Miracle-dz/UniFAR-FTUs huggingface dataset:
An Affective Video Database using Multimedia Content Analysis rated on Indian samples

Availability of naturalistic affective stimuli is needed for creating the affective technological solution as well as making progress in affective science. Although a lot of progress in the collection of affective multimedia stimuli has been made in western countries, the technology and ﬁndings based on such monocultural datasets may not be scalable to other cultures. Moreover, the available dataset on affective multimedia content has some experimenter bias in the initial manual selection of affective multimedia content. Hence, in this work, we mainly tried to address two problems. The ﬁrst problem relates to the experimenter’s subjective bias, and the second relates to the non-availability of affective multimedia dataset validated on Indian population. We tried to address both problems by reducing the experimenter’s bias as much as possible. We adopted the data science and multimedia content analysis techniques to perform our initial collection and a further selection of stimuli. Our method resulted in a dataset with a wide variety in content, stimuli from Western and Indian cinema, and symmetric presence of stimuli along valence, arousal and dominance dimensions. We conclude that using our method, more cross-cultural affective stimuli datasets can be created, which is essential to make progress in affective technology and science.
Cloth Funnels: Canonicalized-Alignment for Multi-Purpose Garment Manipulation

Automating garment manipulation is challenging due to extremely high variability in object configurations. To reduce this intrinsic variation, we introduce the task of “canonicalized-alignment” that simplifies downstream applications by reducing the possible garment configurations. This task can be considered as “cloth state funnel” that manipulates arbitrarily configured clothing items into a predefined deformable configuration (i.e. canonicalization) at an appropriate rigid pose (i.e. alignment). In the end, the cloth items will result in a compact set of structured and highly visible configurations - which are desirable for downstream manipulation skills. To enable this task, we propose a novel canonicalized-alignment objective that effectively guides learning to avoid adverse local minima during learning. Using this objective, we learn a multi-arm, multi-primitive policy that strategically chooses between dynamic flings and quasi-static pick and place actions to achieve efficient canonicalized-alignment. We evaluate this approach on a real-world ironing and folding system that relies on this learned policy as the common first step. Empirically, we demonstrate that our task-agnostic canonicalized-alignment can enable even simple manually -designed policies to work well where they were pre-viously inadequate, thus bridging the gap between automated non-deformable manufacturing and deformable manipulation.
Importance Weighting Correction of Regularized Least-Squares for Covariate and Target Shifts

In many real world problems, the training data and test data have different distributions. This situation is commonly referred as a dataset shift. The most common settings for dataset shift often considered in the literature are {\em covariate shift } and {\em target shift}. Importance weighting (IW) correction is a universal method for correcting the bias present in learning scenarios under dataset shift. The question one may ask is: does IW correction work equally well for different dataset shift scenarios? By investigating the generalization properties of the weighted kernel ridge regression (W-KRR) under covariate and target shifts we show that the answer is negative, except when IW is bounded and the model is wellspecified. In the latter cases, a minimax optimal rates are achieved by importance weighted kernel ridge regression (IW-KRR) in both, covariate and target shift scenarios. Slightly relaxing the boundedness condition of the IW we show that the IW-KRR still achieves the optimal rates under target shift while leading to slower rates for covariate shift. In the case of the model misspecification we show that the performance of the W-KRR under covariate shift could be substantially increased by designing an alternative reweighting function. The distinction between misspecified and wellspecified scenarios does not seem to be crucial in the learning problems under target shift.
Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging

Part-of-Speech (POS) tagging is an important component of the NLP pipeline, but many low-resource languages lack labeled data for training. An established method for training a POS tagger in such a scenario is to create a labeled training set by transferring from high-resource languages. In this paper, we propose a novel method for transferring labels from multiple high-resource source to low-resource target languages. We formalize POS tag projection as graph-based label propagation. Given translations of a sentence in multiple languages, we create a graph with words as nodes and alignment links as edges by aligning words for all language pairs. We then propagate node labels from source to target using a Graph Neural Network augmented with transformer layers. We show that our propagation creates training sets that allow us to train POS taggers for a diverse set of languages. When combined with enhanced contextualized embeddings, our method achieves a new state-of-the-art for unsupervised POS tagging of low-resource languages.
Spontaneous speech synthesis with linguistic-speech consistency training using pseudo-filled pauses

We propose a training method for spontaneous speech synthesis models that guarantees the consistency of linguistic parts of synthesized speech. Personalized spontaneous speech synthesis aims to reproduce the individuality of disﬂuency, such as ﬁlled pauses. Our prior model includes a ﬁlled-pause prediction model and synthe-sizes ﬁlled-pause-included speech from text without ﬁlled pauses. However, inserting the ﬁlled pauses degrades the quality of the linguistic parts of the synthesized speech. This might be because ﬁlled-pause insertion tendencies differ between training and inference, and the synthesis model cannot represent connections between ﬁlled pauses and surrounding phonemes in inference. We, therefore, developed a linguistic-speech consistency training that guarantees the consistency of linguistic parts of synthetic speech with and without ﬁlled pauses. The proposed consistency training utilizes not only ground-truth-ﬁlled pauses but also pseudo ones. Our experiments demonstrate that this method improves the naturalness of the synthetic linguistic speech and the entire predicted-ﬁlled-pause-included synthetic speech.
Anti-Symmetric DGN: a stable architecture for Deep Graph Networks

Deep Graph Networks (DGNs) currently dominate the research landscape of learning from graphs, due to their efficiency and ability to implement an adaptive message-passing scheme between the nodes. However, DGNs are typically limited in their ability to propagate and preserve long-term dependencies between nodes, i.e., they suffer from the over-squashing phenomena. This reduces their effectiveness, since predictive problems may require to capture interactions at different, and possibly large, radii in order to be effectively solved. In this work, we present Anti-Symmetric Deep Graph Networks (A-DGNs), a framework for stable and non-dissipative DGN design, conceived through the lens of ordinary differential equations. We give theoretical proof that our method is stable and non-dissipative, leading to two key results: long-range information between nodes is preserved, and no gradient vanishing or explosion occurs in training. We empirically validate the proposed approach on several graph benchmarks, showing that A-DGN yields to improved performance and enables to learn effectively even when dozens of layers are used.
A Signal Temporal Logic Motion Planner for Bird Diverter Installation Tasks With Multi-Robot Aerial Systems

This paper addresses the problem of task assignment and trajectory generation for installing bird diverters using a fleet of multi-rotors. The proposed solution extends our previous motion planner to compute feasible and constrained trajectories, considering payload capacity limitations and recharging constraints. Signal Temporal Logic (STL) specifications are employed to encode the mission objectives and temporal requirements. Additionally, an event-based replanning strategy is introduced to handle unforeseen failures. An energy minimization term is also employed to implicitly save multi-rotor flight time during installation operations. The effectiveness and validity of the approach are demonstrated through simulations in MATLAB and Gazebo, as well as field experiments carried out in a mock-up scenario.
How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios

In recent years, deep neural networks have demonstrated increasingly strong abilities to recognize objects and activities in videos. However, as video understanding becomes widely used in real-world applications, a key consideration is developing human-centric systems that understand not only the content of the video but also how it would affect the wellbeing and emotional state of viewers. To facilitate research in this setting, we introduce two large-scale datasets with over 60,000 videos manually annotated for emotional response and subjective wellbeing. The Video Cognitive Empathy (VCE) dataset contains annotations for distributions of fine-grained emotional responses, allowing models to gain a detailed understanding of affective states. The Video to Valence (V2V) dataset contains annotations of relative pleasantness between videos, which enables predicting a continuous spectrum of wellbeing. In experiments, we show how video models that are primarily trained to recognize actions and find contours of objects can be repurposed to understand human preferences and the emotional content of videos. Although there is room for improvement, predicting wellbeing and emotional response is on the horizon for state-of-the-art models. We hope our datasets can help foster further advances at the intersection of commonsense video understanding and human preference learning.
Importance of Eccentricities in Parameter Estimation of Compact Binary Inspirals with Decihertz Gravitational-Wave Detectors

Accurate and precise parameter estimation for compact binaries is essential for gravitational wave (GW) science. Future decihertz GW detectors, like B-DECIGO and MAGIS, are anticipated to realize exquisite precision in the parameter estimation of stellar-mass compact binaries. Most of the compact binaries are expected to have very small orbital eccentricities, but the small eccentricities can have non-negligible eﬀects on the parameter estimation due to the high precision measurement in the detectors. Here we discuss the accuracy requirements of GW waveform models for the parameter estimation of stellar-mass eccentric binaries with B-DECIGO and MAGIS. We ﬁnd that if the quasi-circular GW waveform model is used, even very small eccentricity ( ∼ 10 − 4 ) existing at GW frequency 0 . 1 Hz induces systematic errors that exceed statistical errors. We also show that eccentricity measurement in the decihertz detectors is precise enough to detect the small eccentricity ( ∼ 10 − 4 ). Ignoring the high-order Post-Newtonian (PN) corrections of eccentricity eﬀects does not make signiﬁcant systematic errors. As a result, including the 2 PN corrections of the eﬀects will be enough for the accurate eccentricity measurement.
Overlap-guided Gaussian Mixture Models for Point Cloud Registration

Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations. However, registering point cloud pairs in the case of partial overlap is still a challenge. This paper proposes a novel overlap-guided probabilistic registration approach that computes the optimal transformation from matched Gaussian Mixture Model (GMM) parameters. We reformulate the registration problem as the problem of aligning two Gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized. We introduce a Transformer-based detection module to detect overlapping regions, and represent the input point clouds using GMMs by guiding their alignment through overlap scores computed by this detection module. Experiments show that our method achieves superior registration accuracy and efficiency than state-of-the-art methods when handling point clouds with partial overlap and different densities on synthetic and real-world datasets. https://github.com/gfmei/ogmm
Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches, and Future Directions

Abstractive dialogue summarization generates a concise and fluent summary covering the salient information in a dialogue among two or more interlocutors. It has attracted significant attention in recent years based on the massive emergence of social communication platforms and an urgent requirement for efficient dialogue information understanding and digestion. Different from news or articles in traditional document summarization, dialogues bring unique characteristics and additional challenges, including different language styles and formats, scattered information, flexible discourse structures, and unclear topic boundaries. This survey provides a comprehensive investigation of existing work for abstractive dialogue summarization from scenarios, approaches to evaluations. It categorizes the task into two broad categories according to the type of input dialogues, i.e., open-domain and task-oriented, and presents a taxonomy of existing techniques in three directions, namely, injecting dialogue features, designing auxiliary training tasks, and using additional data. A list of datasets under different scenarios and widely accepted evaluation metrics are summarized for completeness. After that, the trends of scenarios and techniques are summarized, together with deep insights into correlations between extensively exploited features and different scenarios. Based on these analyses, we recommend future directions, including more controlled and complicated scenarios, technical innovations and comparisons, publicly available datasets in special domains, and so on.
Transferring Knowledge via Neighborhood-Aware Optimal Transport for Low-Resource Hate Speech Detection

The concerning rise of hateful content on online platforms has increased the attention towards automatic hate speech detection, commonly formulated as a supervised classification task. State-of-the-art deep learning-based approaches usually require a substantial amount of labeled resources for training. However, annotating hate speech resources is expensive, time-consuming, and often harmful to the annotators. This creates a pressing need to transfer knowledge from the existing labeled resources to low-resource hate speech corpora with the goal of improving system performance. For this, neighborhood-based frameworks have been shown to be effective. However, they have limited flexibility. In our paper, we propose a novel training strategy that allows flexible modeling of the relative proximity of neighbors retrieved from a resource-rich corpus to learn the amount of transfer. In particular, we incorporate neighborhood information with Optimal Transport, which permits exploiting the geometry of the data embedding space. By aligning the joint embedding and label distributions of neighbors, we demonstrate substantial improvements over strong baselines, in low-resource scenarios, on different publicly available hate speech corpora.
Adjusting for Non-confounding Covariates in Case-control Association Studies

There is a considerable literature in case-control logistic regression on whether or not non-confounding covariates should be adjusted for. However, only limited and ad hoc theoretical results are available on this important topic. A constrained maximum likelihood method was recently proposed, which appears to be generally more powerful than logistic regression methods with or without adjusting for non-confounding covariates. This note provides a theoretical clarification for the case-control logistic regression with and without covariate adjustment and the constrained maximum likelihood method on their relative performances in terms of asymptotic relative efficiencies. We show that the benefit of covariate adjustment in the case-control logistic regression depends on the disease prevalence. We also show that the constrained maximum likelihood estimator gives an asymptotically uniformly most powerful test.
Soft-Labeled Contrastive Pre-training for Function-level Code Representation

Code contrastive pre-training has recently achieved significant progress on code-related tasks. In this paper, we present \textbf{SCodeR}, a \textbf{S}oft-labeled contrastive pre-training framework with two positive sample construction methods to learn functional-level \textbf{Code} \textbf{R}epresentation. Considering the relevance between codes in a large-scale code corpus, the soft-labeled contrastive pre-training can obtain fine-grained soft-labels through an iterative adversarial manner and use them to learn better code representation. The positive sample construction is another key for contrastive pre-training. Previous works use transformation-based methods like variable renaming to generate semantically equal positive codes. However, they usually result in the generated code with a highly similar surface form, and thus mislead the model to focus on superficial code structure instead of code semantics. To encourage SCodeR to capture semantic information from the code, we utilize code comments and abstract syntax sub-trees of the code to build positive samples. We conduct experiments on four code-related tasks over seven datasets. Extensive experimental results show that SCodeR achieves new state-of-the-art performance on all of them, which illustrates the effectiveness of the proposed pre-training method.
Systematicity in GPT-3's Interpretation of Novel English Noun Compounds

Levin et al. (2019) show experimentally that the interpretations of novel English noun compounds (e.g., stew skillet), while not fully compositional, are highly predictable based on whether the modifier and head refer to artifacts or natural kinds. Is the large language model GPT-3 governed by the same interpretive principles? To address this question, we first compare Levin et al.'s experimental data with GPT-3 generations, finding a high degree of similarity. However, this evidence is consistent with GPT3 reasoning only about specific lexical items rather than the more abstract conceptual categories of Levin et al.'s theory. To probe more deeply, we construct prompts that require the relevant kind of conceptual reasoning. Here, we fail to find convincing evidence that GPT-3 is reasoning about more than just individual lexical items. These results highlight the importance of controlling for low-level distributional regularities when assessing whether a large language model latently encodes a deeper theory.
Deep Black-Box Reinforcement Learning with Movement Primitives

\Episode-based reinforcement learning (ERL) algorithms treat reinforcement learning (RL) as a black-box optimization problem where we learn to select a parameter vector of a controller, often represented as a movement primitive, for a given task descriptor called a context. ERL offers several distinct benefits in comparison to step-based RL. It generates smooth control trajectories, can handle non-Markovian reward definitions, and the resulting exploration in parameter space is well suited for solving sparse reward settings. Yet, the high dimensionality of the movement primitive parameters has so far hampered the effective use of deep RL methods. In this paper, we present a new algorithm for deep ERL. It is based on differentiable trust region layers, a successful on-policy deep RL algorithm. These layers allow us to specify trust regions for the policy update that are solved exactly for each state using convex optimization, which enables policies learning with the high precision required for the ERL. We compare our ERL algorithm to state-of-the-art step-based algorithms in many complex simulated robotic control tasks. In doing so, we investigate different reward formulations - dense, sparse, and non-Markovian. While step-based algorithms perform well only on dense rewards, ERL performs favorably on sparse and non-Markovian rewards. Moreover, our results show that the sparse and the non-Markovian rewards are also often better suited to define the desired behavior, allowing us to obtain considerably higher quality policies compared to step-based RL.
A Data-Driven Method for Locating Sensors and Selecting Alarm Thresholds to Identify Violations of Voltage Limits in Distribution Systems

—Stochastic ﬂuctuations in power injections from dis- tributed energy resources (DERs) combined with load variability can cause constraint violations (e.g., exceeded voltage limits) in electric distribution systems. To monitor grid operations, sensors are placed to measure important quantities such as the voltage magnitudes. In this paper, we consider a sensor placement problem which seeks to identify locations for installing sensors that can capture all possible violations of voltage magnitude limits. We formulate a bilevel optimization problem that minimizes the number of sensors and avoids false sensor alarms in the upper level while ensuring detection of any voltage violations in the lower level. This problem is challenging due to the nonlinearity of the power ﬂow equations and the presence of binary variables. Accordingly, we employ recently developed conservative linear approximations of the power ﬂow equations that overestimate or underestimate the voltage magnitudes. By replacing the nonlinear power ﬂow equations with conservative linear approximations, we can ensure that the resulting sensor locations and thresholds are sufﬁcient to identify any constraint violations. Additionally, we apply various problem reformulations to signiﬁcantly improve computational tractability while simultaneously ensuring an ap- propriate placement of sensors. Lastly, we improve the quality of the results via an approximate gradient descent method that adjusts the sensor thresholds. We demonstrate the effectiveness of our proposed method for several test cases, including a system with multiple switching conﬁgurations.
Verifiable blind quantum computation with identity authentication for different types of clients

Quantum computing has considerable advantages in solving some problems over its classical counterpart. Currently various physical systems are developed to construct quantum computers but it is still challenging and the first use of quantum computers may adopt the cloud style. Blind quantum computing (BQC) provides a solution for clients with limited quantum capabilities to delegate their quantum computation to remote quantum servers while keeping input, output, and even algorithm private. In this paper, we propose three multi-party verifiable blind quantum computing (VBQC) protocols with identity authentication to handle clients with varying quantum capabilities in quantum networks, such as those who can just make measurements, prepare single qubits, or perform a few single-qubit gates. They are client-friendly and flexible since the clients can achieve BQC depending on their own quantum devices and resist both insider outsider attacks in quantum networks. Furthermore, all the three proposed protocols are verifiable, namely that the clients can verify the correctness of their calculations.