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Thanks to the high degree of adjustability, holographic tweezer array has been proved to be the best choice to create arbitrary geometries atomic array. In holographic tweezer array experiment, optical tweezer generated by spatial light modulator (SLM) usually is used as static tweezer array. Due to the alternating current(AC) stark shifts effect, intensity difference of traps will cause different light shift. So, the optimization of intensity equalization is very important in many-body system consist of single atoms. Here we report a work on studying of intensity equalization algorithm. Through this algorithm, the uniformity of tweezer can exceed 96% when the number of tweezer size is bigger than 1000. Our analysis shows that further uniformity requires further optimization of optical system. The realization of the intensity equalization algorithm is of great significance to the many-body experiments based on single atom array., Comment: 8 pages, 5figures

In the field of multi-modal language models, the majority of methods are built on an architecture similar to LLaVA. These models use a single-layer ViT feature as a visual prompt, directly feeding it into the language models alongside textual tokens. However, when dealing with long sequences of visual signals or inputs such as videos, the self-attention mechanism of language models can lead to significant computational overhead. Additionally, using single-layer ViT features makes it challenging for large language models to perceive visual signals fully. This paper proposes an efficient multi-modal language model to minimize computational costs while enabling the model to perceive visual signals as comprehensively as possible. Our method primarily includes: (1) employing cross-attention to image-text interaction similar to Flamingo. (2) utilize hierarchical ViT features. (3) introduce the Mixture of Experts (MoE) mechanism to enhance model effectiveness. Our model achieves competitive scores on public multi-modal benchmarks and performs well in tasks such as image captioning and video captioning.

Meal preparation is an important instrumental activity of daily living~(IADL). While existing research has explored robotic assistance in meal preparation tasks such as cutting and cooking, the crucial task of peeling has received less attention. Robot-assisted peeling, conventionally a bimanual task, is challenging to deploy in the homes of care recipients using two wheelchair-mounted robot arms due to ergonomic and transferring challenges. This paper introduces a robot-assisted peeling system utilizing a single robotic arm and an assistive cutting board, inspired by the way individuals with one functional hand prepare meals. Our system incorporates a multimodal active perception module to determine whether an area on the food is peeled, a human-in-the-loop long-horizon planner to perform task planning while catering to a user's preference for peeling coverage, and a compliant controller to peel the food items. We demonstrate the system on 12 food items representing the extremes of different shapes, sizes, skin thickness, surface textures, skin vs flesh colors, and deformability.

Accurate delineation of tumor-adjacent functional brain regions is essential for planning function-preserving neurosurgery. Functional magnetic resonance imaging (fMRI) is increasingly used for presurgical counseling and planning. When analyzing presurgical fMRI data, false negatives are more dangerous to the patients than false positives because patients are more likely to experience significant harm from failing to identify functional regions and subsequently resecting critical tissues. In this paper, we propose a novel spatially adaptive variable screening procedure to enable effective control of false negatives while leveraging the spatial structure of fMRI data. Compared to existing statistical methods in fMRI data analysis, the new procedure directly controls false negatives at a desirable level and is completely data-driven. The new method is also substantially different from existing false-negative control procedures which do not take spatial information into account. Numerical examples show that the new method outperforms several state-of-the-art methods in retaining signal voxels, especially the subtle ones at the boundaries of functional regions, while providing cleaner separation of functional regions from background noise. Such results could be valuable to preserve critical tissues in neurosurgery.

Motivated by the inquiries of weak signals in underpowered genome-wide association studies (GWASs), we consider the problem of retaining true signals that are not strong enough to be individually separable from a large amount of noise. We address the challenge from the perspective of false negative control and present false negative control (FNC) screening, a data-driven method to efficiently regulate false negative proportion at a user-specified level. FNC screening is developed in a realistic setting with arbitrary covariance dependence between variables. We calibrate the overall dependence through a parameter whose scale is compatible with the existing phase diagram in high-dimensional sparse inference. Utilizing the new calibration, we asymptotically explicate the joint effect of covariance dependence, signal sparsity, and signal intensity on the proposed method. We interpret the results using a new phase diagram, which shows that FNC screening can efficiently select a set of candidate variables to retain a high proportion of signals even when the signals are not individually separable from noise. Finite sample performance of FNC screening is compared to those of several existing methods in simulation studies. The proposed method outperforms the others in adapting to a user-specified false negative control level. We implement FNC screening to empower a two-stage GWAS procedure, which demonstrates substantial power gain when working with limited sample sizes in real applications., Comment: arXiv admin note: text overlap with arXiv:2006.15667

In high speed CNC (Compute Numerical Control) machining, the feed rate scheduling has played an important role to ensure machining quality and machining efficiency. In this paper, a novel feed rate scheduling method is proposed for generating smooth feed rate profile conveniently with the consideration of both geometric error and kinematic error. First, a relationship between feed rate value and chord error is applied to determine the feed rate curve. Then, breaking points, which can split whole curve into several blocks, can be found out using proposed two step screening method. For every block, a feed rate profile based on the Sigmoid function is generated. With the consideration of kinematic limitation and machining efficiency, a time-optimal feed rate adjustment algorithm is proposed to further adjust feed rate value at breaking points. After planning feed rate profile for each block, all blocks feed rate profile will be connected smoothly. The resulting feed rate profile is more concise compared with the polynomial profile and more efficient compared with the trigonometric profile. Finally, simulations with two free-form NURBS curves are conducted and comparison with the sine-curve method are carried out to verify the feasibility and applicability of the proposed method.

Feedrate scheduling is a key step in computer numerical control (CNC) machining, as it has a close relationship with machining time and surface quality, and has now become a hot issue in industry and academia. To reduce high chord errors and round-off errors, and generate continuous velocity, acceleration, and jerk profile of parametric interpolation, a novel and complete S-shape based feedrate scheduling algorithm is presented in this paper. The algorithm consists of three modules: bidirectional scanning module, velocity scheduling module and round-off error elimination module. The bidirectional scanning module with the limitations of chord error, normal acceleration/jerk and command feedrate aims to guarantee the continuity of the feed rate at the junctions between successive NURBS sub-curves. After the NURBS sub-curves have been classified into two cases, the velocity scheduling module firstly calculates the actual maximum federate, and then generates the feed rate profiles of all NURBS sub-curves according to our velocity scheduling function. Later, the round-off error elimination module is proposed to make the total interpolating time become an integer multiple of the interpolation period, which leads to the elimination of round-off errors. Finally, benchmarks are conducted to verify the applicability of the proposed method compared with some other methods.

Spelling irregularities, known now as spelling mistakes, have been found for several centuries. As humans, we are able to understand most of the misspelled words based on their location in the sentence, perceived pronunciation, and context. Unlike humans, computer systems do not possess the convenient auto complete functionality of which human brains are capable. While many programs provide spelling correction functionality, many systems do not take context into account. Moreover, Artificial Intelligence systems function in the way they are trained on. With many current Natural Language Processing (NLP) systems trained on grammatically correct text data, many are vulnerable against adversarial examples, yet correctly spelled text processing is crucial for learning. In this paper, we investigate how spelling errors can be corrected in context, with a pre-trained language model BERT. We present two experiments, based on BERT and the edit distance algorithm, for ranking and selecting candidate corrections. The results of our experiments demonstrated that when combined properly, contextual word embeddings of BERT and edit distance are capable of effectively correcting spelling errors., Comment: Accepted by 2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC). IEEE

We consider the scenario where important signals are not strong enough to be separable from a large amount of noise. Such weak signals commonly exist in large-scale data analysis and play vital roles in many biomedical applications. Existing methods however are mostly underpowered for such weak signals. We address the challenge from the perspective of false negative control and develop a new method to efficiently regulate false negative proportion at a user-specified level. The new method is developed in a realistic setting with arbitrary covariance dependence between variables. We calibrate the overall dependence through a parameter whose scale is compatible with the existing phase diagram in high-dimensional sparse inference. Utilizing the new calibration, we asymptotically explicate the joint effect of covariance dependence, signal sparsity, and signal intensity on the proposed method. We interpret the results using a new phase diagram, which shows that the proposed method can efficiently retain a high proportion of signals even when they cannot be well-separated from noise. Finite sample performance of the proposed method is compared to those of several existing methods in simulation studies. The proposed method outperforms the others in adapting to a user-specified false negative control level. We apply the new method to analyze an fMRI dataset to locate voxels that are functionally relevant to saccadic eye movements. The new method exhibits a nice balance in identifying functional relevant regions and avoiding excessive noise voxels.