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Musical mode is one of the most critical element that establishes the framework of pitch organization and determines the harmonic relationships. Previous works often use the simplistic and rigid alignment method, and overlook the diversity of modes. However, in contrast to AI models, humans possess cognitive mechanisms for perceiving the various modes and keys. In this paper, we propose a spiking neural network inspired by brain mechanisms and psychological theories to represent musical modes and keys, ultimately generating musical pieces that incorporate tonality features. Specifically, the contributions are detailed as follows: 1) The model is designed with multiple collaborated subsystems inspired by the structures and functions of corresponding brain regions; 2)We incorporate mechanisms for neural circuit evolutionary learning that enable the network to learn and generate mode-related features in music, reflecting the cognitive processes involved in human music perception. 3)The results demonstrate that the proposed model shows a connection framework closely similar to the Krumhansl-Schmuckler model, which is one of the most significant key perception models in the music psychology domain. 4) Experiments show that the model can generate music pieces with characteristics of the given modes and keys. Additionally, the quantitative assessments of generated pieces reveals that the generating music pieces have both tonality characteristics and the melodic adaptability needed to generate diverse and musical content. By combining insights from neuroscience, psychology, and music theory with advanced neural network architectures, our research aims to create a system that not only learns and generates music but also bridges the gap between human cognition and artificial intelligence., Comment: 18 pages, 8 figures

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Remote photoplethysmography (rPPG) has gained significant attention in recent years for its ability to extract physiological signals from facial videos. While existing rPPG measurement methods have shown satisfactory performance in intra-dataset and cross-dataset scenarios, they often overlook the incremental learning scenario, where training data is presented sequentially, resulting in the issue of catastrophic forgetting. Meanwhile, most existing class incremental learning approaches are unsuitable for rPPG measurement. In this paper, we present a novel method named ADDP to tackle continual learning for rPPG measurement. We first employ adapter to efficiently finetune the model on new tasks. Then we design domain prototypes that are more applicable to rPPG signal regression than commonly used class prototypes. Based on these prototypes, we propose a feature augmentation strategy to consolidate the past knowledge and an inference simplification strategy to convert potentially forgotten tasks into familiar ones for the model. To evaluate ADDP and enable fair comparisons, we create the first continual learning protocol for rPPG measurement. Comprehensive experiments demonstrate the effectiveness of our method for rPPG continual learning. Source code is available at \url{https://github.com/MayYoY/rPPGDIL}, Comment: ECCV 2024

To address challenges in the digital economy's landscape of digital intelligence, large language models (LLMs) have been developed. Improvements in computational power and available resources have significantly advanced LLMs, allowing their integration into diverse domains for human life. Medical LLMs are essential application tools with potential across various medical scenarios. In this paper, we review LLM developments, focusing on the requirements and applications of medical LLMs. We provide a concise overview of existing models, aiming to explore advanced research directions and benefit researchers for future medical applications. We emphasize the advantages of medical LLMs in applications, as well as the challenges encountered during their development. Finally, we suggest directions for technical integration to mitigate challenges and potential research directions for the future of medical LLMs, aiming to meet the demands of the medical field better., Comment: Preprint. 5 figures,5 tables

Surface currents emerge in superconductors exposed to magnetic fields, and are a key signature of the Meissner effect. Analogously, chiral dynamics were observed in quantum simulators under synthetic Abelian gauge fields. The flexible control of these simulators also facilitates the engineering of non-Abelian gauge fields, but their impact on the chiral dynamics remains elusive. Here, by employing the cutting-edge momentum-lattice technique, we implement a synthetic SU(2) gauge field in a spinful 1D ladder and study the rich chiral dynamics therein. We confirm the non-Abelian nature of the synthetic potential by observing the non-Abelian Aharonov-Bohm effect on a single plaquette. More importantly, the chiral current along the two legs of the ladder is observed to be spin-dependent and highly tunable through the parameters of the gauge potential. We experimentally map out different dynamic regimes of the chiral current, and reveal the underlying competition between overlaying flux ladders with distinct spin compositions. Our experiment demonstrates the dramatic impact of non-Abelian gauge fields on the system dynamics, paving the way for future studies of exotic synthetic gauge fields on the versatile platform of momentum lattices., Comment: 6 pages, 4 figures

The manipulation of particle transport in synthetic quantum matter is an active research frontier for its theoretical importance and potential applications. Here we experimentally demonstrate an engineered topological transport in a synthetic flat-band lattice of ultracold $^{87}$Rb atoms. We implement a quasi-one-dimensional rhombic chain with staggered flux in the momentum space of the atomic condensate and observe biased local oscillations that originate from the interplay of the staggered flux and flat-band localization under the mechanism of Aharonov-Bohm caging. Based on these features, we design and experimentally confirm a state-dependent chiral transport under the periodic modulation of the synthetic flux. We show that the phenomenon is topologically protected by the winding of the Floquet Bloch bands of a coarse-grained effective Hamiltonian. The observed chiral transport offers a strategy for efficient quantum device design where topological robustness is ensured by fast Floquet driving and flat-band localization., Comment: 5 pages, 3 figures

The control and active manipulation of spin-orbit coupling (SOC) in photonic systems is fundamental in the development of modern spin optics and topological photonic devices. Here, we demonstrate the control of an artificial Rashba-Dresselhaus (RD) SOC mediated by photochemical reactions in a microcavity filled with an organic single-crystal of photochromic phase-change character. Splitting of the circular polarization components of the optical modes induced by photonic RD SOC is observed experimentally in momentum space. By applying an ultraviolet light beam, we control the spatial molecular orientation through a photochemical reaction and with that we control the energies of the photonic modes. This way we realize a reversible conversion of spin-splitting of the optical modes with different energies, leading to an optically controlled switching between circularly and linearly polarized emission from our device. Our strategy of in situ and reversible engineering of SOC induced by a light field provides a promising approach to actively design and manipulate synthetic gauge fields towards future on-chip integration in photonics and topological photonic devices.

Time reflection and refraction are temporal analogies of the spatial boundary effects derived from Fermat's principle. They occur when classical waves strike a time boundary where an abrupt change in the properties of the medium is introduced. The main features of time-reflected and refracted waves are the shift of frequency and conservation of momentum, which offer a new degree of freedom for steering extreme waves and controlling phases of matter. The concept was originally proposed for manipulating optical waves more than five decades ago. However, due to the extreme challenges in the ultrafast engineering of the optical materials, the experimental realization of the time boundary effects remains elusive. Here, we introduce a time boundary into a momentum lattice of ultracold atoms and simultaneously demonstrate the time reflection and refraction experimentally. Through launching a Gaussian-superposed state into the Su-Schrieffer-Heeger (SSH) atomic chain, we observe the time-reflected and refracted waves when the input state strikes a time boundary. Furthermore, we detect a transition from time reflection/refraction to localization with increasing strength of disorder and show that the time boundary effects are robust against considerable disorder. Our work opens a new avenue for future exploration of time boundaries and spatiotemporal lattices, and their interplay with non-Hermiticity and many-body interactions., Comment: 6 pages, 4 figures

Organic circularly polarized (CP) lasers have received increasing attention due to their future photoelectric applications. Here, we demonstrate a CP laser from a pure organic crystal-filled microcavity without any chiral molecules or chiral structures. Benefited from the giant anisotropy and excellent laser gain of organic crystals, optical Rashba-Dresselhaus spin-orbit coupling effect can be induced and is conductive to the CP laser in such microcavities. The maximum dissymmetry factor of the CP lasing with opposite helicities reached, is as high as 1.2. Our strategy may provide a new idea for the design of CP lasers towards future 3D laser displays, information storage and other fields.

Aharonov-Bohm (AB) caging, a special flat-band localization mechanism, has spurred great interest in different areas of physics. AB caging can be harnessed to explore the rich and exotic physics of quantum transport in flatband systems, where geometric frustration, disorder and correlations act in a synergetic and distinct way than in ordinary dispersive band systems. In contrast to the ordinary Anderson localization, where disorder induces localization and prevents transport, in flat band systems disorder can induce mobility, a phenomenon dubbed inverse Anderson transition. Here, we report on the experimental realization of the AB cage using a synthehtic lattice in the momentum space of ultracold atoms with tailored gauge fields, demonstrate the geometric localization due to the flat band and the inverse Anderson transition when correlated binary disorder is added to the system. Our experimental platform in a many-body environment provides a fashiinating quantum simulator where the interplay between engineered gauge fields, localization, and topological properties of flat band systems can be finely explored., Comment: 6 pages, 4 figures

Spiking neural networks (SNNs) have attracted extensive attentions in Brain-inspired Artificial Intelligence and computational neuroscience. They can be used to simulate biological information processing in the brain at multiple scales. More importantly, SNNs serve as an appropriate level of abstraction to bring inspirations from brain and cognition to Artificial Intelligence. In this paper, we present the Brain-inspired Cognitive Intelligence Engine (BrainCog) for creating brain-inspired AI and brain simulation models. BrainCog incorporates different types of spiking neuron models, learning rules, brain areas, etc., as essential modules provided by the platform. Based on these easy-to-use modules, BrainCog supports various brain-inspired cognitive functions, including Perception and Learning, Decision Making, Knowledge Representation and Reasoning, Motor Control, and Social Cognition. These brain-inspired AI models have been effectively validated on various supervised, unsupervised, and reinforcement learning tasks, and they can be used to enable AI models to be with multiple brain-inspired cognitive functions. For brain simulation, BrainCog realizes the function simulation of decision-making, working memory, the structure simulation of the Neural Circuit, and whole brain structure simulation of Mouse brain, Macaque brain, and Human brain. An AI engine named BORN is developed based on BrainCog, and it demonstrates how the components of BrainCog can be integrated and used to build AI models and applications. To enable the scientific quest to decode the nature of biological intelligence and create AI, BrainCog aims to provide essential and easy-to-use building blocks, and infrastructural support to develop brain-inspired spiking neural network based AI, and to simulate the cognitive brains at multiple scales. The online repository of BrainCog can be found at https://github.com/braincog-x., Comment: This paper was accepted by Patterns. The accepted version can be seen at https://www.cell.com/patterns/fulltext/S2666-3899(23)00144-7

We report an experimental investigation on the Doppler-free saturated absorption spectroscopy of buffer-gas-cooled Barium monofluoride (BaF) molecules in a 4~K cryogenic cell. The obtained spectra with a resolution of 19~MHz, much smaller than previously observed in absorption spectroscopy, clearly resolve the hyperfine transitions. Moreover, we use these high-resolution spectra to fit the hyperfine splittings of excited $A(v=0)$ state and find the hyperfine splitting of the laser-cooling-relevant $A^2\Pi_{1/2}(v=0, J=1/2, +)$ state is about 18 MHz, much higher than the previous theoretically predicted value. This provides important missing information for laser cooling of BaF molecules., Comment: 6 pages, 3 figures

The non-Hermitian skin effect (NHSE), the accumulation of eigen wavefunctions at boundaries of open systems, underlies a variety of exotic properties that defy conventional wisdom. While NHSE and its intriguing impact on band topology and dynamics have been observed in classical or photonic systems, their demonstration in a quantum many-body setting remains elusive. Here we report the experimental realization of a dissipative Aharonov-Bohm chain -- a non-Hermitian topological model with NHSE -- in the momentum space of a two-component Bose-Einstein condensate. We identify unique signatures of NHSE in the condensate dynamics, and perform Bragg spectroscopy to resolve topological edge states against a background of localized bulk states. Our work sets the stage for further investigation on the interplay of many-body statistics and interactions with NHSE, and is a significant step forward in the quantum control and simulation of non-Hermitian physics., Comment: 13 pages, 11 figures

We demonstrate one-dimensional Doppler cooling of a beam of buffer-gas cooled Barium monofluoride (BaF) molecules. The dependences of the cooling efficiency with the laser detuning, the bias filed and the laser intensity are carefully measured. We numerical simulate our experiment with a Monte Carlo method, and find the theoretic predictions consists with our experimental data. This result represents a key step towards further cooling and trapping of BaF molecules., Comment: 5 pages, 6 figures

We proposed a general Zeeman slower scheme applicable to the majority of the laser-coolable molecules. Different from previous schemes, the key idea of our scheme lies in that the compensation of the detuning with the magnetic field is done for the repumping laser instead of the cooling laser. Only atoms or molecules with the right velocity will be repumped and laser slowed. Such scheme is more feasible for molecules with complex energy sturcutres. We apply this scheme for molecules with large Land\'e g-factor of the excited states and polyatomic molecules, and it shows a better slowing efficiency., Comment: 6 pages, 5 figures

We present a magnetically enhanced laser cooling scheme applicable to multi-level type-II transitions and further diatomic molecules with adiabatic transfer. An angled magnetic field is introduced to not only remix the dark states, but also decompose the multi-level system into several two-level sub-systems in time-ordering, hence allowing multiple photon momentum transfer. For complex multi-level diatomic molecules, although the enhancement gets weakened, our simulations still predict a $\sim 4\times$ larger value of the maximum achievable cooling force and a wider coolable velocity range compared to the conventional Doppler cooling. A reduced dependence on spontaneous emission of this scheme makes laser cooling a molecule with leakage channels become a feasibility., Comment: 10 pages, 9 figures

In cognitive radio systems, cyclostationary feature detection plays an important role in spectrum sensing, especially in low SNR cases. To configure the detection threshold under a certain noise level and a pre-set miss detection probability Pf, it's important to derive the theoretical distribution of the observation variable. In this paper, noise distribution in cyclic frequency domain has been studied and Generalized Extreme Value (GEV) distribution is found to be a precise match. Maximum likelihood estimation is applied to estimate the parameters of GEV. Monte Carlo simulation has been carried out to show that the simulated ROC curve is coincided with the theoretical ROC curve, which proves the efficiency of the theoretical distribution model.

In cognitive radio systems, one of the main requirements is to detect the presence of the primary users' transmission, especially in weak signal cases. Cyclostationary detection is always used to solve weak signal detection, however, the computational complexity prevents it from wide usage. In this paper, a fast cycle frequency domain feature detection algorithm has been proposed, in which only feature frequency with significant cyclic signature is considered for a certain modulation mode. Simulation results show that the proposed algorithm has remarkable performance gain than energy detection when supporting real-time detection with low computational complexity., Comment: 4 pages, 3 figures, submitted to Crowncom 2009