Continuous-variable controlled-Z gate using an atomic ensemble

The continuous-variable controlled-Z gate is a canonical two-mode gate for universal continuous-variable quantum computation. It is considered as one of the most fundamental continuous-variable quantum gates. Here we present a scheme for realizing continuous-variable controlled-Z gate between two optical beams using an atomic ensemble. The gate is performed by simply sending the two beams propagating in two orthogonal directions twice through a spin-squeezed atomic medium. Its fidelity can run up to one if the input atomic state is infinitely squeezed. Considering the noise effects due to atomic decoherence and light losses, we show that the observed fidelities of the scheme are still quite high within presently available techniques.Comment: 7 pages, 3 figures, to appear in Physical Review

(22E,24R)-5α-Ergosta-2,22-dien-6-one

In the title mol­ecule, C28H44O, two six-membered rings have regular chair conformations, while the six-membered ring containing the C=C double bond exhibits a distorted chair conformation. The five-membered ring adopts an envelope conformation. In the crystal, weak inter­molecular C—H⋯O inter­actions link mol­ecules into chains along the b axis. The absolute configuration was assigned to correspond with that of the known chiral centres in a precursor mol­ecule

(22E,24R)-3α,5-Cyclo-5α-ergosta-22-en-6-one

In the title mol­ecule, C28H44O, the two six-membered rings have a chair conformation and the two five-membered rings haveenvelope conformations. The crystal packing exhibits no short inter­molecular contacts. The absolute configuration was assigned to correspond with that of the known chiral centres in a precursor molecule, which remained unchanged during the synthesis of the title compound

Energy Efficiency Maximization in IRS-Aided Cell-Free Massive MIMO System

In this paper, we consider an intelligent reflecting surface (IRS)-aided cell-free massive multiple-input multiple-output system, where the beamforming at access points and the phase shifts at IRSs are jointly optimized to maximize energy efficiency (EE). To solve EE maximization problem, we propose an iterative optimization algorithm by using quadratic transform and Lagrangian dual transform to find the optimum beamforming and phase shifts. However, the proposed algorithm suffers from high computational complexity, which hinders its application in some practical scenarios. Responding to this, we further propose a deep learning based approach for joint beamforming and phase shifts design. Specifically, a two-stage deep neural network is trained offline using the unsupervised learning manner, which is then deployed online for the predictions of beamforming and phase shifts. Simulation results show that compared with the iterative optimization algorithm and the genetic algorithm, the unsupervised learning based approach has higher EE performance and lower running time.Comment: 6 pages, 4 figure

AZI23'UTR Is a New SLC6A3 Downregulator Associated with an Epistatic Protection Against Substance Use Disorders

Regulated activity of SLC6A3, which encodes the human dopamine transporter (DAT), contributes to diseases such as substance abuse disorders (SUDs); however, the exact transcription mechanism remains poorly understood. Here, we used a common genetic variant of the gene, intron 1 DNP1B sequence, as bait to screen and clone a new transcriptional activity, AZI23'UTR, for SLC6A3. AZI23'UTR is a 3' untranslated region (3'UTR) of the human 5-Azacytidine Induced 2 gene (AZI2) but appeared to be transcribed independently of AZI2. Found to be present in both human cell nuclei and dopamine neurons, this RNA was shown to downregulate promoter activity through a variant-dependent mechanism in vitro. Both reduced RNA density ratio of AZI23'UTR/AZI2 and increased DAT mRNA levels were found in ethanol-naive alcohol-preferring rats. Secondary analysis of dbGaP GWAS datasets (Genome-Wide Association Studies based on the database of Genotypes and Phenotypes) revealed significant interactions between regions upstream of AZI23'UTR and SLC6A3 in SUDs. Jointly, our data suggest that AZI23'UTR confers variant-dependent transcriptional regulation of SLC6A3, a potential risk factor for SUDs

Masses of doubly heavy tetraquark states with isospin = 12\frac{1}{2} and 1 and spin-parity 1+±1^{+\pm}

We apply the method of QCD sum rules to study the doubly heavy tetraquark states($QQ\bar{q}\bar{q}$) with the isospin $I = \frac{1}{2}$ and 1 and spin-parity $J^{PC}=1^{++}$ and $J^{PC}=1^{+-}$ by constructing all the $QQ\bar{q}\,\bar{q}$ tetraquark currents. The masses of the doubly bottom and charm tetraquark states are computed in the context of the two-point sum rule method incorporating the quark, gluon and mixed condensates up to dimension $10$. By the way, weak decay widths of the doubly bottom tetraquark $bb\bar{u}\bar{d}$ are also given.Comment: 12 pages, 15 figures. This article is created by revtex

Neural-Symbolic Recursive Machine for Systematic Generalization

Despite the tremendous success, existing machine learning models still fall short of human-like systematic generalization -- learning compositional rules from limited data and applying them to unseen combinations in various domains. We propose Neural-Symbolic Recursive Machine (NSR) to tackle this deficiency. The core representation of NSR is a Grounded Symbol System (GSS) with combinatorial syntax and semantics, which entirely emerges from training data. Akin to the neuroscience studies suggesting separate brain systems for perceptual, syntactic, and semantic processing, NSR implements analogous separate modules of neural perception, syntactic parsing, and semantic reasoning, which are jointly learned by a deduction-abduction algorithm. We prove that NSR is expressive enough to model various sequence-to-sequence tasks. Superior systematic generalization is achieved via the inductive biases of equivariance and recursiveness embedded in NSR. In experiments, NSR achieves state-of-the-art performance in three benchmarks from different domains: SCAN for semantic parsing, PCFG for string manipulation, and HINT for arithmetic reasoning. Specifically, NSR achieves 100% generalization accuracy on SCAN and PCFG and outperforms state-of-the-art models on HINT by about 23%. Our NSR demonstrates stronger generalization than pure neural networks due to its symbolic representation and inductive biases. NSR also demonstrates better transferability than existing neural-symbolic approaches due to less domain-specific knowledge required

A HINT from Arithmetic: On Systematic Generalization of Perception, Syntax, and Semantics

Inspired by humans' remarkable ability to master arithmetic and generalize to unseen problems, we present a new dataset, HINT, to study machines' capability of learning generalizable concepts at three different levels: perception, syntax, and semantics. In particular, concepts in HINT, including both digits and operators, are required to learn in a weakly-supervised fashion: Only the final results of handwriting expressions are provided as supervision. Learning agents need to reckon how concepts are perceived from raw signals such as images (i.e., perception), how multiple concepts are structurally combined to form a valid expression (i.e., syntax), and how concepts are realized to afford various reasoning tasks (i.e., semantics). With a focus on systematic generalization, we carefully design a five-fold test set to evaluate both the interpolation and the extrapolation of learned concepts. To tackle this challenging problem, we propose a neural-symbolic system by integrating neural networks with grammar parsing and program synthesis, learned by a novel deduction--abduction strategy. In experiments, the proposed neural-symbolic system demonstrates strong generalization capability and significantly outperforms end-to-end neural methods like RNN and Transformer. The results also indicate the significance of recursive priors for extrapolation on syntax and semantics.Comment: Preliminary wor