Analyzing the Adoption Rate of Local Variable Type Inference in Open-source Java 10 Projects

Type Inference is used in programming languages to improve writability. In this paper, we will be looking more specifically at Local Variable Type Inference (LVTI). For those unfamiliar with LVTI, we will also give an in-depth explanation of what it is and how it works. There is a lot of debate surrounding Type Inference in modern day programming languages. More specifically, whether the costs associated with LVTI outweigh the benefits. It has found its way into many higher-level languages including C#, C++, JavaScript, Swift, Kotlin, Rust, Go, etc. In this paper, we will look at the usefulness of LVTI and its popularity since the release of Java 10. Our study will show that LVTI in Java has not received widespread adoption. We will also explain a possible reason for this is based on the information we have gather from our empirical study which involved statically analyzing 6 popular open source Java 10 projects. We will also discuss different scenarios in which Type Inference can obscure different programming errors

Nonlocal quantum state ensembles and quantum data hiding

We consider the discrimination of bipartite quantum states and establish a relation between nonlocal quantum state ensemble and quantum data hiding processing. Using a bound on optimal local discrimination of bipartite quantum states, we provide a sufficient condition for a bipartite quantum state ensemble to be used to construct a quantum data-hiding scheme. Our results are illustrated by examples in multidimensional bipartite quantum systems.Comment: 11 pages, 4 figure

Entanglement witness and multipartite quantum state discrimination

We consider multipartite quantum state discrimination and show that the minimum-error discrimination by separable measurements is closely related to the concept of entanglement witness. Based on the properties of entanglement witness, we establish some necessary and/or sufficient conditions on minimum-error discrimination by separable measurements. We also provide some conditions on the upper bound of the maximum success probability over all possible separable measurements. Our results are illustrated by examples of multidimensional multipartite quantum states. Finally, we provide a systematic way in terms of the entanglement witness to construct multipartite quantum state ensembles showing nonlocality in state discrimination.Comment: 13 pages, 1 figure. arXiv admin note: substantial text overlap with arXiv:2212.1079

Thermal Area Law in Long-Range Interacting Systems

The area law of the bipartite information measure characterizes one of the most fundamental aspects of quantum many-body physics. In thermal equilibrium, the area law for the mutual information universally holds at arbitrary temperatures as long as the systems have short-range interactions. In systems with power-law decaying interactions, $r^{-\alpha}$ ($r$: distance), conditions for the thermal area law are elusive. In this work, we aim to clarify the optimal condition $\alpha> \alpha_c$ such that the thermal area law universally holds. A standard approach to considering the conditions is to focus on the magnitude of the boundary interaction between two subsystems. However, we find here that the thermal area law is more robust than this conventional argument suggests. We show the optimal threshold for the thermal area law by $\alpha_c= (D+1)/2$ ($D$: the spatial dimension of the lattice), assuming a power-law decay of the clustering for the bipartite correlations. Remarkably, this condition encompasses even the thermodynamically unstable regimes $\alpha < D$. We verify this condition numerically, finding that it is qualitatively accurate for both integrable and non-integrable systems. Unconditional proof of the thermal area law is possible by developing the power-law clustering theorem for $\alpha > D$ above a threshold temperature. Furthermore, the numerical calculation for the logarithmic negativity shows that the same criterion $\alpha > (D+1)/2$ applies to the thermal area law for quantum entanglement.Comment: 16 pages, 6 figure

Folding Rays: a Bimanual Occluded Target Interaction Technique

As Virtual Reality becomes commonplace in the world, it is important for developers to focus on user interaction with the virtual world. Currently, there are limitations to some selection and navigation techniques that have not yet been completely overcome. Focusing specifically on enhancing ray-casting, we present the advanced technique of folding rays which allows for the selection of occluded targets without any unnecessary physical navigation around a virtual environment. By improving upon current approaches, our technique allows for the selection of these targets without any manipulation of the virtual environment itself using rays that can bend at user-determined points. With their potential to be used in conjunction with teleportation as a virtual navigation technique, folding rays can be used in a variety of scenarios to enhance a user's interactive experience in virtual environments

Comparative Analysis of Change Blindness in Virtual Reality and Augmented Reality Environments

Change blindness is a phenomenon where an individual fails to notice alterations in a visual scene when a change occurs during a brief interruption or distraction. Understanding this phenomenon is specifically important for the technique that uses a visual stimulus, such as Virtual Reality (VR) or Augmented Reality (AR). Previous research had primarily focused on 2D environments or conducted limited controlled experiments in 3D immersive environments. In this paper, we design and conduct two formal user experiments to investigate the effects of different visual attention-disrupting conditions (Flickering and Head-Turning) and object alternative conditions (Removal, Color Alteration, and Size Alteration) on change blindness detection in VR and AR environments. Our results reveal that participants detected changes more quickly and had a higher detection rate with Flickering compared to Head-Turning. Furthermore, they spent less time detecting changes when an object disappeared compared to changes in color or size. Additionally, we provide a comparison of the results between VR and AR environments.Comment: This paper is accepted as a conference paper on ISMAR 202

Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation

The view inconsistency problem in score-distilling text-to-3D generation, also known as the Janus problem, arises from the intrinsic bias of 2D diffusion models, which leads to the unrealistic generation of 3D objects. In this work, we explore score-distilling text-to-3D generation and identify the main causes of the Janus problem. Based on these findings, we propose two approaches to debias the score-distillation frameworks for robust text-to-3D generation. Our first approach, called score debiasing, involves gradually increasing the truncation value for the score estimated by 2D diffusion models throughout the optimization process. Our second approach, called prompt debiasing, identifies conflicting words between user prompts and view prompts utilizing a language model and adjusts the discrepancy between view prompts and object-space camera poses. Our experimental results show that our methods improve realism by significantly reducing artifacts and achieve a good trade-off between faithfulness to the 2D diffusion models and 3D consistency with little overhead