Miracle-dz/UniFAR-FTUs huggingface dataset:
Chain Conformation and Exciton Delocalization in a Push–Pull Conjugated Polymer

Linear and nonlinear optical line shapes reveal details of excitonic structure in polymer semiconductors. We implement absorption, photoluminescence, and transient absorption spectroscopies in DPP-DTT, an electron push–pull copolymer, to explore the relationship between their spectral line shapes and chain conformation, deduced from resonance Raman spectroscopy and from ab initio calculations. The viscosity of precursor polymer solutions before film casting displays a transition that suggests gel formation above a critical concentration. Upon crossing this viscosity deflection concentration, the line shape analysis of the absorption spectra within a photophysical aggregate model reveals a gradual increase in interchain excitonic coupling. We also observe a red-shifted and line-narrowed steady-state photoluminescence spectrum along with increasing resonance Raman intensity in the stretching and torsional modes of the dithienothiophene unit, which suggests a longer exciton coherence length along the polymer-chain backbone. Furthermore, we observe a change of line shape in the photoinduced absorption component of the transient absorption spectrum. The derivative-like line shape may originate from two possibilities: a new excited-state absorption or Stark effect, both of which are consistent with the emergence of a high-energy shoulder as seen in both photoluminescence and absorption spectra. Therefore, we conclude that the exciton is more dispersed along the polymer chain backbone with increasing concentrations, leading to the hypothesis that polymer chain order is enhanced when the push–pull polymers are processed at higher concentrations. Thus, tuning the microscopic chain conformation by concentration would be another factor of interest when considering the polymer assembly pathways for pursuing large-area and high-performance organic optoelectronic devices.
Hybrid Systems Neural Control with Region-of-Attraction Planner

Hybrid systems are prevalent in robotics. However, ensuring the stability of hybrid systems is challenging due to sophisticated continuous and discrete dynamics. A system with all its system modes stable can still be unstable. Hence special treatments are required at mode switchings to stabilize the system. In this work, we propose a hierarchical, neural network (NN)-based method to control general hybrid systems. For each system mode, we first learn an NN Lyapunov function and an NN controller to ensure the states within the region of attraction (RoA) can be stabilized. Then an RoA NN estimator is learned across different modes. Upon mode switching, we propose a differentiable planner to ensure the states after switching can land in next mode's RoA, hence stabilizing the hybrid system. We provide novel theoretical stability guarantees and conduct experiments in car tracking control, pogobot navigation, and bipedal walker locomotion. Our method only requires 0.25X of the training time as needed by other learning-based methods. With low running time (10-50X faster than model predictive control (MPC)), our controller achieves a higher stability/success rate over other baselines such as MPC, reinforcement learning (RL), common Lyapunov methods (CLF), linear quadratic regulator (LQR), quadratic programming (QP) and Hamilton-Jacobian-based methods (HJB). The project page is on https://mit-realm.github. io/hybrid-clf.
A Target-Based Extrinsic Calibration Framework for Non-Overlapping Camera-Lidar Systems Using a Motion Capture System

In this work, we present a novel target-based lidar-camera extrinsic calibration methodology that can be used for non-overlapping field of view (FOV) sensors. Contrary to previous work, our methodology overcomes the non-overlapping FOV challenge using a motion capture system (MCS) instead of traditional simultaneous localization and mapping approaches. Due to the high relative precision of the MCS, our methodology can achieve both the high accuracy and repeatable calibrations of traditional target-based methods, regardless of the amount of overlap in the field of view of the sensors. We show using simulation that we can accurately recover extrinsic calibrations for a range of perturbations to the true calibration that would be expected in real circumstances. We also validate that high accuracy calibrations can be achieved on experimental data. Furthermore, We implement the described approach in an extensible way that allows any camera model, target shape, or feature extraction methodology to be used within our framework. We validate this implementation on two target shapes: an easy to construct cylinder target and a diamond target with a checkerboard. The cylinder target shape results show that our methodology can be used for degenerate target shapes where target poses cannot be fully constrained from a single observation, and distinct repeatable features need not be detected on the target.
Enabling Immersion and Presence in the Metaverse with Over-the-Air Brain-Computer Interface

—Decoding brain signals can not only reveal Meta-verse users’ expectations but also early detect error-related behaviors such as stress, drowsiness, and motion sickness. For that, this article proposes a pioneering framework using wireless/over-the-air Brain-Computer Interface (BCI) to assist creation of virtual avatars as human representation in the Metaverse. Speciﬁcally, to eliminate the computational burden for Metaverse users’ devices, we leverage Wireless Edge Servers (WES) that are popular in 5G architecture and therein URLLC, enhanced broadband features to obtain and process the brain activities, i.e., electroencephalography (EEG) signals (via uplink wireless channels). As a result, the WES can learn human behaviors, adapt system conﬁgurations, and allocate radio resources to create individualized settings and enhance user experiences. Despite the potential of BCI, the inherent noisy/fading wireless channels and the uncertainty in Metaverse users’ demands and behaviors make the related resource allocation and learning/classiﬁcation problems particularly challenging. We formulate the joint learning and resource allocation problem as a Quality-of-Experience (QoE) maximization problem that takes into the latency, brain classiﬁcation accuracy, and resources of the system. To tackle this mixed integer programming problem, we then propose two novel algorithms that are (i) a hybrid learning algorithm to maximize the user QoE and (ii) a meta-learning algorithm to exploit the neurodiversity of the brain signals among multiple Metaverse users. The extensive experiment results with different BCI datasets show that our proposed algorithms can not only provide low delay for virtual reality (VR) applications but also can achieve high classiﬁcation accuracy for the collected brain signals.
ASymReg: Robust symmetric image registration using anti-symmetric formulation and deformation inversion layers

Deep learning based deformable medical image registration methods have emerged as a strong alternative for classical iterative registration methods. However, the currently published deep learning methods do not fulﬁll as strict symmetry properties with respect to the inputs as some classical registration methods, for which the registration outcome is the same regardless of the order of the inputs. While some deep learning methods label themselves as symmetric, they are either symmetric only a priori, which does not guarantee symmetry for any given input pair, or they do not generate accurate explicit inverses. In this work, we propose a novel registration architecture which by construction makes the registration network anti-symmetric with respect to its inputs. We demonstrate on two datasets that the proposed method achieves state-of-the-art results in terms of registration accuracy and that the generated deformations have accurate explicit inverses.
Quantifying the Value of Preview Information for Safety Control

Safety-critical systems, such as autonomous vehicles, often incorporate perception modules that can anticipate upcoming disturbances to system dynamics, expecting that such preview information can improve the performance and safety of the system in complex and uncertain environments. However, there is a lack of formal analysis of the impact of preview information on safety. In this work, we introduce a notion of safety regret, a properly defined difference between the maximal invariant set of a system with finite preview and that of a system with infinite preview, and show that this quantity corresponding to finite-step preview decays exponentially with preview horizon. Furthermore, algorithms are developed to numerically evaluate the safety regret of the system for different preview horizons. Finally, we demonstrate the established theory and algorithms via multiple examples from different application domains.
Bridging Deliberative Democracy and Deployment of Societal-Scale Technology

This position paper encourages the Human-Computer Interaction (HCI) community to focus on designing deliberative processes to inform and coordinate technology and policy design for large language models (LLMs) -- a `societal-scale technology'. First, I propose a definition for societal-scale technology and locate LLMs within this definition. Next, I argue that existing processes to ensure the safety of LLMs are insufficient and do not give the systems democratic legitimacy. Instead, we require processes of deliberation amongst users and other stakeholders on questions about the safety of outputs and deployment contexts. This shift in AI safety research and practice will require the design of corporate and public policies that determine how to enact deliberation and the design of interfaces and technical features to translate the outcomes of deliberation into technical development processes. To conclude, I propose roles for the HCI community to ensure deliberative processes inform technology and policy design for LLMs and other societal-scale technology.
Evaluation of Convolution Primitives for Embedded Neural Networks on 32-bit Microcontrollers

Deploying neural networks on constrained hardware platforms such as 32-bit microcontrollers is a challenging task because of the large memory, computing and energy requirements of their inference process. To tackle these issues, several convolution primitives have been proposed to make the standard convolution more computationally efficient. However, few of these primitives are really implemented for 32-bit microcontrollers. In this work, we collect different state-of-the-art convolutional primitives and propose an implementation for ARM Cortex-M processor family with an open source deployment platform (NNoM). Then, we carry out experimental characterization tests on these implementations. Our benchmark reveals a linear relationship between theoretical MACs and energy consumption. Thus showing the advantages of using computationally efficient primitives like shift convolution. We discuss about the significant reduction in latency and energy consumption due to the use of SIMD instructions and highlight the importance of data reuse in those performance gains. For reproducibility purpose and further experiments, codes and experiments are publicly available.
Inverse problem regularization with hierarchical variational autoencoders

In this paper, we propose to regularize ill-posed inverse problems using a deep hierarchical variational autoencoder (HVAE) as an image prior. The proposed method synthesizes the advantages of i) denoiser-based Plug & Play approaches and ii) generative model based approaches to inverse problems. First, we exploit VAE properties to design an efficient algorithm that benefits from convergence guarantees of Plug-and-Play (PnP) methods. Second, our approach is not restricted to specialized datasets and the proposed PnP-HVAE model is able to solve image restoration problems on natural images of any size. Our experiments show that the proposed PnP-HVAE method is competitive with both SOTA denoiser-based PnP approaches, and other SOTA restoration methods based on generative models. The code for this project is available at https://github.com/jprost76/PnP-HVAE.
ScribbleSeg: Scribble-based Interactive Image Segmentation

Interactive segmentation enables users to extract masks by providing simple annotations to indicate the target, such as boxes, clicks, or scribbles. Among these interaction formats, scribbles are the most flexible as they can be of arbitrary shapes and sizes. This enables scribbles to provide more indications of the target object. However, previous works mainly focus on click-based configuration, and the scribble-based setting is rarely explored. In this work, we attempt to formulate a standard protocol for scribble-based interactive segmentation. Basically, we design diversified strategies to simulate scribbles for training, propose a deterministic scribble generator for evaluation, and construct a challenging benchmark. Besides, we build a strong framework ScribbleSeg, consisting of a Prototype Adaption Module(PAM) and a Corrective Refine Module (CRM), for the task. Extensive experiments show that ScribbleSeg performs notably better than previous click-based methods. We hope this could serve as a more powerful and general solution for interactive segmentation. Our code will be made available.
SEQUENCES OF FRACTIONAL FACTORIAL PLANS.

Abstract : The report begins with the rationale underlying the performance of an experiment as a sequence of blocks and analyzing the data after each block has been observed. A description is given of several procedures for augmenting one-factor-at-a-time plans, some of which permit the estimation of interaction effects. The general procedure for obtaining orthogonal resolution IV plans as a sequence of two orthogonal main-effect plans is outlined. Consideration is given to adding small numbers of treatment combinations to resolution IV plans in order to obtain some information on a relatively few two-factor interaction effects. The remainder of the report deals with sequences of three or more fractional factorial plans involving from three to seventeen two-level factors. A summary of the properties of each sequence is presented. This summary contains the following information at each stage of every sequence: (1) The number of estimable main effects, (2) the number of estimable two-factor interaction effects, (3) the variance of the estimate of the mean, (4) the average variance of the estimable main effects, (5) the average variance of the estimable two-factor interaction effects, and (6) the average variance of the estimable main and two-factor interaction effects. (Author)
Nonclassical features in higher-dimensional systems through logical qudits

In a recent work [S. Asthana. New Journal of Physics 24.5 (2022): 053026], we have shown the interrelation of different nonclassical correlations in multiqubit systems with quantum coherence in a single logical qubit. In this work, we generalize it to higher-dimensional systems. For this, we take different choices of logical qudits and logical continuous-variable (cv) systems in terms of their constituent physical qudits and physical cv systems. Thereafter, we show reciprocity between conditions for coherence (in logical qudits and logical cv systems) and conditions for nonlocality and entanglement (in their underlying constituent qudits and cv systems). This shows that a single nonclassicality condition detects different types of nonclassicalities in different physical systems. Thereby, it reflects the interrelations of different nonclassical features of states belonging to Hilbert spaces of nonidentical dimensions.
Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers

The spatial resolution of images of living samples obtained by fluorescence microscopes is physically limited due to the diffraction of visible light, which makes the study of entities of size less than the diffraction barrier (around 200 nm in the x-y plane) very challenging. To overcome this limitation, several deconvolution and super-resolution techniques have been proposed. Within the framework of inverse problems, modern approaches in fluorescence microscopy reconstruct a super-resolved image from a temporal stack of frames by carefully designing suitable hand-crafted sparsity-promoting regularisers. Numerically, such approaches are solved by proximal gradient-based iterative schemes. Aiming at obtaining a reconstruction more adapted to sample geometries (e.g. thin filaments), we adopt a plug-and-play denoising approach with convergence guarantees and replace the proximity operator associated with the explicit image regulariser with an image denoiser (i.e. a pre-trained network) which, upon appropriate training, mimics the action of an implicit prior. To account for the independence of the fluctuations between molecules, the model relies on second-order statistics. The denoiser is then trained on covariance images coming from data representing sequences of fluctuating fluorescent molecules with filament structure. The method is evaluated on both simulated and real fluorescence microscopy images, showing its ability to correctly reconstruct filament structures with high values of peak signal-to-noise ratio (PSNR).
Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models

Pretrained language models (PLMs) have shown marvelous improvements across various NLP tasks. Most Chinese PLMs simply treat an input text as a sequence of characters, and completely ignore word information. Although Whole Word Masking can alleviate this, the semantics in words is still not well represented. In this paper, we revisit the segmentation granularity of Chinese PLMs. We propose a mixed-granularity Chinese BERT (MigBERT) by considering both characters and words. To achieve this, we design objective functions for learning both character and word-level representations. We conduct extensive experiments on various Chinese NLP tasks to evaluate existing PLMs as well as the proposed MigBERT. Experimental results show that MigBERT achieves new SOTA performance on all these tasks. Further analysis demonstrates that words are semantically richer than characters. More interestingly, we show that MigBERT also works with Japanese. Our code and model have been released here~\footnote{https://github.com/xnliang98/MigBERT}.
Erratum: On multi-nuclei features of some Markarian galaxies

We present a spectroscopic investigation of an unusual cluster of galaxies that contains galaxies Mrk 261 and 262 which have ultraviolet excess at the same redshift of about 0.03. We also study Mrk 266, which has a peculiar optical morphology and a binary nucleus. The nuclear components in both of these samples show quite similar emission lines in their spectra to Seyfert galaxy activity characteristics. The kinematics and physical properties of the gas in Mrk 266 allow a reliable mass estimate to be made assuming that the characteristic broad emission lines arise from the photoionization of the virialized clouds by the central ionizing nucleus. However, the nuclei masses would be overestimated if the radiation pressure and/or magnetic fields contribute significantly to the dynamics, or if the outflows or winds could cause the observed linewidths to exceed those induced by the nucleus potential alone.
Real-Time Parallel Programming: State of Play and Open Issues

Real-time systems applications usually consist of a set of concurrent activities with timing-related properties. Developing these applications requires programming paradigms that can effectively handle the specification of concurrent activities and timing constraints, as well as controlling their execution on a particular platform. The prevailing trend for high-performance, and the use of fine-grained parallel execution makes this an even more challenging task. This paper provides an overview of the state-of-the-art and challenges for the development of real-time parallel applications, focusing on two current research directions, one from the high-performance arena (based on OpenMP) and another from the real-time and critical systems domain (based on Ada). The paper provides a review of the supported features of each one of the approaches, identifying the still open issues and the current research directions.
Adaptive optics system for the observation of terrestrial point-like sources: results and problems

The development of a system intended as a demonstrator for improving the tracking of distant point-like sources through the correction of the atmospheric optical distortions is here reported. The demonstrator consists of a motorized mirror, which can pursuit a moving light source, united to an adaptive optics setup to improve the performances and the precision of the tracing of the object trajectory. The adaptive optics setup consists of a closed loop between a quad cell sensor and a tip-tilt mirror for the atmospheric jitter compensation and of another closed loop between a Shack-Hartmann sensor and a membrane deformable mirror for the compensation of higher order aberrations. Atmospheric measurements of an incoherent source will be also presented. In the case of our interest, where the atmospheric disturbances cannot be addressed only to a turbulent layer near the pupil (near field approximation), the scintillation becomes an important part of the noise. Its effects will be analyzed here, with particular attention to the influence on the wavefront sensor.
Unsupervised Gait Recognition with Selective Fusion

Previous gait recognition methods primarily trained on labeled datasets, which require painful labeling effort. However, using a pre-trained model on a new dataset without fine-tuning can lead to significant performance degradation. So to make the pre-trained gait recognition model able to be fine-tuned on unlabeled datasets, we propose a new task: Unsupervised Gait Recognition (UGR). We introduce a new cluster-based baseline to solve UGR with cluster-level contrastive learning. But we further find more challenges this task meets. First, sequences of the same person in different clothes tend to cluster separately due to the significant appearance changes. Second, sequences taken from 0 and 180 views lack walking postures and do not cluster with sequences taken from other views. To address these challenges, we propose a Selective Fusion method, which includes Selective Cluster Fusion (SCF) and Selective Sample Fusion (SSF). With SCF, we merge matched clusters of the same person wearing different clothes by updating the cluster-level memory bank with a multi-cluster update strategy. And in SSF, we merge sequences taken from front/back views gradually with curriculum learning. Extensive experiments show the effectiveness of our method in improving the rank-1 accuracy in walking with different coats condition and front/back views conditions.