Miracle-dz/UniFAR-FTUs huggingface dataset:
A tight lower bound on non-adaptive group testing estimation

Efficiently counting or detecting defective items is a crucial task in various fields ranging from biological testing to quality control to streaming algorithms. The \emph{group testing estimation problem} concerns estimating the number of defective elements $d$ in a collection of $n$ total within a given factor. We primarily consider the classical query model, in which a query reveals whether the selected group of elements contains a defective one. We show that any non-adaptive randomized algorithm that estimates the value of $d$ within a constant factor requires $\Omega(\log n)$ queries. This confirms that a known $O(\log n)$ upper bound by Bshouty (2019) is tight and resolves a conjecture by Damaschke and Sheikh Muhammad (2010). Additionally, we prove similar matching upper and lower bounds in the threshold query model.
Identification of nonfunctional PABPC1L causing oocyte maturation abnormalities and early embryonic arrest in female primary infertility

Oocyte maturation arrest, fertilization failure, and early embryonic arrest are important causes of female infertility, whereas the genetic events that contribute to these processes are largely unknown. Loss‐of‐function of PABPC1L in mice has been suggested to cause female infertility involved in the absence of mature oocytes or embryos in vivo or in vitro. However, the role of PABPC1L in human female reproduction remains largely elusive. In this study, we identified a homozygous missense mutation (c.536G>A, p.R179Q) and a compound heterozygous mutation (c.793C>T, p.R265W; c.1201C>T, p.Q401*) in PABPC1L in two unrelated infertile females characterized by recurrent oocyte maturation abnormalities and early embryonic arrest. These variants resulted in nonfunctional PABPC1L protein and were associated with impaired chromatin configuration and transcriptional silencing in GV oocytes. Moreover, the binding capacity of mutant PABPC1L to mRNAs related to oocyte maturation and early embryonic development was decreased significantly. Our findings revealed novel PABPC1L mutations causing oocyte maturation abnormalities and early embryonic arrest, confirming the essential role of PABPC1L in human female fertility.
Digital driving data can track driving exposure and quality of life in Parkinson’s disease

Abstract Objective Parkinson’s disease (PD) impairs motor and non-motor functions. Driver strategies to compensate for impairments, like avoiding driving in risky environments, may reduce on-road risk at the cost of decreasing driver mobility, independence, and quality of life (QoL). It is unclear how PD symptoms link to driving risk exposure, strategies, and QoL. We assessed associations between PD symptoms and driving exposure (1) overall, (2) in risky driving environments, and (3) in relationship to QoL. Methods Twenty-eight drivers with idiopathic PD were assessed using the Movement Disorders Society–Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) and RAND 36-Item Short Form Health Survey (SF-36). Real-world driving was monitored for 1 month. Overall driving exposure (miles driven) and risky driving exposure (miles driven in higher risk driving environments) were assessed across PD symptom severity. High traffic, night, and interstate roads were considered risky environments. Results 18,642 miles (30,001 km) driven were collected. Drivers with PD with worse motor symptoms (MDS-UPDRS Part III) drove more overall (b = 0.17, P < .001) but less in risky environments (night: b = −0.35, P < .001; interstate roads: b = −0.23, P < .001; high traffic: b = −0.14, P < .001). Worse non-motor daily activities symptoms (MDS-UPDRS Part I) did not affect overall driving exposure (b = −0.05, P = .43) but did affect risky driving exposure. Worse non-motor daily activities increased risk exposure to interstate (b = 0.36, P < .001) and high traffic (b = 0.09, P = .03) roads while reducing nighttime risk exposure (b = −0.15, P = .01). Daily activity impacts from motor symptoms (MDS-UPDRS Part II) did not affect distance driven. Reduced driving exposure (number of drives per day) was associated with worse physical health–related QoL (b = 2.87, P = .04). Conclusions Results provide pilot data revealing specific PD symptom impacts on driving risk exposure and QoL. Drivers with worse non-motor impairments may have greater risk exposure. In contrast, drivers with worse motor impairments may have reduced driver risk exposure. Reduced driving exposure may worsen physical health–related QoL. Results show promise for using driving to inform clinical care.
Controllable Dynamic Appearance for Neural 3D Portraits

Recent advances in Neural Radiance Fields (NeRFs) have made it possible to reconstruct and reanimate dynamic portrait scenes with control over head-pose, facial expressions and viewing direction. However, training such models assumes photometric consistency over the deformed region e.g. the face must be evenly lit as it deforms with changing head-pose and facial expression. Such photometric consistency across frames of a video is hard to maintain, even in studio environments, thus making the created reanimatable neural portraits prone to artifacts during reanimation. In this work, we propose CoDyNeRF, a system that enables the creation of fully controllable 3D portraits in real-world capture conditions. CoDyNeRF learns to approximate illumination dependent effects via a dynamic appearance model in the canonical space that is conditioned on predicted surface normals and the facial expressions and head-pose deformations. The surface normals prediction is guided using 3DMM normals that act as a coarse prior for the normals of the human head, where direct prediction of normals is hard due to rigid and non-rigid deformations induced by head-pose and facial expression changes. Using only a smartphone-captured short video of a subject for training, we demonstrate the effectiveness of our method on free view synthesis of a portrait scene with explicit head pose and expression controls, and realistic lighting effects. The project page can be found here: http://shahrukhathar.github. io/2023/08/22/CoDyNeRF.html
MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback

To solve complex tasks, large language models (LLMs) often require multiple rounds of interactions with the user, sometimes assisted by external tools. However, current evaluation protocols often emphasize benchmark performance with single-turn exchanges, neglecting the nuanced interactions among the user, LLMs, and external tools, while also underestimating the importance of natural language feedback from users. These oversights contribute to discrepancies between research benchmark evaluations and real-world use cases. We introduce MINT, a benchmark that evaluates LLMs' ability to solve tasks with multi-turn interactions by (1) using tools and (2) leveraging natural language feedback. To ensure reproducibility, we provide an evaluation framework where LLMs can access tools by executing Python code and receive users' natural language feedback simulated by GPT-4. We repurpose a diverse set of established evaluation datasets focusing on reasoning, coding, and decision-making and carefully curate them into a compact subset for efficient evaluation. Our analysis of 20 open- and closed-source LLMs offers intriguing findings. (a) LLMs generally benefit from tools and language feedback, with performance gains (absolute, same below) of 1-8% for each turn of tool use and 2-17% with natural language feedback. (b) Better single-turn performance does not guarantee better multi-turn performance. (c) Surprisingly, on the LLMs evaluated, supervised instruction-finetuning (SIFT) and reinforcement learning from human feedback (RLHF) generally hurt multi-turn capabilities. We expect MINT can help measure progress and incentivize research in improving LLMs' capabilities in multi-turn interactions, especially for open-source communities where multi-turn human evaluation can be less accessible compared to commercial LLMs with a larger user base.
AutoDiffusion: Training-Free Optimization of Time Steps and Architectures for Automated Diffusion Model Acceleration

Diffusion models are emerging expressive generative models, in which a large number of time steps (inference steps) are required for a single image generation. To accelerate such tedious process, reducing steps uniformly is considered as an undisputed principle of diffusion models. We consider that such a uniform assumption is not the optimal solution in practice; i.e., we can find different optimal time steps for different models. Therefore, we propose to search the optimal time steps sequence and compressed model architecture in a unified framework to achieve effective image generation for diffusion models without any further training. Specifically, we first design a unified search space that consists of all possible time steps and various architectures. Then, a two stage evolutionary algorithm is introduced to find the optimal solution in the designed search space. To further accelerate the search process, we employ FID score between generated and real samples to estimate the performance of the sampled examples. As a result, the proposed method is (i). training-free, obtaining the optimal time steps and model architecture without any training process; (ii). orthogonal to most advanced diffusion samplers and can be integrated to gain better sample quality. (iii). generalized, where the searched time steps and architectures can be directly applied on different diffusion models with the same guidance scale. Experimental results show that our method achieves excellent performance by using only a few time steps, e.g. 17.86 FID score on ImageNet 64 × 64 with only four steps, compared to 138.66 with DDIM.
Effects of Explanation Strategies to Resolve Failures in Human-Robot Collaboration

DH Despite significant improvements in robot capabilities, they are likely to fail in human-robot collaborative tasks due to high unpredictability in human environments and varying human expectations. In this work, we explore the role of explanation of failures by a robot in a human-robot collaborative task. We present a user study incorporating common failures in collaborative tasks with human assistance to resolve the failure. In the study, a robot and a human work together to fill a shelf with objects. Upon encountering a failure, the robot explains the failure and the resolution to overcome the failure, either through handovers or humans completing the task. The study is conducted using different levels of robotic explanation based on the failure action, failure cause, and action history, and different strategies in providing the explanation over the course of repeated interaction. Our results show that the success in resolving the failures is not only a function of the level of explanation but also the type of failures. Furthermore, while novice users rate the robot higher overall in terms of their satisfaction with the explanation, their satisfaction is not only a function of the robot’s explanation level at a certain round but also the prior information they received from the robot.
VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition

Spiking Neural Networks (SNNs) are at the forefront of neuromorphic computing thanks to their potential energy-efficiency, low latencies, and capacity for continual learning. While these capabilities are well suited for robotics tasks, SNNs have seen limited adaptation in this field thus far. This work introduces a SNN for Visual Place Recognition (VPR) that is both trainable within minutes and queryable in milliseconds, making it well suited for deployment on compute-constrained robotic systems. Our proposed system, VPRTempo, overcomes slow training and inference times using an abstracted SNN that trades biological realism for efficiency. VPRTempo employs a temporal code that determines the timing of a single spike based on a pixel's intensity, as opposed to prior SNNs relying on rate coding that determined the number of spikes; improving spike efficiency by over 100%. VPRTempo is trained using Spike-Timing Dependent Plasticity and a supervised delta learning rule enforcing that each output spiking neuron responds to just a single place. We evaluate our system on the Nordland and Oxford RobotCar benchmark localization datasets, which include up to 27k places. We found that VPRTempo's accuracy is comparable to prior SNNs and the popular NetVLAD place recognition algorithm, while being several orders of magnitude faster and suitable for real-time deployment -- with inference speeds over 50 Hz on CPU. VPRTempo could be integrated as a loop closure component for online SLAM on resource-constrained systems such as space and underwater robots.
OpenMSD: Towards Multilingual Scientific Documents Similarity Measurement

We develop and evaluate multilingual scientific documents similarity measurement models in this work. Such models can be used to find related works in different languages, which can help multilingual researchers find and explore papers more efficiently. We propose the first multilingual scientific documents dataset, Open-access Multilingual Scientific Documents (OpenMSD), which has 74M papers in 103 languages and 778M citation pairs. With OpenMSD, we pretrain science-specialized language models, and explore different strategies to derive"related"paper pairs to fine-tune the models, including using a mixture of citation, co-citation, and bibliographic-coupling pairs. To further improve the models' performance for non-English papers, we explore the use of generative language models to enrich the non-English papers with English summaries. This allows us to leverage the models' English capabilities to create better representations for non-English papers. Our best model significantly outperforms strong baselines by 7-16% (in mean average precision).
Race, class, risk and trust: Risk communication in post-Katrina New Orleans

The main focus of this study is on risk communication about dangerous environmental events in post-Katrina New Orleans, and the influence that race and class have on that process. It seeks to determine the assessment of New Orleans residents of the various possible sources of risk information that are available to them, and how race and socioeconomic class affect their level of trust in those sources. A 37-question telephone survey was conducted in Orleans Parish by the Public Policy Research Lab at Louisiana State University in June and July of 2012. There were 414 completed surveys, with 278 landline telephone and 136 cell phone respondents. The overall margin of error was +/4.8% at a 95% confidence interval. SPSS software was used to analyze data testing four hypotheses for each of two research questions on risk assessment: the first on race, the second on class (socioeconomic status, or “SES”). An ANCOVA was used to test the hypotheses of the first research question, while Pearson’s Product Moment Correlation Coefficient Test was used to test the second. A trust index was constructed by aggregating Likert-scales responses to questions on the elements of trust in sources, and an SES index was constructed by aggregating education and income responses. After controlling for SES, Whites were shown to have significantly more trust in mass media, local community leaders and spokespersons, and interpersonal communication than Blacks did. After controlling for race, SES was found to be negatively correlated with trust in local TV news, non-news websites, and social media sites. Tests of correlation were also run on frequency of media use and trust in sources of risk messages.
Intraindividual variability in sleep duration and college degree attainment

ABSTRACT The objective of the current study was to examine the relationship between sleep characteristics and college degree attainment. Participants were 968 college students (72% female; mean age 19.7 [1.7]). Participants completed a psychosocial and sleep questionnaire battery followed by one week of daily sleep diaries. Academic degree completion data was obtained from the university registrar 10 years later. Logistic regression examined whether mean and variability in sleep duration and sleep efficiency and insomnia symptoms predicted degree attainment, adjusting for age, gender, semester, grade point average (GPA), and perceived stress. The strongest predictors of degree attainment were female gender (OR = 0.67), greater age (OR = 1.32), GPA (OR = 1.97), and lower intraindividual variability in sleep duration (OR = 0.99). Results highlight the importance of examining variability in sleep duration in addition to mean sleep duration in predicting college retention. Future research should use a combination of objective and subjective measures to explore the impact of sleep factors, including variability, on degree completion and other academic metrics.
Comparative analysis of methodologies for predicting overall survival in patients with non‐small cell lung cancer based on the number and rate of resected positive lymph nodes: A study based on the SEER database for 2010 through 2019

Lymph node (LN) metastasis is crucial in non‐small cell lung cancer (NSCLC) prognosis and treatment, but the TNM system lacks LN quantity consideration. Our goal is to investigate the role of positive LNs (nPLN) and positive LN rate (LNR) in overall survival (OS) and assess whether they offer higher value in prognostic assessment of NSCLC than N‐stage.
Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca

Foundational large language models (LLMs) can be instruction-tuned to perform open-domain question answering, facilitating applications like chat assistants. While such efforts are often carried out in a single language, we empirically analyze cost-efficient strategies for multilingual scenarios. Our study employs the Alpaca dataset and machine translations of it to form multilingual data, which is then used to tune LLMs through either low-rank adaptation or full-parameter training. Under a controlled computation budget, comparisons show that multilingual tuning is on par or better than tuning a model for each language. Furthermore, multilingual tuning with downsampled data can be as powerful and more robust. Our findings serve as a guide for expanding language support through instruction tuning.
The EP Elections 2014 and their consequences: a further step towards EU Parliamentarism?

espanolEn el nombramiento del presidente de la Comision de 2014, el Parlamento Europeo se impuso al Consejo Europeo. Esto implica un reajuste institucional hacia una estructura mas parlamentaria de facto? Despues de dar una descripcion general de nombramientos anteriores, analizamos la investidura de Jean-Claude Juncker con enfoque en la nueva relacion entre la Comision y el Parlamento, asi como en los procedimientos dentro del PE. Argumentamos que los eventos de 2014 representan una continuacion de la tradicion del Parlamento de interpretar las disposiciones del tratado extensamente en su propio beneficio. Marcarlos como una encrucijada critica para la parlamentarizacion de la UE seria demasiado precipitado. Las consecuencias a largo plazo dependeran de la conducta del presidente de la Comision, la voluntad de los partidos nacionales a renunciar a su influencia a traves de sus eurodiputados y la cohesion de la coalicion parlamentaria. EnglishIn the appointment of the Commission President 2014, the European Parliament successfully asserted itself against the European Council. Does this imply an institutional readjustment towards a de facto more parliamentary structure? After giving an overview of previous appointment procedures, we examine Jean-Claude Juncker's investiture focussing on the new linkage between the Commission and the Parliament as well as the internal politics of the EP. We argue that the 2014 events represent a continuation of the Parliament's tradition to interpret the treaty provisions extensively in its own benefit. Still, it would be too early to mark them as a critical juncture for EU parliamentarism. The long-term implications depend on the Commission President's conduct, national parties' willingness to relinquish their leverage via their MEPs and the parliamentary coalition's cohesiveness.
Exploring the Influence of Information Entropy Change in Learning Systems

In this work, we explore the influence of entropy change in deep learning systems by adding noise to the inputs/latent features. The applications in this paper focus on deep learning tasks within computer vision, but the proposed theory can be further applied to other fields, such as natural language processing (NLP). Noise is conventionally viewed as a harmful perturbation in various deep learning architectures, such as convolutional neural networks (CNNs) and vision transformers (ViTs), as well as different learning tasks like image classification and transfer learning. However, this work aims to rethink whether the conventional proposition always holds. We demonstrate that specific noise can boost the performance of various deep architectures under certain conditions. We theoretically prove the enhancement gained from positive noise by reducing the task complexity defined by information entropy and experimentally show the significant performance gain in large image datasets, such as the ImageNet. Herein, we use the information entropy to define the complexity of the task. We categorize the noise into two types, positive noise (PN) and harmful noise (HN), based on whether the noise can help reduce the complexity of the task. Extensive experiments of CNNs and ViTs have shown performance improvements by proactively injecting positive noise, where we achieve an unprecedented top 1 accuracy over 95 % on ImageNet. Both theoretical analysis and empirical evidence have confirmed that the presence of positive noise, can benefit the learning process, while the traditionally perceived harmful noise indeed impairs deep learning models. The different roles of noise offer new explanations for deep models on specific tasks and provide a new paradigm for improving model performance. Moreover, it reminds us that we can influence the performance of learning systems via information entropy change.
An Empirical Study of Attention Networks for Semantic Segmentation

Semantic segmentation is a vital problem in computer vision. Recently, a common solution to semantic segmentation is the end-to-end convolution neural network, which is much more accurate than traditional methods.Recently, the decoders based on attention achieve state-of-the-art (SOTA) performance on various datasets. But these networks always are compared with the mIoU of previous SOTA networks to prove their superiority and ignore their characteristics without considering the computation complexity and precision in various categories, which is essential for engineering applications. Besides, the methods to analyze the FLOPs and memory are not consistent between different networks, which makes the comparison hard to be utilized. What's more, various methods utilize attention in semantic segmentation, but the conclusion of these methods is lacking. This paper first conducts experiments to analyze their computation complexity and compare their performance. Then it summarizes suitable scenes for these networks and concludes key points that should be concerned when constructing an attention network. Last it points out some future directions of the attention network.