Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-predation does not fully explain grouping in the Chinese water deer (Hydropotes Inermis)

The grouping behavior of the Chinese water deer (Hydropotes inermis) was studied at Poyang Lake National Nature Reserve, Jiangxi, China. I made three predictions to test the hypothesis that grouping is an anti predatory strategy: (1) group size is negatively related to vegetation cover, (2) animals in groups have more time to feed than solitary animals, and (3) animals spend less time in vigilance when they are in groups than when they are alone. Results showed that group size was largest in the mating season and smallest in the parturition season. Deer tended to form larger groups when they were in short grass areas than in tall grass areas. Hence, the first prediction is supported. However, solitary deer and deer in groups did not differ in time allocated to feeding or vigilance. Therefore, predictions 2 and 3 are not supported. Time budget analysis revealed that females with young allocated less time to feeding, but more time to walking during the non mating season. During the mating season, females grouped with males spent less time feeding than solitary females, and solitary males spent more time looking for females than did males grouped with females during the mating season. In light of the complications found in the grouping behavior of the water deer, the anti predatory hypothesis seems to be insufficient as the only reason for the deer to group. Other socio ecological factors are also important in determining the grouping behavior of this species.
Fuzzy Ensembles of Reinforcement Learning Policies for Robotic Systems with Varied Parameters

Reinforcement Learning (RL) is an emerging approach to control many dynamical systems for which classical control approaches are not applicable or insufficient. However, the resultant policies may not generalize to variations in the parameters that the system may exhibit. This paper presents a powerful yet simple algorithm in which collaboration is facilitated between RL agents that are trained independently to perform the same task but with different system parameters. The independency among agents allows the exploitation of multi-core processing to perform parallel training. Two examples are provided to demonstrate the effectiveness of the proposed technique. The main demonstration is performed on a quadrotor with slung load tracking problem in a real-time experimental setup. It is shown that integrating the developed algorithm outperforms individual policies by reducing the RMSE tracking error. The robustness of the ensemble is also verified against wind disturbance.
Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Learning feature interaction is the critical backbone to building recommender systems. In web-scale applications, learning feature interaction is extremely challenging due to the sparse and large input feature space; meanwhile, manually crafting effective feature interactions is infeasible because of the exponential solution space. We propose to leverage a Transformer-based architecture with attention layers to automatically capture feature interactions. Transformer architectures have witnessed great success in many domains, such as natural language processing and computer vision. However, there has not been much adoption of Transformer architecture for feature interaction modeling in industry. We aim at closing the gap. We identify two key challenges for applying the vanilla Transformer architecture to web-scale recommender systems: (1) Transformer architecture fails to capture the heterogeneous feature interactions in the self-attention layer; (2) The serving latency of Transformer architecture might be too high to be deployed in web-scale recommender systems. We first propose a heterogeneous self-attention layer, which is a simple yet effective modification to the self-attention layer in Transformer, to take into account the heterogeneity of feature interactions. We then introduce \textsc{Hiformer} (\textbf{H}eterogeneous \textbf{I}nteraction Trans\textbf{former}) to further improve the model expressiveness. With low-rank approximation and model pruning, \hiformer enjoys fast inference for online deployment. Extensive offline experiment results corroborates the effectiveness and efficiency of the \textsc{Hiformer} model. We have successfully deployed the \textsc{Hiformer} model to a real world large scale App ranking model at Google Play, with significant improvement in key engagement metrics (up to +2.66\%).
Optimal Cooperative Multiplayer Learning Bandits with Noisy Rewards and No Communication

We consider a cooperative multiplayer bandit learning problem where the players are only allowed to agree on a strategy beforehand, but cannot communicate during the learning process. In this problem, each player simultaneously selects an action. Based on the actions selected by all players, the team of players receives a reward. The actions of all the players are commonly observed. However, each player receives a noisy version of the reward which cannot be shared with other players. Since players receive potentially different rewards, there is an asymmetry in the information used to select their actions. In this paper, we provide an algorithm based on upper and lower confidence bounds that the players can use to select their optimal actions despite the asymmetry in the reward information. We show that this algorithm can achieve logarithmic $O(\frac{\log T}{\Delta_{\bm{a}}})$ (gap-dependent) regret as well as $O(\sqrt{T\log T})$ (gap-independent) regret. This is asymptotically optimal in $T$. We also show that it performs empirically better than the current state of the art algorithm for this environment.
Relaxation Critical Dynamics with Emergent Symmetry

Different from usual critical point characterized by a single length scale, critical point with emergent symmetry exhibits intriguing critical properties characterized by two relevant length scales, attracting long-term investigations from both theoretical and experimental aspects. A natural question is how the critical dynamics is affected by the presence of two relevant length scales. Here we study the relaxation critical dynamics in the three-dimensional ($3$D) clock model, whose critical point has emergent $U(1)$ symmetry. We find that in contrast to the magnatization $M$, whose relaxation process is described by the usual dynamic exponent $z$ of the $3$D $XY$ universality class, the angular order parameter $\phi_q$ shows a two-stage evolution characterized by different dynamic critical exponents. While in the short-time stage the relaxation dynamics is governed by $z$, in the long-time stage the dynamics is controlled by a new dynamic exponent $z'$. We also show the off-critical-point effects in the critical relaxation. Our results may be experimentally detected in the hexagonal RMnO$_3$ (R$=$rare earth) materials.
On Optimal Control at the Onset of a New Viral Outbreak

An optimal control problem for the early stage of an infectious disease outbreak is considered. At that stage, control is often limited to non-medical interventions like social distancing and other behavioral changes. We show that the running cost of control satisfying mild, problem-specific, conditions generates an optimal control strategy that stays inside its admissible set for the entire duration of the study period $[0 ,T]$. For the optimal control problem, restricted by SIR compartmental model of disease transmission, we prove that the optimal control strategy, $u(t)$, may be growing until some moment $\bar{t} \in [0 ,T)$. However, for any $t \in [\bar{t}, T]$, the function $u(t)$ will decline as $t$ approaches $T$, which may cause the number of newly infected people to increase. So, the window from $0$ to $\bar{t}$ is the time for public health officials to prepare alternative mitigation measures, such as vaccines, testing, antiviral medications, and others. Our theoretical findings are illustrated with numerical examples showing optimal control strategies for various cost functions and weights. Simulation results provide a comprehensive demonstration of the effects of control on the epidemic spread and mitigation expenses, which can serve as invaluable references for public health officials.
Generative Explanations for Graph Neural Network: Methods and Evaluations

Graph Neural Networks (GNNs) achieve state-of-the-art performance in various graph-related tasks. However, the black-box nature often limits their interpretability and trustworthiness. Numerous explainability methods have been proposed to uncover the decision-making logic of GNNs, by generating underlying explanatory substructures. In this paper, we conduct a comprehensive review of the existing explanation methods for GNNs from the perspective of graph generation. Specifically, we propose a unified optimization objective for generative explanation methods, comprising two sub-objectives: Attribution and Information constraints. We further demonstrate their specific manifestations in various generative model architectures and different explanation scenarios. With the unified objective of the explanation problem, we reveal the shared characteristics and distinctions among current methods, laying the foundation for future methodological advancements. Empirical results demonstrate the advantages and limitations of different explainability approaches in terms of explanation performance, efficiency, and generalizability.
Search-Based Fairness Testing: An Overview

Artificial Intelligence (AI) has demonstrated remarkable capabilities in domains such as recruitment, finance, healthcare, and the judiciary. However, biases in AI systems raise ethical and societal concerns, emphasising the need for effective fairness testing methods. This paper reviews current research on fairness testing, particularly its application through search-based testing. Our analysis highlights progress and identifies areas of improvement in addressing AI systems' biases. Future research should focus on leveraging established search-based testing methodologies for fairness testing.
Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification

Knee-Joint Osteoarthritis (KOA) is a prevalent cause of global disability and is inherently complex to diagnose due to its subtle radiographic markers and individualized progression. One promising classification avenue involves applying deep learning methods; however, these techniques demand extensive, diversified datasets, which pose substantial challenges due to medical data collection restrictions. Existing practices typically resort to smaller datasets and transfer learning. However, this approach often inherits unnecessary pre-learned features that can clutter the classifier's vector space, potentially hampering performance. This study proposes a novel paradigm for improving post-training specialized classifiers by introducing adaptive variance thresholding (AVT) followed by Neural Architecture Search (NAS). This approach led to two key outcomes: an increase in the initial accuracy of the pre-trained KOA models and a 60-fold reduction in the NAS input vector space, thus facilitating faster inference speed and a more efficient hyperparameter search. We also applied this approach to an external model trained for KOA classification. Despite its initial performance, the application of our methodology improved its average accuracy, making it one of the top three KOA classification models.
Controlling electron motion with attosecond precision by shaped femtosecond intense laser pulse

We propose the scheme of temporal double-slit interferometer to precisely measure the electric field of shaped intense femtosecond laser pulse directly, and apply it to control the electron tunneling wave packets in attosecond precision. By manipulating the spectra phase of the input femtosecond pulse in frequency domain, one single pulse is split into two sub-pulses whose waveform can be precisely controlled by adjusting the spectra phase. When the shaped pulse interacts with atoms, the two sub-pulses are analogous to the Young's double-slit in time domain. The interference pattern in the photoelectron momentum distribution can be used to precisely retrieve the peak electric field and the time delay between two sub-pulses. Based on the precise characterization of the shaped pulse, we demonstrate that the sub-cycle dynamics of electron can be controlled with attosecond precision. The above scheme is proved to be feasible by both quantum-trajectory Monte Carlo simulations and numerical solutions of three-dimensional time-dependent Schr\"{o}dinger equation.
High-speed coherent photonic random-access memory in long-lasting sound waves

In recent years, remarkable advances in photonic computing have highlighted the need for photonic memory, particularly high-speed and coherent random-access memory. Addressing the ongoing challenge of implementing photonic memories is required to fully harness the potential of photonic computing. A photonic-phononic memory based on stimulated Brillouin scattering is a possible solution as it coherently transfers optical information into sound waves at high-speed access times. Such an optoacoustic memory has shown great potential as it fulfils key requirements for high-performance optical random-access memory due to its coherence, on-chip compatibility, frequency selectivity, and high bandwidth. However, the storage time has so far been limited to a few nanoseconds due to the nanosecond decay of the acoustic wave. In this work, we experimentally enhance the intrinsic storage time of an optoacoustic memory by more than one order of magnitude and coherently retrieve optical information after a storage time of 120 ns. This is achieved by employing the optoacoustic memory in a highly nonlinear fiber at 4.2 K, increasing the intrinsic phonon lifetime by a factor of six. We demonstrate the capability of our scheme by measuring the initial and readout optical data pulse with a direct and double homodyne detection scheme. Finally, we analyze the dynamics of the optoacoustic memory at different cryogenic temperatures in the range of 4.2 K to 20 K and compare the findings to continuous wave measurements. The extended storage time is not only beneficial for photonic computing, but also for Brillouin applications that require long phonon lifetimes, such as optoacoustic filters, true-time delay networks, and synthesizers in microwave photonics.
Sum-max Submodular Bandits

Many online decision-making problems correspond to maximizing a sequence of submodular functions. In this work, we introduce sum-max functions, a subclass of monotone submodular functions capturing several interesting problems, including best-of-$K$-bandits, combinatorial bandits, and the bandit versions on facility location, $M$-medians, and hitting sets. We show that all functions in this class satisfy a key property that we call pseudo-concavity. This allows us to prove $\big(1 - \frac{1}{e}\big)$-regret bounds for bandit feedback in the nonstochastic setting of the order of $\sqrt{MKT}$ (ignoring log factors), where $T$ is the time horizon and $M$ is a cardinality constraint. This bound, attained by a simple and efficient algorithm, significantly improves on the $\widetilde{O}\big(T^{2/3}\big)$ regret bound for online monotone submodular maximization with bandit feedback.
MPGemmFI: A Fault Injection Technique for Mixed Precision GEMM in ML Applications

Emerging deep learning workloads urgently need fast general matrix multiplication (GEMM). To meet such demand, one of the critical features of machine-learning-specific accelerators such as NVIDIA Tensor Cores, AMD Matrix Cores, and Google TPUs is the support of mixed-precision enabled GEMM. For DNN models, lower-precision FP data formats and computation offer acceptable correctness but significant performance, area, and memory footprint improvement. While promising, the mixed-precision computation on error resilience remains unexplored. To this end, we develop a fault injection framework that systematically injects fault into the mixed-precision computation results. We investigate how the faults affect the accuracy of machine learning applications. Based on the error resilience characteristics, we offer lightweight error detection and correction solutions that significantly improve the overall model accuracy if the models experience hardware faults. The solutions can be efficiently integrated into the accelerator's pipelines.
List-$k$-Coloring $H$-free graphs for all $k>4$

Given an integer $k>4$ and a graph $H$, we prove that, assuming P$\neq$NP, the List-$k$-Coloring Problem restricted to $H$-free graphs can be solved in polynomial time if and only if either every component of $H$ is a path on at most three vertices, or removing the isolated vertices of $H$ leaves an induced subgraph of the five-vertex path. In fact, the"if"implication holds for all $k\geq 1$.
Singularities of the hyperbolic elastic flow: Convergence, quantization and blow-ups

This paper delves into the intricate study of the elastic flow of closed curves and of open curves with clamped boundary conditions. While prior research has made strides in establishing global existence and convergence toward critical points for initial data with energy levels below a certain threshold, this study pioneers an investigation into the singular behavior exhibited by this flow. Specifically, we present a novel and comprehensive convergence theorem applicable to arbitrary initial data, coupled with a quantification of potential singularities based on the energy content of the initial configuration: Each singularity carries a cost of at least 8 in terms of the elastic energy. Moreover, we can prove that blow-ups of these singularities are asymptotically geodesic elastica. Of particular significance is our contribution in the understanding of the singular limit of the elastic flow of $\lambda$-figure-eights, an intriguing configuration that previously already served in showing sharpness of the energy threshold 16 for the smooth convergence of the elastic flow of closed curves.
Optimal simulation-based Bayesian decisions

We present a framework for the efficient computation of optimal Bayesian decisions under intractable likelihoods, by learning a surrogate model for the expected utility (or its distribution) as a function of the action and data spaces. We leverage recent advances in simulation-based inference and Bayesian optimization to develop active learning schemes to choose where in parameter and action spaces to simulate. This allows us to learn the optimal action in as few simulations as possible. The resulting framework is extremely simulation efficient, typically requiring fewer model calls than the associated posterior inference task alone, and a factor of $100-1000$ more efficient than Monte-Carlo based methods. Our framework opens up new capabilities for performing Bayesian decision making, particularly in the previously challenging regime where likelihoods are intractable, and simulations expensive.
PodReels: Human-AI Co-Creation of Video Podcast Teasers

Video podcast teasers are short videos that can be shared on social media platforms to capture interest in the full episodes of a video podcast. These teasers enable long-form podcasters to reach new audiences and gain new followers. However, creating a compelling teaser from an hour-long episode is challenging. Selecting interesting clips requires significant mental effort; editing the chosen clips into a cohesive, well-produced teaser is time-consuming. To support the creation of video podcast teasers, we first investigate what makes a good teaser. We combine insights from both audience comments and creator interviews to determine a set of essential ingredients. We also identify a common workflow shared by creators during the process. Based on these findings, we introduce a human-AI co-creative tool called PodReels to assist video podcasters in creating teasers. Our user study shows that PodReels significantly reduces creators' mental demand and improves their efficiency in producing video podcast teasers.
The maintenance of complex visual scenes in working memory may require activation of working memory manipulation circuits in the dlPFC.

Past research has shown that the bilateral dorsolateral prefrontal cortices (dlPFC) are implicated in both emotional processing as well as cognitive processing, in addition to working memory Exactly how these disparate processes interact with one another within the dlPFC is less understood. To explore this, researchers designed an experiment that looked at working memory performance during fMRI under both emotional and non-emotional task conditions. Participants were asked to complete three tasks (letters, neutral images, emotional images) of the Sternberg Sorting Task under one of two trial conditions (sort or maintain). Regions of interest consisted of the left and right dlPFC as defined by brain masks based on NeuroSynth. Results showed a significant main effect of the sort condition on reaction speed for all three trial types, as well as a main effect of task type (letters) on accuracy. In addition, a significant interaction was found between trial type (sort) and task type (letters), but not for either of the picture tasks. These results reveal a discrepancy between BOLD signal and behavioral data, with no significant difference in BOLD activity during image trials being displayed, despite longer response times for every condition. While these results show that the dlPFC is clearly implicated in non-emotional cognitive processing, more research is needed to explain the lack of BOLD activation seen here for similar emotionally valanced tasks, possibly indicating involvement of other brain networks.
Poisoning scientific knowledge using large language models

Biomedical knowledge graphs constructed from scientific literature have been widely used to validate biological discoveries and generate new hypotheses. Recently, large language models (LLMs) have demonstrated a strong ability to generate human-like text data. While most of these text data have been useful, LLM might also be used to generate malicious content. Here, we investigate whether it is possible that a malicious actor can use LLM to generate a malicious paper that poisons scientific knowledge graphs and further affects downstream biological applications. As a proof-of-concept, we develop Scorpius, a conditional text generation model that generates a malicious paper abstract conditioned on a promoting drug and a target disease. The goal is to fool the knowledge graph constructed from a mixture of this malicious abstract and millions of real papers so that knowledge graph consumers will misidentify this promoting drug as relevant to the target disease. We evaluated Scorpius on a knowledge graph constructed from 3,818,528 papers and found that Scorpius can increase the relevance of 71.3% drug disease pairs from the top 1000 to the top 10 by only adding one malicious abstract. Moreover, the generation of Scorpius achieves better perplexity than ChatGPT, suggesting that such malicious abstracts cannot be efficiently detected by humans. Collectively, Scorpius demonstrates the possibility of poisoning scientific knowledge graphs and manipulating downstream applications using LLMs, indicating the importance of accountable and trustworthy scientific knowledge discovery in the era of LLM.