Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-rank quaternion tensor completion for color video inpainting via a novel factorization strategy

Recently, a quaternion tensor product named Qt-product was proposed, and then the singular value decomposition and the rank of a third-order quaternion tensor were given. From a more applicable perspective, we extend the Qt-product and propose a novel multiplication principle for third-order quaternion tensor named gQt-product. With the gQt-product, we introduce a brand-new singular value decomposition for third-order quaternion tensors named gQt-SVD and then define gQt-rank and multi-gQt-rank. We prove that the optimal low-rank approximation of a third-order quaternion tensor exists and some numerical experiments demonstrate the low-rankness of color videos. So, we apply the low-rank quaternion tensor completion to color video inpainting problems and present alternating least-square algorithms to solve the proposed low gQt-rank and multi-gQt-rank quaternion tensor completion models. The convergence analyses of the proposed algorithms are established and some numerical experiments on various color video datasets show the high recovery accuracy and computational efficiency of our methods.
Modeling the Performance of Heterogeneous IaaS Cloud Centers

Performance modeling of cloud computing centers is a challenging task due to complexity, shared resources and large scale of such systems. Introducing heterogeneous resources and workloads make the analysis even more complicated. In this paper, we propose a layered stochastic performance model applicable to cloud centers with wide range of heterogeneous physical and virtual resources as well as task characteristics without sacrificing the scalability, granularity and simplicity. Under various parameters and configurations, key performance metrics such as task blocking probability and total delay incurred on user tasks are obtained. The results also reveal practical insights into capacity planning for cloud computing centers.
Spatio-Temporal Prediction Modulates the Perception of Self-Produced Stimuli

We investigated why self-produced tactile stimulation is perceived as less intense than the same stimulus produced externally. A tactile stimulus on the palm of the right hand was either externally produced, by a robot or self-produced by the subject. In the conditions in which the tactile stimulus was self-produced, subjects moved the arm of a robot with their left hand to produce the tactile stimulus on their right hand via a second robot. Subjects were asked to rate intensity of the tactile sensation and consistently rated self-produced tactile stimuli as less tickly, intense, and pleasant than externally produced tactile stimuli. Using this robotic setup we were able to manipulate the correspondence between the action of the subjects' left hand and the tactile stimulus on their right hand. First, we parametrically varied the delay between the movement of the left hand and the resultant movement of the tactile stimulus on the right hand. Second, we implemented varying degrees of trajectory perturbation and varied the direction of the tactile stimulus movement as a function of the direction of left-hand movement. The tickliness rating increased significantly with increasing delay and trajectory perturbation. This suggests that self-produced movements attenuate the resultant tactile sensation and that a necessary requirement of this attenuation is that the tactile stimulus and its causal motor command correspond in time and space. We propose that the extent to which self-produced tactile sensation is attenuated (i.e., its tickliness) is proportional to the error between the sensory feedback predicted by an internal forward model of the motor system and the actual sensory feedback produced by the movement.
BatDeck: Advancing Nano-drone Navigation with Low-power Ultrasound-based Obstacle Avoidance

Nano-drones, distinguished by their agility, minimal weight, and cost-effectiveness, are particularly well-suited for exploration in confined, cluttered and narrow spaces. Recognizing transparent, highly reflective or absorbing materials, such as glass and metallic surfaces is challenging, as classical sensors, such as cameras or laser rangers, often do not detect them. Inspired by bats, which can fly at high speeds in complete darkness with the help of ultrasound, this paper introduces \textit{BatDeck}, a pioneering sensor-deck employing a lightweight and low-power ultrasonic sensor for nano-drone autonomous navigation. This paper first provides insights about sensor characteristics, highlighting the influence of motor noise on the ultrasound readings, then it introduces the results of extensive experimental tests for obstacle avoidance (OA) in a diverse environment. Results show that \textit{BatDeck} allows exploration for a flight time of 8 minutes while covering 136m on average before crash in a challenging environment with transparent and reflective obstacles, proving the effectiveness of ultrasonic sensors for OA on nano-drones.
Electron Thermometry

The performance and accuracy of quantum electronics is substantially degraded when the temperature of the electrons in the devices is too high. The electron temperature can be reduced with appropriate thermal anchoring and by filtering both the low frequency and radio frequency noise. Ultimately, for high performance filters the electron temperature can approach the phonon temperature (as measured by resistive thermometers) in a dilution refrigerator. In this application note, the method for measuring the electron temperature in a typical quantum electronics device using Coulomb blockade thermometry is described. This technique is applied to find the readily achievable electron temperature in the device when using the QFilter provided by QDevil. With our thermometry measurements, using a single GaAs/AlGaAs quantum dot in an optimized experimental setup, we determined an electron temperature of 28 $\pm$ 2 mK for a dilution refrigerator base temperature of 18 mK.
Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making

This paper introduces a novel multi-armed bandits framework, termed Contextual Restless Bandits (CRB), for complex online decision-making. This CRB framework incorporates the core features of contextual bandits and restless bandits, so that it can model both the internal state transitions of each arm and the influence of external global environmental contexts. Using the dual decomposition method, we develop a scalable index policy algorithm for solving the CRB problem, and theoretically analyze the asymptotical optimality of this algorithm. In the case when the arm models are unknown, we further propose a model-based online learning algorithm based on the index policy to learn the arm models and make decisions simultaneously. Furthermore, we apply the proposed CRB framework and the index policy algorithm specifically to the demand response decision-making problem in smart grids. The numerical simulations demonstrate the performance and efficiency of our proposed CRB approaches.
Design and Performance of Resonant Beam Communications -- Part II: Mobile Scenario

This two-part paper focuses on the system design and performance analysis for a point-to-point resonant beam communication (RBCom) system under both the quasi-static and mobile scenarios. Part I of this paper proposes a synchronization-based information transmission scheme and derives the capacity upper and lower bounds for the quasi-static channel case. In Part II, we address the mobile scenario, where the receiver is in relative motion to the transmitter, and derive a mobile RBCom channel model that jointly considers the Doppler effect, channel variation, and echo interference. With the obtained channel model, we prove that the channel gain of the mobile RBCom decreases as the number of transmitted frames increases, and thus show that the considered mobile RBCom terminates after the transmitter sends a certain number of frames without frequency compensation. By deriving an upper bound on the number of successfully transmitted frames, we formulate the throughput maximization problem for the considered mobile RBCom system, and solve it via a sequential parametric convex approximation (SPCA) method. Finally, simulation results validate the analysis of our proposed method in some typical scenarios.
A Survey on Consumer IoT Traffic: Security and Privacy

For the past few years, the Consumer Internet of Things (CIoT) has entered public lives. While CIoT has improved the convenience of people's daily lives, it has also brought new security and privacy concerns. In this survey, we try to figure out what researchers can learn about the security and privacy of CIoT by traffic analysis, a popular method in the security community. From the security and privacy perspective, this survey seeks out the new characteristics in CIoT traffic analysis, the state-of-the-art progress in CIoT traffic analysis, and the challenges yet to be solved. We collected 310 papers from January 2018 to December 2023 related to CIoT traffic analysis from the security and privacy perspective and summarized the process of CIoT traffic analysis in which the new characteristics of CIoT are identified. Then, we detail existing works based on five application goals: device fingerprinting, user activity inference, malicious traffic analysis, security analysis, and measurement. At last, we discuss the new challenges and future research directions.
Role of Locality and Weight Sharing in Image-Based Tasks: A Sample Complexity Separation between CNNs, LCNs, and FCNs

Vision tasks are characterized by the properties of locality and translation invariance. The superior performance of convolutional neural networks (CNNs) on these tasks is widely attributed to the inductive bias of locality and weight sharing baked into their architecture. Existing attempts to quantify the statistical benefits of these biases in CNNs over locally connected convolutional neural networks (LCNs) and fully connected neural networks (FCNs) fall into one of the following categories: either they disregard the optimizer and only provide uniform convergence upper bounds with no separating lower bounds, or they consider simplistic tasks that do not truly mirror the locality and translation invariance as found in real-world vision tasks. To address these deficiencies, we introduce the Dynamic Signal Distribution (DSD) classification task that models an image as consisting of $k$ patches, each of dimension $d$, and the label is determined by a $d$-sparse signal vector that can freely appear in any one of the $k$ patches. On this task, for any orthogonally equivariant algorithm like gradient descent, we prove that CNNs require $\tilde{O}(k+d)$ samples, whereas LCNs require $\Omega(kd)$ samples, establishing the statistical advantages of weight sharing in translation invariant tasks. Furthermore, LCNs need $\tilde{O}(k(k+d))$ samples, compared to $\Omega(k^2d)$ samples for FCNs, showcasing the benefits of locality in local tasks. Additionally, we develop information theoretic tools for analyzing randomized algorithms, which may be of interest for statistical research.
IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History

Traditional imitation learning focuses on modeling the behavioral mechanisms of experts, which requires a large amount of interaction history generated by some fixed expert. However, in many streaming applications, such as streaming recommender systems, online decision-makers typically engage in online learning during the decision-making process, meaning that the interaction history generated by online decision-makers includes their behavioral evolution from novice expert to experienced expert. This poses a new challenge for existing imitation learning approaches that can only utilize data from experienced experts. To address this issue, this paper proposes an inverse batched contextual bandit (IBCB) framework that can efficiently perform estimations of environment reward parameters and learned policy based on the expert's behavioral evolution history. Specifically, IBCB formulates the inverse problem into a simple quadratic programming problem by utilizing the behavioral evolution history of the batched contextual bandit with inaccessible rewards. We demonstrate that IBCB is a unified framework for both deterministic and randomized bandit policies. The experimental results indicate that IBCB outperforms several existing imitation learning algorithms on synthetic and real-world data and significantly reduces running time. Additionally, empirical analyses reveal that IBCB exhibits better out-of-distribution generalization and is highly effective in learning the bandit policy from the interaction history of novice experts.
Behavioral Self-Control in Treating Procrastination of Studying

56 college students who procrastinated in studying were given either behavioral self-control training or a nondirective, attention-placebo control procedure. Experimental clients were exposed to training in either stimulus control, self-reinforcement, or a combination of the two. Half of these clients used self-punishment; half did not. Half were trained individually and half in groups of four. In terms of reported amount of study time and attitudes toward studying, experimental clients improved, whereas control clients did not. However, there were no significant differences among experimental subconditions. Neither control nor experimental conditions resulted in any significant change in over-all grade point average.
A numerical study of gravity-driven instability in strongly coupled dusty plasma. Part 3: Homo-interaction between a pair of rising/falling bubbles/droplets

A numerical study of the homo-interactions between two falling droplets and between two rising bubbles in a strongly coupled dusty plasma medium is presented in this article. This strongly coupled dusty plasma is considered as a viscoelastic fluid using the generalized hydrodynamic fluid model formalism. Two factors that affect homo-interactions are taken into account: the initial spacing and the coupling strength of the medium. Three different spacings between two droplets are simulated: widely, medium, and closely. In each case, the coupling strength has been given as mild-strong and strong. It is shown that the overall dynamic is governed by the competition between the acceleration of two droplets/bubbles due to gravity and the interaction due to the closeness of the droplets/bubbles. Especially in viscoelastic fluids, the closeness between two droplets/bubbles, aside from their initial separation, at a later time may result from shear waves that emerge from rotating vorticies. For widely-spaced, unlike classical hydrodynamic fluids, we find that shear waves in viscoelastic fluids facilitate the pairing between two bubbles/droplets. In the case of medium-spaced, the two new dipoles of unequal strength blobs exhibit a circular motion and exchange their partners. For closely-spaced, the droplet/bubble fall/rise is suppressed as the coupling strength of the medium increases. Numerous two-dimensional simulations have been carried out. This work is a continuation of the work done in parts I (V. S. Dharodi and A. Das, J. Plasma Phys. 87 (02), 905870216 (2021)) and II (V. S. Dharodi, J. Plasma Phys. 87 (04), 905870402 (2021)).
Segment Anything Model for Road Network Graph Extraction

We propose SAM-Road, an adaptation of the Segment Anything Model (SAM) for extracting large-scale, vectorized road network graphs from satellite imagery. To predict graph geometry, we formulate it as a dense semantic segmentation task, leveraging the inherent strengths of SAM. The image encoder of SAM is fine-tuned to produce probability masks for roads and intersections, from which the graph vertices are extracted via simple non-maximum suppression. To predict graph topology, we designed a lightweight transformer-based graph neural network, which leverages the SAM image embeddings to estimate the edge existence probabilities between vertices. Our approach directly predicts the graph vertices and edges for large regions without expensive and complex post-processing heuristics, and is capable of building complete road network graphs spanning multiple square kilometers in a matter of seconds. With its simple, straightforward, and minimalist design, SAM-Road achieves comparable accuracy with the state-of-the-art method RNGDet++, while being 40 times faster on the City-scale dataset. We thus demonstrate the power of a foundational vision model when applied to a graph learning task. The code is available at https://github.com/htcr/sam_road.
Chromosome motion during attachment to the vertebrate spindle: initial saltatory-like behavior of chromosomes and quantitative analysis of force production by nascent kinetochore fibers

Before forming a monopolar attachment to the closest spindle pole, chromosomes attaching in newt (Taricha granulosa) pneumocytes generally reside in an optically clear region of cytoplasm that is largely devoid of cytoskeletal components, organelles, and other chromosomes. We have previously demonstrated that chromosome attachment in these cells occurs when an astral microtubule contacts one of the kinetochores (Hayden, J., S. S. Bowser, and C. L. Rieder. 1990. J. Cell Biol. 111:1039-1045), and that once this association is established the chromosome can be transported poleward along the surface of the microtubule (Rieder, C. L., and S. P. Alexander. 1990. J. Cell Biol. 110:81-95). In the study reported here we used video enhanced differential interference contrast light microscopy and digital image processing to compare, at high spatial and temporal resolution (0.1 microns and 0.93 s, respectively), the microtubule-mediated poleward movement of attaching chromosomes and poleward moving particles on the spindle. The results of this analysis demonstrate obvious similarities between minus end-directed particle motion on the newt pneumocyte spindle and the motion of attaching chromosomes. This is consistent with the hypothesis that both are driven by a similar force-generating mechanism. We then used the Brownian displacements of particles in the vicinity of attaching chromosomes to calculate the apparent viscosity of cytoplasm through which the chromosomes were moving. From these data, and that from our kinetic analyses and previous work, we calculate the force-producing potential of nascent kinetochore fibers in newt pneumocytes to be approximately 0.1-7.4 x 10(-6) dyn/microtubule) This is essentially equivalent to that calculated by Nicklas (Nicklas, R.B. 1988. Annu. Rev. Biophys. Biophys. Chem. 17:431- 449) for prometaphase (4 x 10(-6) dyn/microtubule) and anaphase (5 x 10(-6) dyn/microtubule) chromosomes in Melanoplus. Thus, within the limits of experimental error, there appears to be a remarkable consistency in force production per microtubule throughout the various stages of mitosis and between groups of diverse taxonomic affinities.
Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?

Multilingual pretraining and fine-tuning have remarkably succeeded in various natural language processing tasks. Transferring representations from one language to another is especially crucial for cross-lingual learning. One can expect machine translation objectives to be well suited to fostering such capabilities, as they involve the explicit alignment of semantically equivalent sentences from different languages. This paper investigates the potential benefits of employing machine translation as a continued training objective to enhance language representation learning, bridging multilingual pretraining and cross-lingual applications. We study this question through two lenses: a quantitative evaluation of the performance of existing models and an analysis of their latent representations. Our results show that, contrary to expectations, machine translation as the continued training fails to enhance cross-lingual representation learning in multiple cross-lingual natural language understanding tasks. We conclude that explicit sentence-level alignment in the cross-lingual scenario is detrimental to cross-lingual transfer pretraining, which has important implications for future cross-lingual transfer studies. We furthermore provide evidence through similarity measures and investigation of parameters that this lack of positive influence is due to output separability -- which we argue is of use for machine translation but detrimental elsewhere.
PathoTune: Adapting Visual Foundation Model to Pathological Specialists

As natural image understanding moves towards the pretrain-finetune era, research in pathology imaging is concurrently evolving. Despite the predominant focus on pretraining pathological foundation models, how to adapt foundation models to downstream tasks is little explored. For downstream adaptation, we propose the existence of two domain gaps, i.e., the Foundation-Task Gap and the Task-Instance Gap. To mitigate these gaps, we introduce PathoTune, a framework designed to efficiently adapt pathological or even visual foundation models to pathology-specific tasks via multi-modal prompt tuning. The proposed framework leverages Task-specific Visual Prompts and Task-specific Textual Prompts to identify task-relevant features, along with Instance-specific Visual Prompts for encoding single pathological image features. Results across multiple datasets at both patch-level and WSI-level demonstrate its superior performance over single-modality prompt tuning approaches. Significantly, PathoTune facilitates the direct adaptation of natural visual foundation models to pathological tasks, drastically outperforming pathological foundation models with simple linear probing. The code will be available upon acceptance.
As Good As A Coin Toss: Human detection of AI-generated images, videos, audio, and audiovisual stimuli

As synthetic media becomes progressively more realistic and barriers to using it continue to lower, the technology has been increasingly utilized for malicious purposes, from financial fraud to nonconsensual pornography. Today, the principal defense against being misled by synthetic media relies on the ability of the human observer to visually and auditorily discern between real and fake. However, it remains unclear just how vulnerable people actually are to deceptive synthetic media in the course of their day to day lives. We conducted a perceptual study with 1276 participants to assess how accurate people were at distinguishing synthetic images, audio only, video only, and audiovisual stimuli from authentic. To reflect the circumstances under which people would likely encounter synthetic media in the wild, testing conditions and stimuli emulated a typical online platform, while all synthetic media used in the survey was sourced from publicly accessible generative AI technology. We find that overall, participants struggled to meaningfully discern between synthetic and authentic content. We also find that detection performance worsens when the stimuli contains synthetic content as compared to authentic content, images featuring human faces as compared to non face objects, a single modality as compared to multimodal stimuli, mixed authenticity as compared to being fully synthetic for audiovisual stimuli, and features foreign languages as compared to languages the observer is fluent in. Finally, we also find that prior knowledge of synthetic media does not meaningfully impact their detection performance. Collectively, these results indicate that people are highly susceptible to being tricked by synthetic media in their daily lives and that human perceptual detection capabilities can no longer be relied upon as an effective counterdefense.