Freeze-in Dark Matter via Lepton Portal: Hubble Tension and Stellar Cooling

We propose a new freeze-in dark matter candidate which feebly couples to the standard model charged leptons. The feeble interactions allow it (i) to freeze-in from the Standard Model thermal bath with its relic density being either a fraction or the entirety of the observed dark matter density and (ii) to radiatively decay to two photons in the dark matter mass ranges of order keV scale with lifetime larger than the age of Universe. These features make this model a realistic realization of dark matter with late-time decay to reduce Hubble tension. We show the best-fit value of H_{0}=68.31(69.34) km s^{-1}Mpc^{-1} in light of Planck 2018+BAO(+LSS)+Pantheon data sets. We then use stellar cooling data to place constraints on the parameter space favored by the Hubble tension. While the universal coupling scenario is excluded, the hierarchical coupling scenario can be tested by future observations of white dwarfs after a careful look into photon inverse decay, Primakoff and Bremsstrahlung emission of the dark matter in various stellar systems. The viable parameter space may be linked to anomalies in future X-ray telescopes.Comment: 21 pages, 8 figure

Quench behavior of high temperature superconductor (RE)Ba2Cu3Ox CORC cable

High temperature superconductor (HTS) (RE)Ba2Cu3Ox (REBCO) conductor on round core cable (CORC) shows great advantages on high current capacity and power density. In REBCO CORC cables, current is redistributed among tapes through terminal contact resistances (TCR) when a local quench occurs. Therefore, its quench behaviour is different from single tape situation. To better understand the underlying physical process of local quenches in CORC cables, a new 3D multi-physics modelling tool for CORC cables is developed and presented in this paper. In this model, the REBCO tape is treated as a thin shell without thickness, and four models are coupled: T-formulation model, A-formulation model, a heat transfer model and an equivalent circuit model. The T-formulation is applied to the conductor shell only to calculate current distribution, which will be input into A-formulation model; the A-formulation is applied to the whole 3D domain to calculate magnetic field, which is then fed back to the T-formulation model. The hot spot induced quenches of CORC cables are analysed. The results show that the thermal stability of CORC cable can be considerably improved by reducing TCR. The minimum quench energy (MQE) increases rapidly with the reduction of TCR when the resistance is in a middle range, which is about 5 μΩ ≤ Rt ≤ 200 μΩ in this study. When TCR is too low (Rt 50 μΩ), the MQE shows no obvious variation with TRC. With low TCR, a hot spot in one tape may induce an over-current quench on other tapes. This will not happen in a cable with high TCR. In this case, the tape with hot spot will quench and burn out before inducing a quench on other tapes. The modelling tool developed can be used to design CORC cables with improved thermal stability

Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation

Advanced recommender systems usually involve multiple domains (scenarios or categories) for various marketing strategies, and users interact with them to satisfy their diverse demands. The goal of multi-domain recommendation is to improve the recommendation performance of all domains simultaneously. Conventional graph neural network based methods usually deal with each domain separately, or train a shared model for serving all domains. The former fails to leverage users' cross-domain behaviors, making the behavior sparseness issue a great obstacle. The latter learns shared user representation with respect to all domains, which neglects users' domain-specific preferences. These shortcomings greatly limit their performance in multi-domain recommendation. To tackle the limitations, an appropriate way is to learn from multi-domain user feedbacks and obtain separate user representations to characterize their domain-specific preferences. In this paper we propose $\mathsf{H^3Trans}$, a hierarchical hypergraph network based correlative preference transfer framework for multi-domain recommendation. $\mathsf{H^3Trans}$ represents multi-domain feedbacks into a unified graph to help preference transfer via taking full advantage of users' multi-domain behaviors. We incorporate two hyperedge-based modules, namely dynamic item transfer module (Hyper-I) and adaptive user aggregation module (Hyper-U). Hyper-I extracts correlative information from multi-domain user-item feedbacks for eliminating domain discrepancy of item representations. Hyper-U aggregates users' scattered preferences in multiple domains and further exploits the high-order (not only pair-wise) connections among them to learn user representations. Experimental results on both public datasets and large-scale production datasets verify the superiority of $\mathsf{H^3Trans}$ for multi-domain recommendation.Comment: Work in progres

Fractional Calculus Guidance Algorithm in a Hypersonic Pursuit-Evasion Game

Aiming at intercepting a hypersonic weapon in a hypersonic pursuit-evasion game, this paper presents a fractional calculus guidance algorithm based on a nonlinear proportional and differential guidance law. First, under the premise of without increasing the complexity degree of the guidance system against a hypersonic manoeuvering target, the principle that the differential signal of the line-of-sight rate is more sensitive to the target manoeuver than the line-of-sight rate is employed as the guidelines to design the guidance law. A nonlinear proportional and differential guidance law (NPDG) is designed by using the differential derivative of the line-of-sight rate from a nonlinear tracking differentiator. By using the differential definition of fractional calculus, on the basis of the NPDG, a fractional calculus guidance law (FCG) is proposed. According to relative motions between the interceptor and target, the guidance system stability condition with the FCG is given and quantitative values are also proposed for the parameters of the FCG. Under different target manoeuver conditions and noisy conditions, the interception accuracy and robustness of these two guidance laws are analysed. Numerical experimental results demonstrate that the proposed guidance algorithms effectively reduce the miss distance against target manoeuvers. Compared with the NPDG, a stronger robustness of the FCG is shown under noisy condition

Beyond Generic: Enhancing Image Captioning with Real-World Knowledge using Vision-Language Pre-Training Model

Current captioning approaches tend to generate correct but "generic" descriptions that lack real-world knowledge, e.g., named entities and contextual information. Considering that Vision-Language Pre-Training (VLP) models master massive such knowledge from large-scale web-harvested data, it is promising to utilize the generalizability of VLP models to incorporate knowledge into image descriptions. However, using VLP models faces challenges: zero-shot inference suffers from knowledge hallucination that leads to low-quality descriptions, but the generic bias in downstream task fine-tuning hinders the VLP model from expressing knowledge. To address these concerns, we propose a simple yet effective method called Knowledge-guided Replay (K-Replay), which enables the retention of pre-training knowledge during fine-tuning. Our approach consists of two parts: (1) a knowledge prediction task on automatically collected replay exemplars to continuously awaken the VLP model's memory about knowledge, thus preventing the model from collapsing into the generic pattern; (2) a knowledge distillation constraint to improve the faithfulness of generated descriptions hence alleviating the knowledge hallucination. To evaluate knowledge-enhanced descriptions, we construct a novel captioning benchmark KnowCap, containing knowledge of landmarks, famous brands, special foods and movie characters. Experimental results show that our approach effectively incorporates knowledge into descriptions, outperforming strong VLP baseline by 20.9 points (78.7->99.6) in CIDEr score and 20.5 percentage points (34.0%->54.5%) in knowledge recognition accuracy. Our code and data is available at https://github.com/njucckevin/KnowCap.Comment: Accepted at ACM Multimedia (ACMMM) 202

SpikeBERT: A Language Spikformer Trained with Two-Stage Knowledge Distillation from BERT

Spiking neural networks (SNNs) offer a promising avenue to implement deep neural networks in a more energy-efficient way. However, the network architectures of existing SNNs for language tasks are too simplistic, and deep architectures have not been fully explored, resulting in a significant performance gap compared to mainstream transformer-based networks such as BERT. To this end, we improve a recently-proposed spiking transformer (i.e., Spikformer) to make it possible to process language tasks and propose a two-stage knowledge distillation method for training it, which combines pre-training by distilling knowledge from BERT with a large collection of unlabelled texts and fine-tuning with task-specific instances via knowledge distillation again from the BERT fine-tuned on the same training examples. Through extensive experimentation, we show that the models trained with our method, named SpikeBERT, outperform state-of-the-art SNNs and even achieve comparable results to BERTs on text classification tasks for both English and Chinese with much less energy consumption

Self-assembly of block-copolymer chains to promote the dispersion of nanoparticles in polymer nanocomposites

In this paper we adopt molecular-dynamics simulations to study the amphiphilic AB block-copolymer (BCP) mediated nanoparticles (NPs) dispersion in polymer nanocomposites (PNCs), with the A-block being compatible with the NPs and the B-block being miscible with the polymer matrix. The effects of the number and components of BCP, as well as the interaction strength between A-block and NPs on the spatial organization of NPs are explored. We find the increase of the fraction of the A-block brings different dispersion effect to NPs than that of B-block. We also find that the best dispersion state of the NPs occurs in the case of a moderate interaction strength between the A-block and the NPs. Meanwhile, the stress-strain behaviour is probed. Our simulation results verify that adopting BCP is an effective way to adjust the dispersion of NPs in the polymer matrix, further to manipulate the mechanical properties.</p