Reviewerly/exHarmony huggingface dataset:
Scalable digital quantum simulation of lattice fermion theories with local encoding

We numerically analyze the feasibility of a platform-neutral, general strategy to perform quantum simulations of fermionic lattice field theories under open boundary conditions. The digital quantum simulator requires solely one- and two-qubit gates and is scalable since integrating each Hamiltonian term requires a finite (non-scaling) cost. The exact local fermion encoding we adopt relies on auxiliary $\mathbb{Z}_2$ lattice gauge fields by adding a pure gauge Hamiltonian term akin to the Toric Code. By numerically emulating the quantum simulator real-time dynamics, we observe a timescale separation for spin- and charge-excitations in a spin-$\frac{1}{2}$ Hubbard ladder in the $t-J$ model limit. Additionally, we show that such local fermion encoding is also useful in tensor network numerical simulations.
FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models

Semantic segmentation has witnessed tremendous progress due to the proposal of various advanced network architectures. However, they are extremely hungry for delicate annotations to train, and the acquisition is laborious and unaffordable. Therefore, we present FreeMask in this work, which resorts to synthetic images from generative models to ease the burden of both data collection and annotation procedures. Concretely, we first synthesize abundant training images conditioned on the semantic masks provided by realistic datasets. This yields extra well-aligned image-mask training pairs for semantic segmentation models. We surprisingly observe that, solely trained with synthetic images, we already achieve comparable performance with real ones (e.g., 48.3 vs. 48.5 mIoU on ADE20K, and 49.3 vs. 50.5 on COCO-Stuff). Then, we investigate the role of synthetic images by joint training with real images, or pre-training for real images. Meantime, we design a robust filtering principle to suppress incorrectly synthesized regions. In addition, we propose to inequally treat different semantic masks to prioritize those harder ones and sample more corresponding synthetic images for them. As a result, either jointly trained or pre-trained with our filtered and re-sampled synthesized images, segmentation models can be greatly enhanced, e.g., from 48.7 to 52.0 on ADE20K. Code is available at https://github.com/LiheYoung/FreeMask.
Large Language Models are Visual Reasoning Coordinators

Visual reasoning requires multimodal perception and commonsense cognition of the world. Recently, multiple vision-language models (VLMs) have been proposed with excellent commonsense reasoning ability in various domains. However, how to harness the collective power of these complementary VLMs is rarely explored. Existing methods like ensemble still struggle to aggregate these models with the desired higher-order communications. In this work, we propose Cola, a novel paradigm that coordinates multiple VLMs for visual reasoning. Our key insight is that a large language model (LLM) can efficiently coordinate multiple VLMs by facilitating natural language communication that leverages their distinct and complementary capabilities. Extensive experiments demonstrate that our instruction tuning variant, Cola-FT, achieves state-of-the-art performance on visual question answering (VQA), outside knowledge VQA, visual entailment, and visual spatial reasoning tasks. Moreover, we show that our in-context learning variant, Cola-Zero, exhibits competitive performance in zero and few-shot settings, without finetuning. Through systematic ablation studies and visualizations, we validate that a coordinator LLM indeed comprehends the instruction prompts as well as the separate functionalities of VLMs; it then coordinates them to enable impressive visual reasoning capabilities.
Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment

Unsupervised domain adaptation aims to transfer rich knowledge from the annotated source domain to the unlabeled target domain with the same label space. One prevalent solution is the bi-discriminator domain adversarial network, which strives to identify target domain samples outside the support of the source domain distribution and enforces their classification to be consistent on both discriminators. Despite being effective, agnostic accuracy and overconfident estimation for out-of-distribution samples hinder its further performance improvement. To address the above challenges, we propose a novel bi-discriminator domain adversarial neural network with class-level gradient alignment, i.e. BACG. BACG resorts to gradient signals and second-order probability estimation for better alignment of domain distributions. Specifically, for accuracy-awareness, we first design an optimizable nearest neighbor algorithm to obtain pseudo-labels of samples in the target domain, and then enforce the backward gradient approximation of the two discriminators at the class level. Furthermore, following evidential learning theory, we transform the traditional softmax-based optimization method into a Multinomial Dirichlet hierarchical model to infer the class probability distribution as well as samples uncertainty, thereby alleviating misestimation of out-of-distribution samples and guaranteeing high-quality classes alignment. In addition, inspired by contrastive learning, we develop a memory bank-based variant, i.e. Fast-BACG, which can greatly shorten the training process at the cost of a minor decrease in accuracy. Extensive experiments and detailed theoretical analysis on four benchmark data sets validate the effectiveness and robustness of our algorithm.
Fusion of Global and Local Features with Multi-Inverted Indices for Efficient Image Retrieval

&lt;p&gt;Feature fusion is an effective solution for improving image retrieval performance. Although the more feature types, the better accuracy, complexity also increases. Applications in practice typically afford a limited number of feature types. Due to the strong complementarity, global and local features form an ideal combination for many fusion applications. However, the two kinds of features are intrinsically different in nature, thus cannot be fused in a straightforward way. In this work, we propose an integrated image retrieval and feature fusion framework for global and local features. It is based on inverted index fusion, a technique for efficient image retrieval. The core idea is to rank candidates by weighted voting during candidate selection, which is named pre-ranking. This procedure takes place before re-ranking, and is potentially superior to conventional late fusion. Extensive experiments on three public datasets show that the light-weight pre-ranking stage significantly contributes to accuracy, and brings substantial improvement when used together with re-ranking. Our method is robust and versatile, and can be applied to any scenario where inverted indexing is used. It is a promising technique for multimedia retrieval in the big data era.&lt;/p&gt;
Spreadsheets on Interactive Surfaces: Breaking through the Grid with the Pen

Spreadsheet programs for interactive surfaces have limited manipulations capabilities and are often frustrating to use. One key reason is that the spreadsheet grid creates a layer that intercepts most user input events, making it difficult to reach the cell values that lie underneath. We conduct an analysis of commercial spreadsheet programs and an elicitation study to understand what users can do and what they would like to do with spreadsheets on interactive surfaces. Informed by these, we design interaction techniques that leverage the precision of the pen to mitigate friction between the different layers. These enable more operations by direct manipulation on and through the grid, targeting not only cells and groups of cells, but values and substrings within and across cells as well. We prototype these interaction techniques and conduct a qualitative study with information workers who perform a variety of spreadsheet operations on their own data.
PromptMagician: Interactive Prompt Engineering for Text-to-Image Creation

Generative text-to-image models have gained great popularity among the public for their powerful capability to generate high-quality images based on natural language prompts. However, developing effective prompts for desired images can be challenging due to the complexity and ambiguity of natural language. This research proposes PromptMagician, a visual analysis system that helps users explore the image results and refine the input prompts. The backbone of our system is a prompt recommendation model that takes user prompts as input, retrieves similar prompt-image pairs from DiffusionDB, and identifies special (important and relevant) prompt keywords. To facilitate interactive prompt refinement, PromptMagician introduces a multi-level visualization for the cross-modal embedding of the retrieved images and recommended keywords, and supports users in specifying multiple criteria for personalized exploration. Two usage scenarios, a user study, and expert interviews demonstrate the effectiveness and usability of our system, suggesting it facilitates prompt engineering and improves the creativity support of the generative text-to-image model.
NIDALoc: Neurobiologically Inspired Deep LiDAR Localization

Absolute pose regression has shown great potential in LiDAR localization, which learns to regress 6-DoF LiDAR poses through deep networks. However, recent regression methods suffer from scene ambiguities in challenging scenarios, leading to inaccurate and unstable localization. Inspired by neurobiological localization mechanisms, i.e., the firing mechanism of place cells, head-direction cells, and grid cells in mammalian brains, we propose a novel LiDAR localization framework called NIDALoc to achieve more robust and accurate results. First, we propose a Hebbian memory module, motivated by place cells, to preserve historical information, which helps refine local view features to reduce scene ambiguities. Specifically, the memory module stores scene information and then recalls it when revisiting an old place. Second, we propose a novel pose constrained framework, consisting of an orientation classification task and a grid center regression task, to regularize orientation and position estimation, respectively. The framework based on head-direction cells and grid cells constrains the absolute pose regression to reduce wrong predictions. Extensive experiments on two outdoor datasets demonstrate the effectiveness of NIDALoc, which outperforms state-of-the-art localization methods, especially in large-scale challenging scenes. The source code is available on the project website at https://github.com/PSYZ1234/NIDALoc.
Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning

Federated learning is an important privacy-preserving multi-party learning paradigm, involving collaborative learning with others and local updating on private data. Model heterogeneity and catastrophic forgetting are two crucial challenges, which greatly limit the applicability and generalizability. This paper presents a novel FCCL+, federated correlation and similarity learning with non-target distillation, facilitating the both intra-domain discriminability and inter-domain generalization. For heterogeneity issue, we leverage irrelevant unlabeled public data for communication between the heterogeneous participants. We construct cross-correlation matrix and align instance similarity distribution on both logits and feature levels, which effectively overcomes the communication barrier and improves the generalizable ability. For catastrophic forgetting in local updating stage, FCCL+ introduces Federated Non Target Distillation, which retains inter-domain knowledge while avoiding the optimization conflict issue, fulling distilling privileged inter-domain information through depicting posterior classes relation. Considering that there is no standard benchmark for evaluating existing heterogeneous federated learning under the same setting, we present a comprehensive benchmark with extensive representative methods under four domain shift scenarios, supporting both heterogeneous and homogeneous federated settings. Empirical results demonstrate the superiority of our method and the efficiency of modules on various scenarios. The benchmark code for reproducing our results is available at https://github.com/WenkeHuang/FCCL.
Quantivine: A Visualization Approach for Large-scale Quantum Circuit Representation and Analysis

Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have led to the development of more complex quantum circuits. However, traditional quantum circuit diagrams suffer from scalability and readability issues, which limit the efficiency of analysis and optimization processes. In this research, we propose a novel visualization approach for large-scale quantum circuits by adopting semantic analysis to facilitate the comprehension of quantum circuits. We first exploit meta-data and semantic information extracted from the underlying code of quantum circuits to create component segmentations and pattern abstractions, allowing for easier wrangling of massive circuit diagrams. We then develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations is designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits and a formal user evaluation with quantum experts. A free copy of this paper and all supplemental materials are available at https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.
An Approach for Efficient Neural Architecture Search Space Definition

As we advance in the fast-growing era of Machine Learning, various new and more complex neural architectures are arising to tackle problem more efficiently. On the one hand their efficient usage requires advanced knowledge and expertise, which is most of the time difficult to find on the labor market. On the other hand, searching for an optimized neural architecture is a time-consuming task when it is performed manually using a trial and error approach. Hence, a method and a tool support is needed to assist users of neural architectures, leading to an eagerness in the field of Automatic Machine Learning (AutoML). When it comes to Deep Learning, an important part of AutoML is the Neural Architecture Search (NAS). In this paper, we propose a novel cell-based hierarchical search space, easy to comprehend and manipulate. The objectives of the proposed approach are to optimize the search-time and to be general enough to handle most of state of the art Convolutional Neural Networks (CNN) architectures.
Analysis of How to Improve the Spiritual and Cultural Literacy of Middle School Students from the Perspective of Creating a Civilized Campus Policy

From the perspective of creating a civilized campus policy, this paper analyzes the impact of the policy on the spiritual and cultural literacy of middle school students, as well as the current status and importance of their spiritual and cultural literacy. Through conventional classroom teaching, multimedia teaching, and ideological guidance teaching methods, the paper proposes to strengthen teacher-student relationship building, promote joint education between families and schools, and improve school democratic mechanisms. In addition, it suggests enhancing campus cultural construction to create a civilized campus atmosphere, enriching campus activities, linking theory with practice, and improving overall quality to enhance the spiritual and cultural literacy of middle school students. It concludes that creating a civilized campus policy helps to improve the level of spiritual and cultural literacy among middle school students. Teachers and students should firmly establish Xi Jinpings new era of value orientation, and pay attention to cultivating the inner world of students. Efforts should be made to promote the development of the spiritual and cultural literacy of middle school students through improvements in various aspects.
Advances Toward Word-Sense Disambiguation

The process of assigning the correct meaning to a word with respect to its context is known as Word-Sense Disambiguation (WSD). It is a hard problem in natural language processing. In this paper, we discuss various approaches used to tackle this problem. Research works conduced in recent years to solve this problem are also discussed with special focus on resource poor languages. Technological trends followed in the recent years are analyzed that may help in the identification of future path to search better WSD solutions.
Develop and Design Small Scale UAV

Nowadays, many people want to buy a small-scale unmanned aerial vehicle (UAV) either for recreational purposes, photography and video editing or for air surveillance. The main issue with conventional small-scale UAVs is that they require experience to operate them. Besides that, the control system also plays important role for its flight stability and endurance for small-scale UAV. The weight of the load and travelling speed also being issued for small-scale UAV. As a result, three objectives were formulated based on the problem statement which are to fabricate the appropriate size of a small-scale UAV in term of its mass and frame size; to design the control system and increase the stability of the small-scale UAV; and to test the flight endurance of the small-scale UAV. This research is conducted through the following methodology, which is designing the quadcopter body mainframe, constructing the circuit diagram, developing the RC transmitter and receiver for Arduino and testing the UAV functionality and flight test. Softwares that were involved in this project are Solidworks, Arduino compiler, Proteus, Processing and Matlab. The equipments that are used are Arduino UNO, MPU-6050 sensor, Li-Po 11.1V battery, Electronic Speed Controller (ESC), brushless DC motor, 2.4 GHZ RC transmitter and 6-channel receiver. The limitation also being found from those experiments. Finally, the results and discussion will show and explain about the early sketch for quadcopter body mainframe including dimension for each part and stress-strain diagram using Solidworks. It also includes the simulation and hardware connection for brushless DC motor and MPU-6050 sensor using Proteus, Arduino compiler, Processing and Matlab software.
AMENet:Monocular Depth Estimation Network for Autostereoscopic Display

Abstract Monocular depth estimation has a wide range of applications in the field of autostereoscopic displays, however, accuracy and robustness in complex scenes are still a challenge. In this paper, we propose a depth estimation network for autostereoscopic displays, which aims at improving the accuracy of monocular depth estimation by fusing Vision Transformer (ViT) and Convolutional Neural Network (CNN). Our approach feeds the input image as a sequence of visual features into the ViT module and utilizes its global perception capability to extract high-level semantic features of the image. The relationship between the losses is quantified by adding a weight correction module to improve model robustness. We conducted experimental evaluations on several public datasets, and the results show that AMENet achieves better accuracy and robustness than existing methods in different scenarios and complex conditions. In addition, we conduct a detailed experimental analysis to verify the effectiveness and stability of our method. In summary, AMENet is a promising depth estimation method that can provide higher robustness and accuracy for monocular depth estimation tasks.
Entropic partial orderings of quantum measurements

We investigate four partial orderings on the space of quantum measurements (i.e on POVMs or positive operator valued measures), describing four notions of coarse/fine-ness of measurement. These are the partial orderings induced by: (1) classical post-processing, (2) measured relative entropy, (3) observational entropy, and (4) linear relation of POVMs. The orderings form a hierarchy of implication, where e.g. post-processing relation implies all the others. We show that this hierarchy is strict for general POVMs, with examples showing that all four orderings are strictly inequivalent. Restricted to projective measurements, all are equivalent. Finally we show that observational entropy equality $S_M = S_N$ (for all $\rho$) holds if and only if $M \equiv N$ are post-processing equivalent, which shows that the first three orderings induce identical equivalence classes.
Learning From Free-Text Human Feedback -- Collect New Datasets Or Extend Existing Ones?

Learning from free-text human feedback is essential for dialog systems, but annotated data is scarce and usually covers only a small fraction of error types known in conversational AI. Instead of collecting and annotating new datasets from scratch, recent advances in synthetic dialog generation could be used to augment existing dialog datasets with the necessary annotations. However, to assess the feasibility of such an effort, it is important to know the types and frequency of free-text human feedback included in these datasets. In this work, we investigate this question for a variety of commonly used dialog datasets, including MultiWoZ, SGD, BABI, PersonaChat, Wizards-of-Wikipedia, and the human-bot split of the Self-Feeding Chatbot. Using our observations, we derive new taxonomies for the annotation of free-text human feedback in dialogs and investigate the impact of including such data in response generation for three SOTA language generation models, including GPT-2, LLAMA, and Flan-T5. Our findings provide new insights into the composition of the datasets examined, including error types, user response types, and the relations between them.
Integrating Language Models into Direct Speech Translation: An Inference-Time Solution to Control Gender Inflection

When translating words referring to the speaker, speech translation (ST) systems should not resort to default masculine generics nor rely on potentially misleading vocal traits. Rather, they should assign gender according to the speakers' preference. The existing solutions to do so, though effective, are hardly feasible in practice as they involve dedicated model re-training on gender-labeled ST data. To overcome these limitations, we propose the first inference-time solution to control speaker-related gender inflections in ST. Our approach partially replaces the (biased) internal language model (LM) implicitly learned by the ST decoder with gender-specific external LMs. Experiments on en->es/fr/it show that our solution outperforms the base models and the best training-time mitigation strategy by up to 31.0 and 1.6 points in gender accuracy, respectively, for feminine forms. The gains are even larger (up to 32.0 and 3.4) in the challenging condition where speakers' vocal traits conflict with their gender.