Reviewerly/exHarmony huggingface dataset:
Neural Graphical Models

Probabilistic Graphical Models are often used to understand dynamics of a system. They can model relationships between features (nodes) and the underlying distribution. Theoretically these models can represent very complex dependency functions, but in practice often simplifying assumptions are made due to computational limitations associated with graph operations. In this work we introduce Neural Graphical Models (NGMs) which attempt to represent complex feature dependencies with reasonable computational costs. Given a graph of feature relationships and corresponding samples, we capture the dependency structure between the features along with their complex function representations by using a neural network as a multi-task learning framework. We provide efficient learning, inference and sampling algorithms. NGMs can fit generic graph structures including directed, undirected and mixed-edge graphs as well as support mixed input data types. We present empirical studies that show NGMs’ capability to represent Gaussian graphical models, perform inference analysis of a lung cancer data and extract insights from a real world infant mortality data provided by CDC. Software: NGM code link .
Using deep neural networks as a guide for modeling human planning

When developing models in cognitive science, researchers typically start with their own intuitions about human behavior in a given task and then build in mechanisms that explain additional aspects of the data. This refinement step is often hindered by how difficult it is to distinguish the unpredictable randomness of people's decisions from meaningful deviations between those decisions and the model. One solution for this problem is to compare the model against deep neural networks trained on behavioral data, which can detect almost any pattern given sufficient data. Here, we apply this method to the domain of planning with a heuristic search model for human play in 4-in-a-row, a combinatorial game where participants think multiple steps into the future. Using a data set consisting of 10,874,547 games, we train deep neural networks to predict human moves and find that they accurately do so while capturing meaningful patterns in the data. Thus, deviations between the model and the best network allow us to identify opportunities for model improvement despite starting with a model that has undergone substantial testing in previous work. Based on this analysis, we add three extensions to the model that range from a simple opening bias to specific adjustments regarding endgame planning. Overall, our work demonstrates the advantages of model comparison with a high-performance deep neural network as well as the feasibility of scaling cognitive models to massive data sets for systematically investigating the processes underlying human sequential decision-making.
Efficient Registration-Based Encryption

Registration-based encryption (RBE) was recently introduced as an alternative to identity-based encryption (IBE), to resolve the key-escrow problem: In RBE, the trusted authority is substituted with a weaker entity, called the key curator, who has no knowledge of any secret key. Users generate keys on their own and then publicly register their identities and their corresponding public keys to the key curator. RBE is a promising alternative to IBE, retaining many of its advantages while removing the key-escrow problem, the major drawback of IBE. Unfortunately, all existing constructions of RBE use cryptographic schemes in a non black-box way, which makes them prohibitively expensive. It has been estimated that the size of an RBE ciphertext would be in the order of terabytes (though no RBE has even been implemented).
Evading Watermark based Detection of AI-Generated Content

A generative AI model can generate extremely realistic-looking content, posing growing challenges to the authenticity of information. To address the challenges, watermark has been leveraged to detect AI-generated content. Specifically, a watermark is embedded into an AI-generated content before it is released. A content is detected as AI-generated if a similar watermark can be decoded from it. In this work, we perform a systematic study on the robustness of such watermark-based AI-generated content detection. We focus on AI-generated images. Our work shows that an attacker can post-process a watermarked image via adding a small, human-imperceptible perturbation to it, such that the post-processed image evades detection while maintaining its visual quality. We show the effectiveness of our attack both theoretically and empirically. Moreover, to evade detection, our adversarial post-processing method adds much smaller perturbations to AI-generated images and thus better maintain their visual quality than existing popular post-processing methods such as JPEG compression, Gaussian blur, and Brightness/Contrast. Our work shows the insufficiency of existing watermark-based detection of AI-generated content, highlighting the urgent needs of new methods. Our code is publicly available: https://github.com/zhengyuan-jiang/WEvade.
Verifiable Mix-Nets and Distributed Decryption for Voting from Lattice-Based Assumptions

Cryptographic voting protocols have recently seen much interest from practitioners due to their (planned) use in countries such as Estonia, Switzerland, France, and Australia. Practical protocols usually rely on tested designs such as the mixing-and-decryption paradigm. There, multiple servers verifiably shuffle encrypted ballots, which are then decrypted in a distributed manner. While several efficient protocols implementing this paradigm exist from discrete log-type assumptions, the situation is less clear for post-quantum alternatives such as lattices. This is because the design ideas of the discrete log-based voting protocols do not carry over easily to the lattice setting, due to specific problems such as noise growth and approximate relations.
Threshold Signatures from Inner Product Argument: Succinct, Weighted, and Multi-threshold

Threshold signatures protect the signing key by sharing it among a group of signers so that an adversary must corrupt a threshold number of signers to be able to forge signatures. Existing threshold signatures with succinct signatures and constant verification times do not work if signers have different weights. Such weighted settings are seeing increasing importance in decentralized systems, especially in the Proof-of-Stake blockchains. This paper presents a new paradigm for threshold signatures for pairing and discrete logarithm-based cryptosystems. Our scheme has a compact verification key consisting of only 7 group elements, and a signature consisting of 8 group elements. Verifying the signature requires 8 exponentiations and 8 bilinear pairings. Our scheme supports arbitrary weight distributions among signers and arbitrary thresholds. It requires non-interactive preprocessing after a universal powers-of-tau setup. We prove the security of our scheme in the Algebraic Group Model and implement it using Golang. Our evaluation shows that our scheme achieves a comparable signature size and verification time to a standard (unweighted) threshold signature. Compared to existing multisignature schemes, our scheme has a much smaller public verification key.
Tabi: An Efficient Multi-Level Inference System for Large Language Models

Today's trend of building ever larger language models (LLMs), while pushing the performance of natural language processing, adds significant latency to the inference stage. We observe that due to the diminishing returns of adding parameters to LLMs, a smaller model could make the same prediction as a costly LLM for a majority of queries. Based on this observation, we design Tabi, an inference system with a multi-level inference engine that serves queries using small models and optional LLMs for demanding applications. Tabi is optimized for discriminative models (i.e., not generative LLMs) in a serving framework. Tabi uses the calibrated confidence score to decide whether to return the accurate results of small models extremely fast or re-route them to LLMs. For re-routed queries, it uses attention-based word pruning and weighted ensemble techniques to offset the system overhead and accuracy loss. We implement and evaluate Tabi with multiple tasks and models. Our result shows that Tabi achieves 21%-40% average latency reduction (with comparable tail latency) over the state-of-the-art while meeting LLM-grade high accuracy targets.
Discriminating mixed qubit states with collective measurements

Abstract It is a central fact in quantum mechanics that non-orthogonal states cannot be distinguished perfectly. In general, the optimal measurement for distinguishing such states is a collective measurement. However, to the best our knowledge, collective measurements have not been used to enhance quantum state discrimination to date. One of the main reasons for this is the fact that, in the usual state discrimination setting with equal prior probabilities, at least three copies of a quantum state are required to be measured collectively to outperform separable measurements. This is very challenging experimentally. In this work, by considering unequal prior probabilities, we propose and experimentally demonstrate a protocol for distinguishing two copies of single qubit states using collective measurements which achieves a lower probability of error than can be achieved by any non-entangling measurement. Additionally, we implemented collective measurements on three and four copies of the unknown state and found they performed poorly.
Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation

Light field (LF) depth estimation plays a crucial role in many LF-based applications. Existing LF depth estimation methods consider depth estimation as a regression problem, where a pixel-wise L1 loss is employed to supervise the training process. However, the disparity map is only a sub-space projection (i.e., an expectation) of the disparity distribution, which is essential for models to learn. In this paper, we propose a simple yet effective method to learn the sub-pixel disparity distribution by fully utilizing the power of deep networks, especially for LF of narrow baselines. We construct the cost volume at the sub-pixel level to produce a finer disparity distribution and design an uncertainty-aware focal loss to supervise the predicted disparity distribution toward the ground truth. Extensive experimental results demonstrate the effectiveness of our method. Our method significantly outperforms recent state-of-the-art LF depth algorithms on the HCI 4D LF Benchmark in terms of all four accuracy metrics (i.e., BadPix 0.01, BadPix 0.03, BadPix 0.07, and MSE ×100). The code and model of the proposed method are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/chaowentao/SubFocal</uri> .
LLMs for Financial Advisement: A Fairness and Efficacy Study in Personal Decision Making

As Large Language Model (LLM) based chatbots are becoming more accessible, users are relying on these chatbots for reliable and personalized recommendations in diverse domains, ranging from code generation to financial advisement. In this context, we set out to investigate how such systems perform in the personal finance domain, where financial inclusion has been an overarching stated aim of banks for decades. We test widely used LLM-based chatbots, ChatGPT and Bard, and compare their performance against SafeFinance, a rule-based chatbot built using the Rasa platform. The comparison is across two critical tasks: product discovery and multi-product interaction, where product refers to banking products like Credit Cards, Certificate of Deposits, and Checking Accounts. With this study, we provide interesting insights into the chatbots' efficacy in financial advisement and their ability to provide fair treatment across different user groups. We find that both Bard and ChatGPT can make errors in retrieving basic online information, the responses they generate are inconsistent across different user groups, and they cannot be relied on for reasoning involving banking products. On the other hand, despite their limited generalization capabilities, rule-based chatbots like SafeFinance provide safe and reliable answers to users that can be traced back to their original source. Overall, although the outputs of the LLM-based chatbots are fluent and plausible, there are still critical gaps in providing consistent and reliable financial information.
Multimodal LLMs for Health Grounded in Individual-Specific Data

Foundation large language models (LLMs) have shown an impressive ability to solve tasks across a wide range of fields including health. To effectively solve personalized health tasks, LLMs need the ability to ingest a diversity of data modalities that are relevant to an individual's health status. In this paper, we take a step towards creating multimodal LLMs for health that are grounded in individual-specific data by developing a framework (HeLM: Health Large Language Model for Multimodal Understanding) that enables LLMs to use high-dimensional clinical modalities to estimate underlying disease risk. HeLM encodes complex data modalities by learning an encoder that maps them into the LLM's token embedding space and for simple modalities like tabular data by serializing the data into text. Using data from the UK Biobank, we show that HeLM can effectively use demographic and clinical features in addition to high-dimensional time-series data to estimate disease risk. For example, HeLM achieves an AUROC of 0.75 for asthma prediction when combining tabular and spirogram data modalities compared with 0.49 when only using tabular data. Overall, we find that HeLM outperforms or performs at parity with classical machine learning approaches across a selection of eight binary traits. Furthermore, we investigate the downstream uses of this model such as its generalizability to out-of-distribution traits and its ability to power conversations around individual health and wellness.
Alignment between initial state and mixer improves QAOA performance for constrained optimization

Abstract Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic algorithm, which it can approximate with sufficient depth. However, it is unclear to what extent the lessons from the adiabatic regime apply to QAOA as executed in practice with small to moderate depth. In this paper, we demonstrate that the intuition from the adiabatic algorithm applies to the task of choosing the QAOA initial state. Specifically, we observe that the best performance is obtained when the initial state of QAOA is set to be the ground state of the mixing Hamiltonian, as required by the adiabatic algorithm. We provide numerical evidence using the examples of constrained portfolio optimization problems with both low ( p ≤ 3) and high ( p = 100) QAOA depth. Additionally, we successfully apply QAOA with XY mixer to portfolio optimization on a trapped-ion quantum processor using 32 qubits and discuss our findings in near-term experiments.
A general-purpose organic gel computer that learns by itself

Abstract To build energy minimized superstructures, self-assembling molecules explore astronomical options, colliding ∼10 9 molecules s −1 . Thus far, no computer has used it fully to optimize choices and execute advanced computational theories only by synthesizing supramolecules. To realize it, first, we remotely re-wrote the problem in a language that supramolecular synthesis comprehends. Then, all-chemical neural network synthesizes one helical nanowire for one periodic event. These nanowires self-assemble into gel fibers mapping intricate relations between periodic events in any-data-type, the output is read instantly from optical hologram. Problem-wise, self-assembling layers or neural network depth is optimized to chemically simulate theories discovering invariants for learning. Subsequently, synthesis alone solves classification, feature learning problems instantly with single shot training. Reusable gel begins general-purpose computing that would chemically invent suitable models for problem-specific unsupervised learning. Irrespective of complexity, keeping fixed computing time and power, gel promises a toxic-hardware-free world. One sentence summary: fractally coupled deep learning networks revisits Rosenblatt’s 1950s theorem on deep learning network.
Educational technology: Exploring the convergence of technology and pedagogy through mobility, interactivity, AI, and learning tools

Efforts at the intersection of technology and pedagogy converge upon four pivotal axes that collectively delineate the future educational landscape. These axes, namely mobility, interactivity, artificial intelligence (AI), and technological learning tools like games and augmented reality, encompass the domain of educational transformation. The fusion of these elements necessitates the development of a mobile-interactive paradigm that duly acknowledes the learner's temporal availability and optimal convenience. Currently, technology is already integrated into the educational realm. However, its diverse manifestations across various contexts underscore the urgent need to integrate and amalgamate these facets within pedagogical frameworks that prioritize students' erudition. This research undertakes a comprehensive analysis of multifarious technological modalities and puts forth a harmonized model that could furnish a foundational structure for classroom instruction. Central to this paradigm is the recognition of the paramountcy of intelligent tutoring systems, which serve to democratize access to tutoring. By imbuing these systems with advanced AI capabilities, learners can benefit from personalized and adaptive support, irrespective of their location or socioeconomic background. Furthermore, the significance of conducting technological experiments cannot be understated, as it allows for the exploration of new frontiers and the subsequent application of findings to "teaching-learning models." These models harness a diverse range of interaction patterns to enhance the educational experience. By embracing these transformative elements, educational frameworks can better cater to the evolving needs of learners, while intelligent tutoring systems and ongoing technological experimentation serve as cornerstones in advancing the educational journey.
Correlated two-photon scattering in a one-dimensional waveguide coupled to two- or three-level giant atoms

We investigate the two-photon scattering processes in a one-dimensional waveguide coupled to either a two-level or three-level giant atom. By manipulating the accumulated phase shift between the two coupling points, we are able to effectively modify the characteristics of these scattering processes. Utilizing the Lippmann-Schwinger formalism, we obtain the exact two-photon interacting scattering wave functions of these two systems. Additionally, analytical expressions for the incoherent power spectra and second-order correlations are derived. The incoherent spectrum, which is defined by the correlation of the bound state, provides valuable insights into photon-photon correlations. It serves as a useful indicator of the degree of photon-photon correlation between scattered photons. Furthermore, the second-order correlation function gives a direct measure of the photon-photon correlation. For photons scattered by the two-level giant atom, manipulating the accumulated phase shift allows for improvement of the photon-photon correlation and adjustment of the evolution of the second-order correlation. In the case of the three-level giant atom, the photon-photon correlation can be substantially increased. The photon-photon interaction and correlation distance of the scattered photons can be further enhanced by tuning the accumulated phase shift. Moreover, the statistical properties can be adjusted by the control field.
A Robust S Box Design Using Cyclic Groups and Image Encryption

Modern cryptographic systems use substitution boxes (S-boxes) throughout the encryption process to enhance the security of the plaintext. The integrity of the communication process is ensured by these S-boxes, which are vital in converting the ciphertext back into the original plaintext during the decryption phase. The cryptographic strength of a certain S-box has a substantial impact on the overall security of a given cipher. As a result, many researchers have used innovative construction techniques to create robust S-boxes. The method used in this paper is a novel combination of a specific map on the direct product of cyclic groups of order 16 × 16 and an inversion map of a Galois field with order 256. This strategy aims to produce dynamic S-boxes. The proposed method can produce a large number of strong S-boxes by making little changes to the map’s parameters. Four S-boxes were created and their performance was analyzed using industry standards such as bijectivity, the strict avalanche criterion (SAC), nonlinearity (NL), the bit independence criterion (BIC), linear probability (LAP), and differential probability (DAP). The performance of the recommended S-boxes was compared to that of state-of-the-art S-boxes to show their efficacy. The proposed S-box also exhibits considerable potential as a candidate for modern cryptosystems aiming at securing multimedia information, as shown by a suggested method for protecting the privacy of digital images using it. The effectiveness of the encryption method was then assessed using several tests including contrast, correlation, homogeneity, entropy, energy, Number of Pixel change rate (NPCR), and Unified Average changing intensity (UACI). We observed the efficacy of the suggested method for image encryption by comparing our results with different methods.
A Hybrid Deep Learning Model for Rapid Probabilistic Earthquake Source Parameter Estimation With Displacement Waveforms From a Flexible Set of Seismic or HR-GNSS Stations

The prompt and reliable determination of seismic source parameters, which provide information on earthquake location, magnitude, and focal mechanism, is an imperative undertaking after a destructive earthquake. Deep learning (DL) techniques, owing to their excellent ability to extract pertinent features from waveforms, have gained growing popularity in addressing the challenge of rapid seismic source characterization. However, most existing DL models, particularly those for focal mechanism estimation, rely on a fixed configuration of stations. In this work, we propose a hybrid DL model tailored to probabilistic seismic source characterization using displacement waveforms from a flexible set of seismic or HR-GNSS stations. The proposed model encompasses a convolutional neural network (CNN) module, a graph module, and a mixture density network (MDN) module. Preliminary features are first extracted by the CNN module for each station. The graph module, where a graph attention network (GAT) branch and a maximum aggregation branch are included, then aggregates these features, enabling the adaptive learning of edge information among stations. The MDN module finally calculates the posterior probability distribution of each source parameter. The proposed model performed best compared to benchmark models within synthetic test datasets. The established model was then applied to estimate the source parameters of five events with M > 5 in the 2019 Ridgecrest earthquake sequence and three other representative earthquakes worldwide. Probabilistic estimates that are consistent with known published ground-truth values could be obtained within seconds, verifying the effectiveness and efficiency of the proposed hybrid model in real-world scenarios.