Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs

Causal discovery is crucial for causal inference in observational studies: it can enable the identification of valid adjustment sets (VAS) for unbiased effect estimation. However, global causal discovery is notoriously hard in the nonparametric setting, with exponential time and sample complexity in the worst case. To address this, we propose local discovery by partitioning (LDP), a novel nonparametric local discovery algorithm that is tailored for downstream inference tasks while avoiding the pretreatment assumption. LDP is a constraint-based procedure that partitions variables into subsets defined solely by their causal relation to an exposure-outcome pair. Further, LDP returns a VAS for the exposure-outcome pair under causal insufficiency and mild sufficient conditions. Total independence tests is worst-case quadratic in variable count. Asymptotic theoretical guarantees are numerically validated on synthetic graphs. Adjustment sets from LDP yield less biased and more precise average treatment effect estimates than baseline discovery algorithms, with LDP outperforming on confounder recall, runtime, and test count for VAS discovery. Further, LDP ran at least 1300x faster than baselines on a benchmark.
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews

Role-playing agents (RPAs), powered by large language models, have emerged as a flourishing field of applications. However, a key challenge lies in assessing whether RPAs accurately reproduce the personas of target characters, namely their character fidelity. Existing methods mainly focus on the knowledge and linguistic patterns of characters. This paper, instead, introduces a novel perspective to evaluate the personality fidelity of RPAs with psychological scales. Overcoming drawbacks of previous self-report assessments on RPAs, we propose InCharacter, namely Interviewing Character agents for personality tests. Experiments include various types of RPAs and LLMs, covering 32 distinct characters on 14 widely used psychological scales. The results validate the effectiveness of InCharacter in measuring RPA personalities. Then, with InCharacter, we show that state-of-the-art RPAs exhibit personalities highly aligned with the human-perceived personalities of the characters, achieving an accuracy up to 80.7%. Our demo, code, dataset, and results are publicly available at https://github.com/Neph0s/InCharacter.
Diagrammatic approach to excitonic effects on nonlinear optical response

Optical responses of atomically thin 2D materials are greatly influenced by electron-hole interactions. It is by far established that exciton signatures can be well-identified in the optical absorption spectrum of quasi-2D materials. However, the same level of understanding of excitonic effects on nonlinear optical responses and the ability to compute them accurately is still much desired. Based on the functional integral formalisms and working in the velocity gauge, we introduce a convenient Feynman diagram approach for calculating nonlinear responses including excitonic effects. By dressing electron-photon interactions with electron-hole ladder diagrams, we derive an expression for second-order optical responses and provide a comprehensive description of excitonic effects. We apply our approach to a monolayer h-BN model and show qualitative changes in the second harmonic generation spectrum when comparing with results assuming independent particles. Our approach can be readily extended to higher order optical responses and is feasible for first-principles calculations.
An Energy-Efficient Near-Data Processing Accelerator for DNNs that Optimizes Data Accesses

The constant growth of DNNs makes them challenging to implement and run efficiently on traditional compute-centric architectures. Some accelerators have attempted to add more compute units and on-chip buffers to solve the memory wall problem without much success, and sometimes even worsening the issue since more compute units also require higher memory bandwidth. Prior works have proposed the design of memory-centric architectures based on the Near-Data Processing (NDP) paradigm. NDP seeks to break the memory wall by moving the computations closer to the memory hierarchy, reducing the data movements and their cost as much as possible. The 3D-stacked memory is especially appealing for DNN accelerators due to its high-density/low-energy storage and near-memory computation capabilities to perform the DNN operations massively in parallel. However, memory accesses remain as the main bottleneck for running modern DNNs efficiently. To improve the efficiency of DNN inference we present QeiHaN, a hardware accelerator that implements a 3D-stacked memory-centric weight storage scheme to take advantage of a logarithmic quantization of activations. In particular, since activations of FC and CONV layers of modern DNNs are commonly represented as powers of two with negative exponents, QeiHaN performs an implicit in-memory bit-shifting of the DNN weights to reduce memory activity. Only the meaningful bits of the weights required for the bit-shift operation are accessed. Overall, QeiHaN reduces memory accesses by 25\% compared to a standard memory organization. We evaluate QeiHaN on a popular set of DNNs. On average, QeiHaN provides $4.3x$ speedup and $3.5x$ energy savings over a Neurocube-like accelerator.
ControlLLM: Augment Language Models with Tools by Searching on Graphs

We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks. Despite the remarkable performance of LLMs, they still struggle with tool invocation due to ambiguous user prompts, inaccurate tool selection and parameterization, and inefficient tool scheduling. To overcome these challenges, our framework comprises three key components: (1) a \textit{task decomposer} that breaks down a complex task into clear subtasks with well-defined inputs and outputs; (2) a \textit{Thoughts-on-Graph (ToG) paradigm} that searches the optimal solution path on a pre-built tool graph, which specifies the parameter and dependency relations among different tools; and (3) an \textit{execution engine with a rich toolbox} that interprets the solution path and runs the tools efficiently on different computational devices. We evaluate our framework on diverse tasks involving image, audio, and video processing, demonstrating its superior accuracy, efficiency, and versatility compared to existing methods. The code is at https://github.com/OpenGVLab/ControlLLM.
Unsupervised Representation Learning for Diverse Deformable Shape Collections

We introduce a novel learning-based method for encoding and manipulating 3D surface meshes. Our method is specifically designed to create an interpretable embedding space for deformable shape collections. Unlike previous 3D mesh autoencoders that require meshes to be in a 1-to-1 correspondence, our approach is trained on diverse meshes in an unsupervised manner. Central to our method is a spectral pooling technique that establishes a universal latent space, breaking free from traditional constraints of mesh connectivity and shape categories. The entire process consists of two stages. In the first stage, we employ the functional map paradigm to extract point-to-point (p2p) maps between a collection of shapes in an unsupervised manner. These p2p maps are then utilized to construct a common latent space, which ensures straightforward interpretation and independence from mesh connectivity and shape category. Through extensive experiments, we demonstrate that our method achieves excellent reconstructions and produces more realistic and smoother interpolations than baseline approaches.
Artificial light at night (ALAN) causes shifts in soil communities and functions

Artificial light at night (ALAN) is increasing worldwide, but its effects on the soil system have not yet been investigated. We tested the influence of experimental manipulation of ALAN on two taxa of soil communities (microorganisms and soil nematodes) and three aspects of soil functioning (soil basal respiration, soil microbial biomass and carbon use efficiency) over four and a half months in a highly controlled Ecotron facility. We show that during peak plant biomass, increasing ALAN reduced plant biomass and was also associated with decreased soil water content. This further reduced soil respiration under high ALAN at peak plant biomass, but microbial communities maintained stable biomass across different levels of ALAN and times, demonstrating higher microbial carbon use efficiency under high ALAN. While ALAN did not affect microbial community structure, the abundance of plant-feeding nematodes increased and there was homogenization of nematode communities under higher levels of ALAN, indicating that soil communities may be more vulnerable to additional disturbances at high ALAN. In summary, the effects of ALAN reach into the soil system by altering soil communities and ecosystem functions, and these effects are mediated by changes in plant productivity and soil water content at peak plant biomass. This article is part of the theme issue ‘Light pollution in complex ecological systems’.
Solving Linguistic Olympiad Problems with Tree-of-Thought Prompting

In this study, we delve into the efficacy of the Tree-of-Thought Prompting technique as a mechanism to address linguistic challenges and augment the reasoning capabilities of expansive language models. Specifically, we scrutinize the reasoning prowess of the Generative Pre-trained Transformer (GPT) model, which has garnered significant attention within the research and practitioner community. Utilizing the Tree-of-Thought Prompting methodology, we assess its utility in enhancing both the precision and response latency of the GPT model, especially for Linguistic Olympiad tasks demanding elevated reasoning competencies. Concurrently, we delineate inherent limitations within this approach and proffer avenues for future research to refine and optimize it.
Accelerating Hakka Speech Recognition Research and Development Using the Whisper Model

Language preservation has become increasingly urgent with globalization and rapid technological advancement. Minority languages, such as Hakka, are particularly vulnerable. This study aims to expedite the research and development of Hakka speech recognition by integrating Open AI's Whisper model with online Hakka speech resources. This study developed an efficient speech recognition model for Hakka, a low-resource language, and provided insights into its applications for preserving and popularizing Hakka culture. This paper addresses the challenge of building an Automatic Speech Recognition (ASR) model for the Hakka language. Utilizing Open AI's Whisper technology, this study presents a complete workflow for training and deploying a Hakka language ASR model. The end product could be a vital tool for digital Hakka language education and intelligent living applications.
CrowNER at ROCLING 2023 MultiNER-Health Task: Enhancing NER Task with GPT Paraphrase Augmentation on Sparsely Labeled Data

In this research, we utilized the training dataset from the ROCLING 2023 Chinese Multi-genre Named Entity Recognition in the Healthcare Domain, which comprises the Chinese HealthNER Corpus (Lee and Lu, 2021) and the ROCLING 2022 CHNER Dataset (Lee et al., 2022), along with the test set (Lee et al., 2023). The objective was to address the named entity recognition task within the Chinese healthcare domain. Our initial step involved preprocessing the training dataset. We identified instances in the training set where sentences with identical structural patterns exhibited ambiguities and errors in named entity definitions. Prioritizing data validation, we manually excluded erroneous entries. In specialized domains such as medicine, domain-specific terminologies and proprietary names are often defined within sentences as merged labels, rather than separate ones. Thus, we employed the ’Entity Relationship Construction and Merging Strategies’ approach to consolidate related named entities. Subsequently,
The Taiwan AI Labs Hakka ASR System for Formosa Speech Recognition Challenge 2023

This paper introduces the architecture and training approach of the Taiwan AI Labs Hakka automatic speech recognition (ASR) system for the Formosa Speech Recognition Challenge 2023 (FSR2023). Overall, this Hakka ASR system consists of an acoustic model trained based on the HuBERT (Hsu et al., 2021) model and a 5-gram language model. The HuBERT acoustic model is built upon the open-source sequence modeling toolkit, fairseq (Ott et al., 2019), while the language model is implemented using KenLM (Heafield, 2011). In the end, this system achieved a CER of 8 . 28 % and a WER of 3 . 14 % on track1 (character) and track2 (pinyin) of the pilot test dataset for the FSR2023, respectively. In the FSR2023 final dataset, the system achieved a CER of 30 . 85 % (reading: 10 . 85 %, spon-taneous: 33 . 94 %) and a WER of 17 . 93 % (reading: 6 . 16 %, spontaneous: 19 . 66 %) on track1 (character) and track2 (pinyin), respectively.
Is GPT-4 a Good Islamic Expert for Answering Quran Questions?

In this research, we investigated GPT-4 as a question-answering model for the Holy Quran. As a first step, we built the Quran question–answer pair (QUQA) dataset, comprising 2,189 questions, and made it freely available via our repository. This dataset was then used to benchmark the performance of the current Generative Pre-trained Transformer 4 (GPT-4) model from the OpenAI research laboratory. The results show that GPT-4 did not do well with this dataset, with a 0.23 partial Average Precision (pAP) score, 0.26 F1@1 score, and 0.19 Exact Match (EM) score. Therefore, further improvement is needed for Classical Arabic responses generated by GPT model.
Investigating Cross-Institutional Recognition of Cancer Registration Items: A Case Study on Catastrophic Forgetting

A cancer registry is a critical database for cancer research, which require diverse domain knowledge and manual extraction of vital information from patient records for surveillance. In order to building a real-time and high-quality cancer registry database, a named entity recognition (NER) model based on bidirectional long short-term memory (BiLSTM)-conditional random fields (CRFs) to automatically extract 14 cancer registry items from unstructured pathology reports was developed for five hospitals. Because not all hospitals have sufficient training data, so that we apply transfer learning to develop our models for different hospitals. However, catastrophic forgetting leads to poor performance of the transferred model on the source hospital. To address this issue, we study the effectiveness of applying the elastic weight consolidation (EWC) method for the extraction of cancer registry items from the unstructured pathology reports of colorectal cancer to mitigate the occurrence of catastrophic forgetting. In our results, we observe that effective parameter settings can reduce the impact of catastrophic forgetting.
Detecting transthyretin amyloid cardiomyopathy (ATTR-CM) using machine learning: an evaluation of the performance of an algorithm in a UK setting

Objective The aim of this study was to evaluate the potential real-world application of a machine learning (ML) algorithm, developed and trained on heart failure (HF) cohorts in the USA, to detect patients with undiagnosed wild type cardiac amyloidosis (ATTRwt) in the UK. Design In this retrospective observational study, anonymised, linked primary and secondary care data (Clinical Practice Research Datalink GOLD and Hospital Episode Statistics, respectively, were used to identify patients diagnosed with HF between 2009 and 2018 in the UK. International Classification of Diseases (ICD)-10 clinical modification codes were matched to equivalent Read (primary care) and ICD-10 WHO (secondary care) diagnosis codes used in the UK. In the absence of specific Read or ICD-10 WHO codes for ATTRwt, two proxy case definitions (definitive and possible cases) based on the degree of confidence that the contributing codes defined true ATTRwt cases were created using ML. Primary outcome measure Algorithm performance was evaluated primarily using the area under the receiver operating curve (AUROC) by comparing the actual versus algorithm predicted case definitions at varying sensitivities and specificities. Results The algorithm demonstrated strongest predictive ability when a combination of primary care and secondary care data were used (AUROC: 0.84 in definitive cohort and 0.86 in possible cohort). For primary care or secondary care data alone, performance ranged from 0.68 to 0.78. Conclusion The ML algorithm, despite being developed in a US population, was effective at identifying patients that may have ATTRwt in a UK setting. Its potential use in research and clinical care to aid identification of patients with undiagnosed ATTRwt, possibly enabling earlier diagnosis in the disease pathway, should be investigated.
Mid-Holocene humid periods reconstructed from calcite varves of the Lake Woserin sediment record (north-eastern Germany)

Time-series of varve properties and geochemistry were established from varved sediments of Lake Woserin (north-eastern Germany) covering the recent period AD 2010–1923 and the mid-Holocene time-window 6400–4950 varve years before present (vyr BP) using microfacies analyses, x-ray fluorescence (µ-XRF) scanning, microscopic varve chronology, and 14C dating. The microscopic varve chronology was compared with a macroscopic varve chronology for the same sediment interval. Calcite layer thickness during the recent period is significantly correlated to increases in local annual precipitation (r = 0.46, p = 0.03) and reduced air-pressure (r = −0.72, p < 0.0001). Meteorologically consistent with enhanced precipitation at Lake Woserin, a composite 500 hPa anomaly map for years with >1 standard deviation calcite layer thickness depicts a negative wave train air-pressure anomaly centered over southern Europe, with north-eastern Germany at its northern frontal zone. Three centennial-scale intervals of thicker calcite layers around the mid-Holocene periods 6200–5900, 5750–5400, and 5300–4950 vyr BP might reflect humid conditions favoring calcite precipitation through the transport of Ca2+ ions into Lake Woserin, synchronous to wetter conditions in Europe. Calcite layer thickness oscillations of about 88 and 208 years resemble the solar Gleissberg and Suess cycles suggesting that the recorded hydroclimate changes in north-eastern Germany are modified by solar influences on synoptic-scale atmospheric circulation. However, parts of the periods of thicker calcite layers around 5750–5400 and 5200 vyr BP also coincide with enhanced human catchment activity at Lake Woserin. Therefore, calcite precipitation during these time-windows might have further been favored by anthropogenic deforestation mobilizing Ca2+ ions and/or lake eutrophication.
Spatial User Clustering and Power Control for Downlink MIMO-NOMA Systems

The proliferation of Internet of Things (IoT) devices and the attractive underlying market has motivated the integration of IoT into the fifth generation (5G) of wireless communication. As the number of connected devices continues to increase, wireless communication networks are facing challenges in terms of throughput and massive connectivity. To tackle these challenges, this paper investigates the utilization of Massive Multi-Input Multi-Output (mMIMO) over millimeter-wave (mmWave) frequency bands as well as Non-Orthogonal Multiple Access (NOMA), which is considered a key technology enabling multiple users to share the same frequency/time/code resource. Specifically, it is performed to separate users served through the same beam in IoT scenarios where the number of active users exceeds the number of Radio Frequency (RF) chains at the Base Station (BS). By incorporating NOMA on top of MIMO, the system's spectral efficiency can be increased due to the introduction of additional degrees of freedom. We propose two joint user clustering and power control algorithms to address the aforementioned challenges. The proposed algorithms take into account the spatial channel correlation between users to maximize the number of allocated users while considering their Quality of Service (QoS) requirements. Extensive simulations illustrate that the proposed techniques improve the efficiency of wireless resource utilization, especially when there is a large set of active users and a limited number of RF chains.
Heat Simulation on Meshless Crafted-Made Shapes

Interactive shape crafting is an increasingly popular feature in video games, offering players a sense of freedom and personalization. In this work, we propose to combine a stochastic simulation approach to solve the heat equation on Implicit Surface, enabling crafting-ready shapes. Our simulation relies on the "Walk on Sphere" (WoS) approach allowing to solve the asymptotic solution of the heat PDE at any point in space without the need for an explicit mesh structure. To enable interactivity when the shape is moved near a heat source, we propose the integration of time-evolving modifiers. Firstly, using the separation of variables over the PDE enables the approximation of the heating evolution using an additional exponential time variation. Then, we procedurally attach local secondary heat sources to the surface for smooth cool-down. We demonstrate the effectiveness our approach on blended-material shapes generated using CSG operations, combining spatially-varying thermal diffusivity. Overall, our method offers a promising avenue for incorporating often-neglected physical interactions, such as heat-related phenomena, into video games with complex and customizable shapes.
A Novel Multimodal Behavior Prediction Method for Automated Vehicles

Ensuring the safe and effective path planning of automated vehicles in uncertain conditions hinges on the precise and dependable anticipation of future movements of nearby vehicles, coupled with a comprehensive grasp of the surrounding environment. This difficulty escalates significantly in dynamic and complex scenarios, such as unsignalized intersections, where there are no traffic lights to regulate vehicle behavior, and where multiple lanes are not available to infer drivers intentions based on their chosen lane. In this work, we design a novel method based on deep learning to anticipate vehicle behaviors at unsignalized intersections. Our approach accounts for the inherent uncertainty and multimodality of vehicle behavior by generating multiple potential outcomes. Our introduced model combines temporal convolutions with a mixture density layer to achieve this. We further cluster the obtained potential modes into feasible maneuvers, ranking them according to their probabilities. To evaluate the performance of our multimodal behavior prediction model, we conducted comprehensive evaluations using a substantial real-world dataset comprising over 23000 trajectories. The evaluation results underscore the better performance of our behavior prediction approach when compared to various baseline and state-of-the-art models.