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This paper explores user preferences for sharing sensitive information via telepresence robots using six input methods: pen & paper, smartphone, robot display, speech, whisper, and silent speech. Through a crowdsourced survey and a follow-up user study, it identifies key differences in effort, convenience, privacy, security, and social acceptability. Speech is perceived as the easiest but least secure method, while pen & paper, initially favored, proves inconvenient in practice. Robot display and smartphone consistently rank as the most secure, private, and socially acceptable. Silent speech emerges as a strong alternative, offering greater privacy than other speech-based methods. These findings highlight the need for telepresence robots to support multiple input methods to accommodate diverse user needs and privacy concerns. 

Abstract Engineering stabilized proteins is a fundamental challenge in the development of industrial and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph-transformer framework that achieves SOTA performance on accurately identifying thermodynamically stabilizing mutations. Our framework introduces several innovations to overcome well-known challenges in data scarcity and bias, generalization, and computation time, such as: Thermodynamic Permutations for data augmentation, structural amino acid embeddings to model a mutation with a single structure, a protein structure-specific attention-bias mechanism that makes transformers a viable alternative to graph neural networks. We provide training/test splits that mitigate data leakage and ensure proper model evaluation. Furthermore, to examine our data engineering contributions, we fine-tune ESM2 representations (Prostata-IFML) and achieve SOTA for sequence-based models. Notably, Stability Oracle outperforms Prostata-IFML even though it was pretrained on 2000X less proteins and has 548X less parameters. Our framework establishes a path for fine-tuning structure-based transformers to virtually any phenotype, a necessary task for accelerating the development of protein-based biotechnologies. 

Vector Oblivious Linear Evaluation (VOLE) supports fast and scalable interactive Zero-Knowledge (ZK) proofs. Despite recent improvements to VOLE-based ZK, compiling proof statements to a control-flow oblivious form (e.g., a circuit) continues to lead to expensive proofs. One useful setting where this inefficiency stands out is when the statement is a disjunction of clauses $$\mathcal{L}_1 \lor \cdots \lor \mathcal{L}_B$$. Typically, ZK requires paying the price to handle all $$B$$ branches. Prior works have shown how to avoid this price in communication, but not in computation. Our main result, $$\mathsf{Batchman}$$, is asymptotically and concretely efficient VOLE-based ZK for batched disjunctions, i.e. statements containing $$R$$ repetitions of the same disjunction. This is crucial for, e.g., emulating CPU steps in ZK. Our prover and verifier complexity is only $$\bigO(RB+R|\C|+B|\C|)$$, where $$|\C|$$ is the maximum circuit size of the $$B$$ branches. Prior works' computation scales in $$RB|\C|$$. For non-batched disjunctions, we also construct a VOLE-based ZK protocol, $$\mathsf{Robin}$$, which is (only) communication efficient. For small fields and for statistical security parameter $$\lambda$$, this protocol's communication improves over the previous state of the art ($$\mathsf{Mac'n'Cheese}$$, Baum et al., CRYPTO'21) by up to factor $$\lambda$$. Our implementation outperforms prior state of the art. E.g., we achieve up to $$6\times$$ improvement over $$\mathsf{Mac'n'Cheese}$$ (Boolean, single disjunction), and for arithmetic batched disjunctions our experiments show we improve over $$\mathsf{QuickSilver}$$ (Yang et al., CCS'21) by up to $$70\times$$ and over $$\mathsf{AntMan}$$ (Weng et al., CCS'22) by up to $$36\times$$. 

Abstract The first line of treatment for most solid tumors is surgical resection of the primary tumor with adequate negative margins. Incomplete tumor resections with positive margins account for over 75% of local recurrences and the development of distant metastases. In cases of oral cavity squamous cell carcinoma (OSCC), the rate of successful tumor removal with adequate margins is just 50–75%. Advanced real‐time imaging methods that improve the detection of tumor margins can help improve success rates,overall safety, and reduce the cost. Fluorescence imaging in the second near‐infrared (NIR‐II) window has the potential to revolutionize the field due to its high spatial resolution, low background signal, and deep tissue penetration properties, but NIR‐II dyes with adequate in vivo performance and safety profiles are scarce. A novel NIR‐II fluorophore, XW‐03‐66, with a fluorescence quantum yield (QY) of 6.0% in aqueous media is reported. XW‐03‐66 self‐assembles into nanoparticles (≈80 nm) and has a systemic circulation half‐life ( t 1/2 ) of 11.3 h. In mouse models of human papillomavirus (HPV)+ and HPV‐ OSCC, XW‐03‐66 outperformed indocyanine green (ICG), a clinically available NIR dye, and enabled intraoperative NIR‐II image‐guided resection of the tumor and adjacent draining lymph node with negative margins. In vitro and in vivo toxicity assessments revealed minimal safety concerns for in vivo applications. 

Communication during touch provides a seamless and natural way of interaction between humans and ambient intelligence. Current techniques that couple wireless transmission with touch detection suffer from the problem of selectivity and security, i.e., they cannot ensure communication only through direct touch and not through close proximity. We present  BodyWire-HCI , which utilizes the human body as a wire-like communication channel, to enable human–computer interaction, that for the first time, demonstrates selective and physically secure communication strictly during touch. The signal leakage out of the body is minimized by utilizing a novel, low frequency Electro-QuasiStatic Human Body Communication (EQS-HBC) technique that enables interaction strictly when there is a conductive communication path between the transmitter and receiver through the human body. Design techniques such as capacitive termination and voltage mode operation are used to minimize the human body channel loss to operate at low frequencies and enable EQS-HBC. The demonstrations highlight the impact of  BodyWire-HCI in enabling new human–machine interaction modalities for variety of application scenarios such as secure authentication (e.g., opening a door and pairing a smart device) and information exchange (e.g., payment, image, medical data, and personal profile transfer) through touch (https://www.youtube.com/watch?v=Uwrig2XQIH8). 

The advent of 3D digital printers has led to the evolution of realistic anatomical organ shaped structures that are being currently used as experimental models for rehearsing and preparing complex surgical procedures by clinicians. However, the actual material properties are still far from being ideal, which necessitates the need to develop new materials and processing techniques for the next generation of 3D printers optimized for clinical applications. Recently, the voxelated soft matter technique has been introduced to provide a much broader range of materials and a profile much more like the actual organ that can be designed and fabricated voxel by voxel with high precision. For the practical applications of 3D voxelated materials, it is crucial to develop the novel high precision material manufacturing and characterization technique to control the mechanical properties that can be difficult using the conventional methods due to the complexity and the size of the combination of materials. Here we propose the non-destructive ultrasound effective density and bulk modulus imaging to evaluate 3D voxelated materials printed by J750 Digital Anatomy 3D Printer of Stratasys. Our method provides the design map of voxelated materials and substantially broadens the applications of 3D digital printing in the clinical research area.