Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender and Robots: A Literature Review

Here, I ask what we can learn about how gender affects how people engage with robots. I review 46 empirical studies of social robots, published 2018 or earlier, which report on the gender of their participants or the perceived or intended gender of the robot, or both, and perform some analysis with respect to either participant or robot gender. From these studies, I find that robots are by default perceived as male, that robots absorb human gender stereotypes, and that men tend to engage with robots more than women. I highlight open questions about how such gender effects may be different in younger participants, and whether one should seek to match the gender of the robot to the gender of the participant to ensure positive interaction outcomes. I conclude by suggesting that future research should: include gender diverse participant pools, include non-binary participants, rely on self-identification for discerning gender rather than researcher perception, control for known covariates of gender, test for different study outcomes with respect to gender, and test whether the robot used was perceived as gendered by participants. I include an appendix with a narrative summary of gender-relevant findings from each of the 46 papers to aid in future literature reviews.
A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model

The nervous system, more specifically, the brain, is capable of solving complex problems simply and efficiently, far surpassing modern computers. In this regard, neuromorphic engineering is a research field that focuses on mimicking the basic principles that govern the brain in order to develop systems that achieve such computational capabilities. Within this field, bio-inspired learning and memory systems are still a challenge to be solved, and this is where the hippocampus is involved. It is the region of the brain that acts as a short-term memory, allowing the learning and unstructured and rapid storage of information from all the sensory nuclei of the cerebral cortex and its subsequent recall. In this work, we propose a novel bio-inspired memory model based on the hippocampus with the ability to learn memories, recall them from a cue (a part of the memory associated with the rest of the content) and even forget memories when trying to learn others with the same cue. This model has been implemented on the SpiNNaker hardware platform using Spiking Neural Networks, and a set of experiments and tests were performed to demonstrate its correct and expected operation. The proposed spike-based memory model generates spikes only when it receives an input, being energy efficient, and it needs 7 timesteps for the learning step and 6 timesteps for recalling a previously-stored memory. This work presents the first hardware implementation of a fully functional bio-inspired spike-based hippocampus memory model, paving the road for the development of future more complex neuromorphic systems.
Fast synchronization of inhomogenous random automata

We examine the reset threshold of randomly generated deterministic automata. We present a simple proof that an automaton with a random mapping and two random permutation letters has a reset threshold of $\mathcal{O}\big( \sqrt{n \log^3 n} \big)$ with high probability, assuming only certain partial independence of the letters. Our observation is motivated by Nicaud (2014) providing a near-linear bound in the case of two random mapping letters, among multiple other results. The upper bound for the latter case has been recently improved by the breakthrough work of Chapuy and Perarnau (2023) to $\mathcal{O}(\sqrt{n} \log n)$.
The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenon

The grokking phenomenon as reported by Power et al. ( arXiv:2201.02177 ) refers to a regime where a long period of overfitting is followed by a seemingly sudden transition to perfect generalization. In this paper, we attempt to reveal the underpinnings of Grokking via a series of empirical studies. Specifically, we uncover an optimization anomaly plaguing adaptive optimizers at extremely late stages of training, referred to as the Slingshot Mechanism. A prominent artifact of the Slingshot Mechanism can be measured by the cyclic phase transitions between stable and unstable training regimes, and can be easily monitored by the cyclic behavior of the norm of the last layers weights. We empirically observe that without explicit regularization, Grokking as reported in ( arXiv:2201.02177 ) almost exclusively happens at the onset of Slingshots, and is absent without it. While common and easily reproduced in more general settings, the Slingshot Mechanism does not follow from any known optimization theories that we are aware of, and can be easily overlooked without an in depth examination. Our work points to a surprising and useful inductive bias of adaptive gradient optimizers at late stages of training, calling for a revised theoretical analysis of their origin.
Generate, Evaluate, and Select: A Dialogue System with a Response Evaluator for Diversity-Aware Response Generation

We aim to overcome the lack of diversity in responses of current dialogue systems and to develop a dialogue system that is engaging as a conversational partner. We propose a generator-evaluator model that evaluates multiple responses generated by a response generator and selects the best response by an evaluator. By generating multiple responses, we obtain diverse responses. We conduct human evaluations to compare the output of the proposed system with that of a baseline system. The results of the human evaluations showed that the proposed system’s responses were often judged to be better than the baseline system’s, and indicated the effectiveness of the proposed method.
Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy

Multi-agent deep reinforcement learning has been applied to address a variety of complex problems with either discrete or continuous action spaces and achieved great success. However, most real-world environments cannot be described by only discrete action spaces or only continuous action spaces. And there are few works having ever utilized deep reinforcement learning (drl) to multi-agent problems with hybrid action spaces. Therefore, we propose a novel algorithm: Deep Multi-Agent Hybrid Soft Actor-Critic (MAHSAC) to fill this gap. This algorithm follows the centralized training but decentralized execution (CTDE) paradigm, and extend the Soft Actor-Critic algorithm (SAC) to handle hybrid action space problems in Multi-Agent environments based on maximum entropy. Our experiences are running on an easy multi-agent particle world with a continuous observation and discrete action space, along with some basic simulated physics. The experimental results show that MAHSAC has good performance in training speed, stability, and anti-interference ability. At the same time, it outperforms existing independent deep hybrid learning method in cooperative scenarios and competitive scenarios.
Fisher SAM: Information Geometry and Sharpness Aware Minimisation

Recent sharpness-aware minimisation (SAM) is known to find flat minima which is beneficial for better generalisation with improved robustness. SAM essentially modifies the loss function by reporting the maximum loss value within the small neighborhood around the current iterate. However, it uses the Euclidean ball to define the neighborhood, which can be inaccurate since loss functions for neural networks are typically defined over probability distributions (e.g., class predictive probabilities), rendering the parameter space non Euclidean. In this paper we consider the information geometry of the model parameter space when defining the neighborhood, namely replacing SAM's Euclidean balls with ellipsoids induced by the Fisher information. Our approach, dubbed Fisher SAM, defines more accurate neighborhood structures that conform to the intrinsic metric of the underlying statistical manifold. For instance, SAM may probe the worst-case loss value at either a too nearby or inappropriately distant point due to the ignorance of the parameter space geometry, which is avoided by our Fisher SAM. Another recent Adaptive SAM approach stretches/shrinks the Euclidean ball in accordance with the scale of the parameter magnitudes. This might be dangerous, potentially destroying the neighborhood structure. We demonstrate improved performance of the proposed Fisher SAM on several benchmark datasets/tasks.
Deep Leakage from Model in Federated Learning

Distributed machine learning has been widely used in recent years to tackle the large and complex dataset problem. Therewith, the security of distributed learning has also drawn increasing attentions from both academia and industry. In this context, federated learning (FL) was developed as a"secure"distributed learning by maintaining private training data locally and only public model gradients are communicated between. However, to date, a variety of gradient leakage attacks have been proposed for this procedure and prove that it is insecure. For instance, a common drawback of these attacks is shared: they require too much auxiliary information such as model weights, optimizers, and some hyperparameters (e.g., learning rate), which are difficult to obtain in real situations. Moreover, many existing algorithms avoid transmitting model gradients in FL and turn to sending model weights, such as FedAvg, but few people consider its security breach. In this paper, we present two novel frameworks to demonstrate that transmitting model weights is also likely to leak private local data of clients, i.e., (DLM and DLM+), under the FL scenario. In addition, a number of experiments are performed to illustrate the effect and generality of our attack frameworks. At the end of this paper, we also introduce two defenses to the proposed attacks and evaluate their protection effects. Comprehensively, the proposed attack and defense schemes can be applied to the general distributed learning scenario as well, just with some appropriate customization.
A Programmatic Definition of Visualization Tasks, Insights, and Objectives

— Researchers have developed several theoretical models for identifying and categorizing data analysis tasks for visualization systems. However, these models focus primarily on abstraction or generalizing speciﬁc tasks into higher-level concepts, resulting in broad guidelines that are not always straightforward to implement within visualization systems. Few models ﬂow in the opposite direction to enable instantiation or a precise approach to applying high-level task concepts to speciﬁc analysis scenarios or user interaction logs. This paper presents a synthesis of existing task theory into a new instantiation-focused model and Pyxis, a speciﬁcation language for applying this model to existing evaluation methods. Speciﬁcally, Pyxis enables researchers to dissect theoretical and study-driven analysis sessions to identify instances of tasks that users have performed. Further, it formalizes the relationship between tasks, insights, and objectives implied in prior work. We present three use cases that apply Pyxis to a wide range of analysis scenarios from the literature to demonstrate its utility. Finally, we discuss the model’s implications and opportunities for future work.
Functional limit theorems for P\'olya urns with growing initial compositions

In this paper, we prove functional limit theorems for P\'olya urn processes whose number of draws and initial number of balls tend to infinity together. This is motivated by recent work of Borovkov [5], where they prove a functional limit theorem for this model when the urn has identity replacement rule. We generalize this result to arbitrary balanced replacement rules (the total number of balls added to the urn is deterministic). Three asymptotic regimes are possible depending on how one lets the number of initial balls scale with the number of draws of the urn. In each regime, we show a first order deterministic limit and Gaussian second order fluctuations, where the behaviour of these limit processes depend on the regime, the initial composition of the urn, and the urns replacement rule. To prove our main results, we embed the process in continuous-time and use martingale theory. Although these methods are classical since the works of Athreya&Karlin [1] and Janson [11], our setting with initial growing composition necessitates many new ideas. The main difference in proving limiting results for the continuous time embedding in our setting is that, when the initial composition is large compared to the number of draws, the branching process does not have time to reach equilibrium. Because of this, translating the results back to discrete-time is also much harder than in Janson [11]. Interestingly, our continuous-time results hold under weaker assumptions on the replacement structure than classical results for multi-type branching processes; in particular, we do not need any"irreducibility"assumption.
Impact of dynamics, entanglement, and Markovian noise on the fidelity of few-qubit digital quantum simulation

For quantum computations without error correction, the dynamics of a simulation can strongly influence the overall fidelity decay rate as well as the relative impact of different noise processes on the fidelity. Theoretical models of Markovian noise that include incoherent Lindblad noise, gate-based errors, and stochastic Hamiltonian noise qualitatively agree that greater diffusion and entanglement throughout Hilbert space typically increase the fidelity decay rate. Simulations of the gate-efficient quantum sawtooth map support these predictions, and experiments performed on three qubits on the IBM-Q quantum hardware platform at fixed gate count qualitatively confirm the predictions. A pure depolarizing noise model, often used within randomized benchmarking (RB) theory, cannot explain the observed effect, but gate-based Lindblad models can provide an explanation. They can also estimate the effective Lindblad coherence times during gates, and find a consistent $2\text{-}3\times$ shorter effective $T_2$ dephasing time than reported for idle qubits. Additionally, the observed error per CNOT gate exceeds IBM-Q's reported value from RB by $3.0\times$ during localized dynamics and $4.5\times$ during diffusive dynamics. This demonstrates the magnitude by which RB understates error in a complex quantum simulation due to dynamics and crosstalk.
REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction

Relation extraction is an important but challenging task that aims to extract all hidden relational facts from the text. With the development of deep language models, relation extraction methods have achieved good performance on various benchmarks. However, we observe two shortcomings of previous methods: first, there is no unified framework that works well under various relation extraction settings; second, effectively utilizing external knowledge as background information is absent. In this work, we propose a knowledge-enhanced generative model to mitigate these two issues. Our generative model is a unified framework to sequentially generate relational triplets under various relation extraction settings and explicitly utilizes relevant knowledge from Knowledge Graph (KG) to resolve ambiguities. Our model achieves superior performance on multiple benchmarks and settings, including WebNLG, NYT10, and TACRED.
Interactively Learning Preference Constraints in Linear Bandits

We study sequential decision-making with known rewards and unknown constraints, motivated by situations where the constraints represent expensive-to-evaluate human preferences, such as safe and comfortable driving behavior. We formalize the challenge of interactively learning about these constraints as a novel linear bandit problem which we call constrained linear best-arm identification. To solve this problem, we propose the Adaptive Constraint Learning (ACOL) algorithm. We provide an instance-dependent lower bound for constrained linear best-arm identification and show that ACOL's sample complexity matches the lower bound in the worst-case. In the average case, ACOL's sample complexity bound is still significantly tighter than bounds of simpler approaches. In synthetic experiments, ACOL performs on par with an oracle solution and outperforms a range of baselines. As an application, we consider learning constraints to represent human preferences in a driving simulation. ACOL is significantly more sample efficient than alternatives for this application. Further, we find that learning preferences as constraints is more robust to changes in the driving scenario than encoding the preferences directly in the reward function.
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators

In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at the edge leveraging non-MAC (free of floating-point Multiply-ACcumulate operations) deep neural nets (DNN) combined with hyperdimensional (HD) computing accelerators. We describe the algorithm, trained quantized model generation, and simulated performance of a feature extractor free of multiply-accumulates feeding a hyperdimensional logic-based classifier. Then we show how performance increases with the number of hyperdimensions. We describe the realized low-SWaP FPGA hardware and embedded software system compared to traditional DNNs and detail the implemented hardware accelerators. We discuss the measured system latency and power, noise robustness due to use of learnable quantization and HD computing, actual versus simulated system performance for a video activity classification task and demonstration of reconfiguration on this same dataset. We show that reconfigurability in the field is achieved by retraining only the feed-forward HD classifier without gradient descent backpropagation (gradient-free), using few-shot learning of new classes at the edge.Initial work performed used LRCN DNN and is currently extended to use Two-stream DNN with improved performance.
Prediction of quaternary hydrides based on densest ternary sphere packings

We exhaustively search quaternary metal hydrides based on the (13-2-1) and (13-3-1) structures that are the two of the putative densest ternary sphere packings in cubic systems [R. Koshoji et al. , Phys. Rev. E 104 , 024101 (2021)]. The 73304 candidate hydrides are generated by substituting the small spheres with hydrogen atoms, the medium, large, and fourth spheres with metallic atoms. Especially, the substitution of the small spheres with hydrogen atoms gives the unconventional hydrogen sublattices. We screen unstable hydrides in the candidates through geometrical optimizations, constant pressure molecular dynamics simulations, and phonon calculations under hydrostatic pressure of 10 GPa, and identify 23 hydrides with static and dynamic stability, including H 12 ScY 2 La and H 12 TiNi 3 Ba. We expect that the 23 candidates of hydrides screened by the exhaustive search for 73304 hydrides provide a guideline to narrow down the search space for trials in the experimental synthesis of quaternary metal hydrides.
Consistent Estimation of Multiple Breakpoints in Dependence Measures*

This paper proposes different methods to consistently detect multiple breaks in copula-based dependence measures, mainly focusing on Spearman's $\rho$. The leading model is a factor copula model due to its usefulness for analyzing data in high dimensions. Starting with the classical binary segmentation, also the more recent wild binary segmentation (WBS) and a procedure based on an information criterion are considered. For all procedures, consistency of the estimators for the location of the breakpoints as well as the number of breaks is proved. Monte Carlo simulations indicate that WBS performs best in many, but not in all, situations. A real data application on recent Euro Stoxx 50 data reveals the usefulness of the procedures.
Quantum heat engine based on a spin-orbit- and Zeeman-coupled Bose-Einstein condensate

We explore the potential of a spin-orbit coupled Bose-Einstein condensate for thermodynamic cycles. For this purpose we propose a quantum heat engine based on a condensate with spin-orbit and Zeeman coupling as a working medium. The cooling and heating are simulated by contacts of the condensate with an external magnetized media and demagnetized media. We examine the condensate ground state energy and its dependence on the strength of the synthetic spin-orbit and Zeeman couplings and interatomic interaction. Then we study the e ﬃ ciency of the proposed engine. The cycle has a critical value of spin-orbit coupling related to the engine maximum e ﬃ ciency.
Ask to Know More: Generating Counterfactual Explanations for Fake Claims

Automated fact-checking systems have been proposed that quickly provide veracity prediction at scale to mitigate the negative influence of fake news on people and on public opinion. However, most studies focus on veracity classifiers of those systems, which merely predict the truthfulness of news articles. We posit that effective fact checking also relies on people's understanding of the predictions. In this paper, we propose elucidating fact-checking predictions using counterfactual explanations to help people understand why a specific piece of news was identified as fake. In this work, generating counterfactual explanations for fake news involves three steps: asking good questions, finding contradictions, and reasoning appropriately. We frame this research question as contradicted entailment reasoning through question answering (QA). We first ask questions towards the false claim and retrieve potential answers from the relevant evidence documents. Then, we identify the most contradictory answer to the false claim by use of an entailment classifier. Finally, a counterfactual explanation is created using a matched QA pair with three different counterfactual explanation forms. Experiments are conducted on the FEVER dataset for both system and human evaluations. Results suggest that the proposed approach generates the most helpful explanations compared to state-of-the-art methods. Our code and data is publicly available https://github.com/yilihsu/AsktoKnowMore.
The First Direct Measurement of Gravitational Potential Decay Rate at Cosmological Scales and Improved Dark Energy Constraint

The integrated Sachs–Wolfe (ISW) effect probes the decay rate (DR) of large-scale gravitational potential and therefore provides a unique constraint on dark energy (DE). However, its constraining power is degraded by the ISW measurement, which relies on cross-correlating with the large-scale structure (LSS) and suffers from uncertainties in galaxy bias and matter clustering. In combination with lensing-LSS cross correlation, DR can be isolated in a way free of uncertainties in galaxy bias and matter clustering. We applied this proposal to the combination of the Data Release 8 galaxy catalog of DESI imaging surveys and Planck cosmic microwave background maps. We achieved the first DR measurement, with a total significance of 3. 2σ. We verified the measurements at three redshift bins ([0.2, 0.4), [0.4, 0.6), [0.6, 0.8]), with two LSS tracers (the “low-density points” and the conventional galaxy positions). Despite its relatively low signal-to-noise ratio, the addition of DR significantly improves dark energy constraints, over Sloan Digital Sky Survey baryon acoustic oscillation (BAO) data alone or Pantheon supernovae (SNe) compilation alone. For flat wCDM cosmology, the improvement in the precision of Ω m is a factor of 1.8 over BAO and 1.5 over SNe. For the DE equation of state w, the improvement factor is 1.3 over BAO and 1.4 over SNe. These improvements demonstrate DR as a useful cosmological probe, and therefore we advocate its usage in future cosmological analysis.