Possible Deuteron-like Molecular States Composed of Heavy Baryons

We perform a systematic study of the possible loosely bound states composed of two charmed baryons or a charmed baryon and an anti-charmed baryon within the framework of the one boson exchange (OBE) model. We consider not only the $\pi$ exchange but also the $\eta$, $\rho$, $\omega$, $\phi$ and $\sigma$ exchanges. The $S-D$ mixing effects for the spin-triplets are also taken into account. With the derived effective potentials, we calculate the binding energies and root-mean-square (RMS) radii for the systems $\Lambda_c\Lambda_c(\bar{\Lambda}_c)$, $\Xi_c\Xi_c(\bar{\Xi}_c)$, $\Sigma_c\Sigma_c(\bar{\Sigma}_c)$, $\Xi_c^\prime\Xi_c^\prime(\bar{\Xi}_c^\prime)$ and $\Omega_c\Omega_c(\bar{\Omega}_c)$. Our numerical results indicate that: (1) the H-dibaryon-like state $\Lambda_c\Lambda_c$ does not exist; (2) there may exist four loosely bound deuteron-like states $\Xi_c\Xi_c$ and $\Xi_c^\prime\Xi_c^\prime$ with small binding energies and large RMS radii.Comment: 17 pages, 32 figure

1-(2-Chloro­benzyl­idene)-2-(2,4-dinitro­phen­yl)hydrazine

In the title compound, C13H9ClN4O4, there are two crystallographically independent mol­ecules in the asymmetric unit, which have very similar conformations. The C=N—N angles in each independent mol­ecule are 115.0 (2) and 116.6 (2)°, which are significantly smaller than the ideal value of 120° expected for sp 2-hybridized N atoms. This is probably a consequence of repulsion between the nitro­gen lone pairs and the adjacent N—N bonds. Two bifurcated intra­molecular N—H⋯O hydrogen bonds help to establish the mol­ecular conformation and consolidate the crystal packing

Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction

Federated learning is known for its capability to safeguard participants' data privacy. However, recently emerged model inversion attacks (MIAs) have shown that a malicious parameter server can reconstruct individual users' local data samples through model updates. The state-of-the-art attacks either rely on computation-intensive search-based optimization processes to recover each input batch, making scaling difficult, or they involve the malicious parameter server adding extra modules before the global model architecture, rendering the attacks too conspicuous and easily detectable. To overcome these limitations, we propose Scale-MIA, a novel MIA capable of efficiently and accurately recovering training samples of clients from the aggregated updates, even when the system is under the protection of a robust secure aggregation protocol. Unlike existing approaches treating models as black boxes, Scale-MIA recognizes the importance of the intricate architecture and inner workings of machine learning models. It identifies the latent space as the critical layer for breaching privacy and decomposes the complex recovery task into an innovative two-step process to reduce computation complexity. The first step involves reconstructing the latent space representations (LSRs) from the aggregated model updates using a closed-form inversion mechanism, leveraging specially crafted adversarial linear layers. In the second step, the whole input batches are recovered from the LSRs by feeding them into a fine-tuned generative decoder. We implemented Scale-MIA on multiple commonly used machine learning models and conducted comprehensive experiments across various settings. The results demonstrate that Scale-MIA achieves excellent recovery performance on different datasets, exhibiting high reconstruction rates, accuracy, and attack efficiency on a larger scale compared to state-of-the-art MIAs

Linear optical quantum computation with imperfect entangled photon-pair sources and inefficient non-photon-number-resolving detectors

We propose a scheme for efficient cluster state quantum computation by using imperfect polarization-entangled photon-pair sources, linear optical elements and inefficient non-photon-number-resolving detectors. The efficiency threshold for loss tolerance in our scheme requires the product of source and detector efficiencies should be >1/2 - the best known figure. This figure applies to uncorrelated loss. We further find that the loss threshold is unaffected by correlated loss in the photon pair source. Our approach sheds new light on efficient linear optical quantum computation with imperfect experimental conditions.Comment: 5 pages, 2 figure

Optical loss compensation in a bulk left-handed metamaterial by the gain in quantum dots

A bulk left-handed metamaterial with fishnet structure is investigated to show the optical loss compensation via surface plasmon amplification, with the assistance of a Gaussian gain in PbS quantum dots. The optical resonance enhancement around 200 THz is confirmed by the retrieval method. By exploring the dependence of propagation loss on the gain coefficient and metamaterial thickness, we verify numerically that the left-handed response can endure a large propagation thickness with ultralow and stable loss under a certain gain coefficient.Comment: 6 pages with 4 figure

A Deep Reinforcement Learning Approach to Two-Timescale Transmission for RIS-aided Multiuser MISO systems

Reconfigurable intelligent surface (RIS) has drawn great attention recently as a promising technology for future wireless networks. In this letter, considering the two-timescale transmission protocol, we investigate the joint design of the transmit beamforming at the base station (BS) with instantaneous channel state information (CSI) and the RIS phase shifts with statistical CSI. Due to the large number of RIS elements, this design issue usually suffers from high computational complexity. To resolve the non-convexity issue with low complexity, we propose a novel deep reinforcement learning (DRL) framework, which contains two agents applying proximal policy optimization (PPO) based algorithm. Experiment results demonstrate that the proposed algorithm has comparable spectral efficiency performance to the state-of-the-art methods with substantially reduced computational delay