Imaging of Alignment, Deformation and Dissociation of CS2 Molecules using Ultrafast Electron Diffraction

Imaging the structure of molecules in transient excited states remains a challenge due to the extreme requirements for spatial and temporal resolution. Ultrafast electron diffraction from aligned molecules (UEDAM) provides atomic resolution and allows for the retrieval of structural information without the need to rely on theoretical models. Here we use UEDAM and femtosecond laser mass spectrometry (FLMS) to investigate the dynamics in carbon disulfide (CS2) following the interaction with an intense femtosecond laser pulse. We have retrieved images of ground state and excited molecules with 0.03 {\AA} precision. We have observed that the degree of alignment reaches an upper limit at laser intensities below the ionization threshold, and found evidence of structural deformation, dissociation, and ionization at higher laser intensities

Interacting Attention-gated Recurrent Networks for Recommendation

Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history are equally relevant to recommendation, which however does not apply in real-world scenarios where user-item interactions can often happen accidentally. More importantly, they learn user and item dynamics separately, thus failing to capture their joint effects on user-item interactions. To better model user and item dynamics, we present the Interacting Attention-gated Recurrent Network (IARN) which adopts the attention model to measure the relevance of each time step. In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions. By doing so, IARN can selectively memorize different time steps of a user's history when predicting her preferences over different items. Our model can therefore provide meaningful interpretations for recommendation results, which could be further enhanced by auxiliary features. Extensive validation on real-world datasets shows that IARN consistently outperforms state-of-the-art methods.Comment: Accepted by ACM International Conference on Information and Knowledge Management (CIKM), 201

Local vertical measurements and violation of Bell inequality

For two qubits belonging to Alice and Bob, we derive an approach to setup the bound of Bell operator in the condition that Alice and Bob continue to perform local vertical measurements. For pure states we find that if the entanglement of the two qubits is less than 0.2644 (measured with von Neumann entropy) the violation of the Bell inequality will never be realized, and only when the entanglement is equal to 1 the maximal violation ($2\sqrt{2}$) can occur. For specific form of mixed states, we prove that the bound of the Bell inequality depends on the concurrence. Only when the concurrence is greater than 0.6 the violation of the Bell inequality can occur, and the maximal violation can never be achieved. We suggest that the bound of the Bell operator in the condition of local vertical measurements may be used as a measure of the entanglement.Comment: 4 pages, 3 figure

Target-searching on the percolation

We study target-searching processes on a percolation, on which a hunter tracks a target by smelling odors it emits. The odor intensity is supposed to be inversely proportional to the distance it propagates. The Monte Carlo simulation is performed on a 2-dimensional bond-percolation above the threshold. Having no idea of the location of the target, the hunter determines its moves only by random attempts in each direction. For lager percolation connectivity $p\gtrsim 0.90$, it reveals a scaling law for the searching time versus the distance to the position of the target. The scaling exponent is dependent on the sensitivity of the hunter. For smaller $p$, the scaling law is broken and the probability of finding out the target significantly reduces. The hunter seems trapped in the cluster of the percolation and can hardly reach the goal.Comment: 5 figure

Exploring LLMs as a Source of Targeted Synthetic Textual Data to Minimize High Confidence Misclassifications

Natural Language Processing (NLP) models optimized for predictive performance often make high confidence errors and suffer from vulnerability to adversarial and out-of-distribution data. Existing work has mainly focused on mitigation of such errors using either humans or an automated approach. In this study, we explore the usage of large language models (LLMs) for data augmentation as a potential solution to the issue of NLP models making wrong predictions with high confidence during classification tasks. We compare the effectiveness of synthetic data generated by LLMs with that of human data obtained via the same procedure. For mitigation, humans or LLMs provide natural language characterizations of high confidence misclassifications to generate synthetic data, which are then used to extend the training set. We conduct an extensive evaluation of our approach on three classification tasks and demonstrate its effectiveness in reducing the number of high confidence misclassifications present in the model, all while maintaining the same level of accuracy. Moreover, we find that the cost gap between humans and LLMs surpasses an order of magnitude, as LLMs attain human-like performance while being more scalable

Imaging of alignment and structural changes of carbon disulfide molecules using ultrafast electron diffraction

Imaging the structure of molecules in transient-excited states remains a challenge due to the extreme requirements for spatial and temporal resolution. Ultrafast electron diffraction from aligned molecules provides atomic resolution and allows for the retrieval of structural information without the need to rely on theoretical models. Here we use ultrafast electron diffraction from aligned molecules and femtosecond laser mass spectrometry to investigate the dynamics in carbon disulfide following the interaction with an intense femtosecond laser pulse. We observe that the degree of alignment reaches an upper limit at laser intensities below the ionization threshold, and find evidence of structural deformation, dissociation and ionization at higher laser intensities

Dataset of embodied perspective enhances self and friend-biases in perceptual matching

Acknowledgements This work was supported by the National Natural Science Foundation of China (Project 31371017) and Tsinghua University Initiative Scientific Research Programme (Project 20131089329).Peer reviewedPublisher PD