either problem achieved an approximation of $O(\min\{ \log n , \log (Δd)\} \log n)$ [Andoni-Indyk-Krauthgamer '08, Backurs-Dong-Indyk-Razenshteyn-Wagner '20]. For MST, we also prove that any constant space streaming algorithm can only achieve an approximation of $Ω(\log n)$, analogous to the $Ω(\log n)$ lower bound for EMD of [Andoni-Indyk-Krauthgamer '08]. Our algorithms are based on an improved analysis of a recursive space partitioning method known generically as the Quadtree. Specifically, we show that the Quadtree achieves an $\tilde{O}(\log n)$ approximation for both EMD and MST, improving on the $O(\min\{ \log n , \log (Δd)\} \log n)$ approximation of [Andoni-Indyk-Krauthgamer '08, Backurs-Dong-Indyk-Razenshteyn-Wagner '20].

cs.DS↗

arXiv2021

ActFloor-GAN: Activity-Guided Adversarial Networks for Human-Centric Floorplan Design

We present a novel two-stage approach for automated floorplan design in residential buildings with a given exterior wall boundary. Our approach has the unique advantage of being human-centric, that is, the generated floorplans can be geometrically plausible, as well as topologically reasonable to enhance resident interaction with the environment. From the input boundary, we first synthesize a human-activity map that reflects both the spatial configuration and human-environment interaction in an architectural space. We propose to produce the human-activity map either automatically by a pre-trained generative adversarial network (GAN) model, or semi-automatically by synthesizing it with user manipulation of the furniture. Second, we feed the human-activity map into our deep framework ActFloor-GAN to guide a pixel-wise prediction of room types. We adopt a re-formulated cycle-consistency constraint in ActFloor-GAN to maximize the overall prediction performance, so that we can produce high-quality room layouts that are readily convertible to vectorized floorplans. Experimental results show several benefits of our approach. First, a quantitative comparison with prior methods shows superior performance of leveraging the human-activity map in predicting piecewise room types. Second, a subjective evaluation by architects shows that our results have compelling quality as professionally-designed floorplans and much better than those generated by existing methods in terms of the room layout topology. Last, our approach allows manipulating the furniture placement, considers the human activities in the environment, and enables the incorporation of user-design preferences.

cs.GR↗

arXiv2021

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- V. Hierarchical fragmentation and gas dynamics in IRDC G034.43+00.24

We present new 3-mm continuum and molecular lines observations from the ATOMS survey towards the massive protostellar clump, MM1, located in the filamentary infrared dark cloud (IRDC), G034.43+00.24 (G34). The lines observed are the tracers of either dense gas (e.g. HCO+/H13CO+ J = 1-0) or outflows (e.g. CS J = 2-1). The most complete picture to date of seven cores in MM1 is revealed by dust continuum emission. These cores are found to be gravitationally bound, with virial parameter, $α_{vir}<2$. At least four outflows are identified in MM1 with a total outflowing mass of $\sim 45 M_\odot$, and a total energy of $\sim 1\times 10^{47}$ erg, typical of outflows from a B0-type star. Evidence of hierarchical fragmentation, where turbulence dominates over thermal pressure, is observed at both the cloud and the clump scales. This could be linked to the scale-dependent, dynamical mass inflow/accretion on clump and core scales. We therefore suggest that the G34 cloud could be undergoing a dynamical mass inflow/accretion process linked to the multiscale fragmentation, which leads to the sequential formation of fragments of the initial cloud, clumps, and ultimately dense cores, the sites of star formation.

astro-ph.GA↗

arXiv2021

Generalized Data Weighting via Class-level Gradient Manipulation

Label noise and class imbalance are two major issues coexisting in real-world datasets. To alleviate the two issues, state-of-the-art methods reweight each instance by leveraging a small amount of clean and unbiased data. Yet, these methods overlook class-level information within each instance, which can be further utilized to improve performance. To this end, in this paper, we propose Generalized Data Weighting (GDW) to simultaneously mitigate label noise and class imbalance by manipulating gradients at the class level. To be specific, GDW unrolls the loss gradient to class-level gradients by the chain rule and reweights the flow of each gradient separately. In this way, GDW achieves remarkable performance improvement on both issues. Aside from the performance gain, GDW efficiently obtains class-level weights without introducing any extra computational cost compared with instance weighting methods. Specifically, GDW performs a gradient descent step on class-level weights, which only relies on intermediate gradients. Extensive experiments in various settings verify the effectiveness of GDW. For example, GDW outperforms state-of-the-art methods by $2.56\%$ under the $60\%$ uniform noise setting in CIFAR10. Our code is available at https://github.com/GGchen1997/GDW-NIPS2021.

cs.CV↗

arXiv2021

Artificial confocal microscopy for deep label-free imaging

Widefield microscopy methods applied to optically thick specimens are faced with reduced contrast due to spatial crosstalk, in which the signal at each point is the result of a superposition from neighboring points that are simultaneously illuminated. In 1955, Marvin Minsky proposed confocal microscopy as a solution to this problem. Today, laser scanning confocal fluorescence microscopy is broadly used due to its high depth resolution and sensitivity, which come at the price of photobleaching, chemical, and photo-toxicity. Here, we present artificial confocal microscopy (ACM) to achieve confocal-level depth sectioning, sensitivity, and chemical specificity, on unlabeled specimens, nondestructively. We augmented a laser scanning confocal instrument with a quantitative phase imaging module, which provides optical pathlength maps of the specimen on the same field of view as the fluorescence channel. Using pairs of phase and fluorescence images, we trained a convolution neural network to translate the former into the latter. The training to infer a new tag is very practical as the input and ground truth data are intrinsically registered and the data acquisition is automated. Remarkably, the ACM images present significantly stronger depth sectioning than the input images, enabling us to recover confocal-like tomographic volumes of microspheres, hippocampal neurons in culture, and 3D liver cancer spheroids. By training on nucleus-specific tags, ACM allows for segmenting individual nuclei within dense spheroids for both cell counting and volume measurements. Furthermore, taking the estimated fluorescence volumes, as annotation for the phase data, we extracted dry mass information for individual nuclei. Finally, our results indicate that the network learning can be transferred between spheroids suspended in different media.

physics.optics↗

arXiv2021

Average-Case Subset Balancing Problems

Given a set of $n$ input integers, the Equal Subset Sum problem asks us to find two distinct subsets with the same sum. In this paper we present an algorithm that runs in time $O^*(3^{0.387n})$ in the~average case, significantly improving over the $O^*(3^{0.488n})$ running time of the best known worst-case algorithm and the Meet-in-the-Middle benchmark of $O^*(3^{0.5n})$. Our algorithm generalizes to a number of related problems, such as the ``Generalized Equal Subset Sum'' problem, which asks us to assign a coefficient $c_i$ from a set $C$ to each input number $x_i$ such that $\sum_{i} c_i x_i = 0$. Our algorithm for the average-case version of this problem runs in~time $|C|^{(0.5-c_0/|C|)n}$ for some positive constant $c_0$, whenever $C=\{0, \pm 1, \dots, \pm d\}$ or $\{\pm 1, \dots, \pm d\}$ for some positive integer $d$ (with $O^*(|C|^{0.45n})$ when $|C|<10$). Our results extend to the~problem of finding ``nearly balanced'' solutions in which the target is a not-too-large nonzero offset $τ$. Our approach relies on new structural results that characterize the probability that $\sum_{i} c_i x_i$ $=τ$ has a solution $c \in C^n$ when $x_i$'s are chosen randomly; these results may be of independent interest. Our algorithm is inspired by the ``representation technique'' introduced by Howgrave-Graham and Joux. This requires several new ideas to overcome preprocessing hurdles that arise in the representation framework, as well as a novel application of dynamic programming in the solution recovery phase of the algorithm.

cs.CC↗

arXiv2021

Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning

Despite recent advances in its theoretical understanding, there still remains a significant gap in the ability of existing PAC-Bayesian theories on meta-learning to explain performance improvements in the few-shot learning setting, where the number of training examples in the target tasks is severely limited. This gap originates from an assumption in the existing theories which supposes that the number of training examples in the observed tasks and the number of training examples in the target tasks follow the same distribution, an assumption that rarely holds in practice. By relaxing this assumption, we develop two PAC-Bayesian bounds tailored for the few-shot learning setting and show that two existing meta-learning algorithms (MAML and Reptile) can be derived from our bounds, thereby bridging the gap between practice and PAC-Bayesian theories. Furthermore, we derive a new computationally-efficient PACMAML algorithm, and show it outperforms existing meta-learning algorithms on several few-shot benchmark datasets.

cs.LG↗

arXiv2021

Ideal Mixing of Paraelectric and Ferroelectric Nematic Phases in Liquid Crystals of Distinct Molecular Species

The organic mesogens RM734 and DIO are members of separate molecular families featuring distinct molecular structures. These families, at the present time, are the only ones known to exhibit a ferroelectric nematic liquid crystal (LC) phase. Here we present an experimental investigation of the phase diagram and electro-optics of binary mixtures of RM734 and DIO. We observe paraelectric nematic (N) and ferroelectric nematic (NF) phases in both materials, each of which exhibits complete miscibility across the phase diagram, showing that the paraelectric and ferroelectric are the same phases in RM734 as in DIO. Remarkably, these molecules form ideal mixtures with respect to both the paraelectric-ferroelectric nematic phase behavior and the ferroelectric polarization density of the mixtures, the principal order parameter of the transition. Ideal mixing is also manifested in the orientational viscosity, and the onset of glassy dynamics at low temperature. This behavior is attributable in part to the similarity of their overall molecular shape and net longitudinal dipole moment (~ 11 Debye), and to a common tendency for head-to-tail molecular association. In contrast, the significant difference in molecular structures leads to poor solubility in the crystal phases, enhancing the stability of the ferroelectric nematic phase at low temperature in the mixtures and making possible room temperature electro-optic effects. In the mixtures with excess DIO, an intermediate phase appears via an ultraweak, first-order transition from the N phase, in a narrow temperature range between the paraelectric and ferroelectric nematics.

cond-mat.soft↗

arXiv2021

Variability in extragalactic class I methanol masers: New maser components toward NGC4945 and NGC253

We have used the Australia Telescope Compact Array (ATCA) to make new observations of the 36.2-GHz ($4_{-1}\rightarrow3_0$E) methanol transition toward NGC 4945 and NGC 253. These observations have revealed the presence of new maser components toward these galaxies, and have provided the first clear evidence for variability in extragalactic class~I methanol masers. Alongside the new observations of NGC 4945 and NGC 253, we present the results of recent 36.2-GHz methanol maser searches toward 12 galaxies, placing upper limits on the emission from the 36.2-GHz class~I transition and the 37.7-GHz ($7_{2}\rightarrow8_1$E) class~II maser line toward these sources. Flux density values for the 7-mm continuum emission toward these sources are also reported where applicable. A re-analysis of the published 36.2-GHz methanol observations of Arp 220 undertaken as part of the search revealed some issues with previous imaging procedures. The re-analysis, combined with non-detections in independent follow-up observations suggest that there is no 36.2-GHz methanol emission toward Arp 220 stronger than 3.5 mJy in a 10 km s$^{-1}$ channel (5$σ$ upper limit).

astro-ph.GA↗

arXiv2021

Experimental implementation of precisely tailored light-matter interaction via inverse engineering

Accurate and efficient quantum control in the presence of constraints and decoherence is a requirement and a challenge in quantum information processing. Shortcuts to adiabaticity, originally proposed to speed up slow adiabatic process, have nowadays become versatile toolboxes for preparing states or controlling the quantum dynamics. Unique shortcut designs are required for each quantum system with intrinsic physical constraints, imperfections, and noises. Here, we implement fast and robust control for the state preparation and state engineering in a rare-earth ions system. Specifically, the interacting pulses are inversely engineered and further optimized with respect to inhomogeneities of the ensemble and the unwanted interaction with other qubits. We demonstrate that our protocols surpass the conventional adiabatic schemes, by reducing the decoherence from the excited state decay and inhomogeneous broadening. The results presented here are applicable to other noisy intermediate scale quantum systems.

quant-ph↗

arXiv2021

On the Fairness of Randomized Trials for Recommendation with Heterogeneous Demographics and Beyond

Observed events in recommendation are consequence of the decisions made by a policy, thus they are usually selectively labeled, namely the data are Missing Not At Random (MNAR), which often causes large bias to the estimate of true outcomes risk. A general approach to correct MNAR bias is performing small Randomized Controlled Trials (RCTs), where an additional uniform policy is employed to randomly assign items to each user. In this work, we concentrate on the fairness of RCTs under both homogeneous and heterogeneous demographics, especially analyzing the bias for the least favorable group on the latter setting. Considering RCTs' limitations, we propose a novel Counterfactual Robust Risk Minimization (CRRM) framework, which is totally free of expensive RCTs, and derive its theoretical generalization error bound. At last, empirical experiments are performed on synthetic tasks and real-world data sets, substantiating our method's superiority both in fairness and generalization.

cs.LG↗

arXiv2021

Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients

Glioblastoma is profoundly heterogeneous in microstructure and vasculature, which may lead to tumor regional diversity and distinct treatment response. Although successful in tumor sub-region segmentation and survival prediction, radiomics based on machine learning algorithms, is challenged by its robustness, due to the vague intermediate process and track changes. Also, the weak interpretability of the model poses challenges to clinical application. Here we proposed a machine learning framework to semi-automatically fine-tune the clustering algorithms and quantitatively identify stable sub-regions for reliable clinical survival prediction. Hyper-parameters are automatically determined by the global minimum of the trained Gaussian Process (GP) surrogate model through Bayesian optimization(BO) to alleviate the difficulty of tuning parameters for clinical researchers. To enhance the interpretability of the survival prediction model, we incorporated the prior knowledge of intra-tumoral heterogeneity, by segmenting tumor sub-regions and extracting sub-regional features. The results demonstrated that the global minimum of the trained GP surrogate can be used as sub-optimal hyper-parameter solutions for efficient. The sub-regions segmented based on physiological MRI can be applied to predict patient survival, which could enhance the clinical interpretability for the machine learning model.

eess.IV↗