Miracle-dz/UniFAR-FTUs huggingface dataset:
IR-UWB Radar-Based Contactless Silent Speech Recognition of Vowels, Consonants, Words, and Phrases

Several sensing techniques have been proposed for silent speech recognition (SSR); however, many of these methods require invasive processes or sensor attachment to the skin using adhesive tape or glue, rendering them unsuitable for frequent use in daily life. By contrast, impulse radio ultra-wideband (IR-UWB) radar can operate without physical contact with users’ articulators and related body parts, offering several advantages for SSR. These advantages include high range resolution, high penetrability, low power consumption, robustness to external light or sound interference, and the ability to be embedded in space-constrained handheld devices. This study demonstrated IR-UWB radar-based contactless SSR using four types of speech stimuli (vowels, consonants, words, and phrases). To achieve this, a novel speech feature extraction algorithm specifically designed for IR-UWB radar-based SSR is proposed. Each speech stimulus is recognized by applying a classification algorithm to the extracted speech features. Two different algorithms, multidimensional dynamic time warping (MD-DTW) and deep neural network—hidden Markov model (DNN–HMM), were compared for the classification task. Additionally, a favorable radar antenna position, either in front of the user’s lips or below the user’s chin, was determined to achieve higher recognition accuracy. Experimental results demonstrated the efficacy of the proposed speech feature extraction algorithm combined with DNN–HMM for classifying vowels, consonants, words, and phrases. Notably, this study represents the first demonstration of phoneme-level SSR using contactless radar.
On The Equivariant Tamagawa Number Conjecture

Let $F$ be a totally real field and $K$ a finite abelian CM extension of $F$. Using class field theory, we show that our previous result giving a strong form of the Brumer-Stark conjecture implies the minus part of the equivariant Tamagawa number conjecture for the Tate motive associated to $K/F$. We work integrally over $\mathbf{Z}$, in particular the prime 2 is not inverted. This note can be viewed as our perspective on the recent work of Bullach, Burns, Daoud, and Seo. Following their philosophy, we show that the functorial properties (i.e. norm compatibilities) connecting the strong Brumer-Stark conjecture for varying number fields actually implies the minus part of the equivariant Tamagawa number conjecture.
Deep Representation Learning for Open Vocabulary Electroencephalography-to-Text Decoding

Previous research has demonstrated the potential of using pre-trained language models for decoding open vocabulary Electroencephalography (EEG) signals captured through a non-invasive Brain-Computer Interface (BCI). However, the impact of embedding EEG signals in the context of language models and the effect of subjectivity, remain unexplored, leading to uncertainty about the best approach to enhance decoding performance. Additionally, current evaluation metrics used to assess decoding effectiveness are predominantly syntactic and do not provide insights into the comprehensibility of the decoded output for human understanding. We present an end-to-end deep learning framework for non-invasive brain recordings that brings modern representational learning approaches to neuroscience. Our proposal introduces the following innovations: 1) an end-to-end deep learning architecture for open vocabulary EEG decoding, incorporating a subject-dependent representation learning module for raw EEG encoding, a BART language model, and a GPT-4 sentence refinement module; 2) a more comprehensive sentence-level evaluation metric based on the BERTScore; 3) an ablation study that analyses the contributions of each module within our proposal, providing valuable insights for future research. We evaluate our approach on two publicly available datasets, ZuCo v1.0 and v2.0, comprising EEG recordings of 30 subjects engaged in natural reading tasks. Our model achieves a BLEU-1 score of 42.75%, a ROUGE-1-F of 33.28%, and a BERTScore-F of 53.86%, outperforming the previous state-of-the-art methods by 3.38%, 8.43%, and 6.31%, respectively.
Insert-Only versus Insert-Delete in Dynamic Query Evaluation

We study the dynamic query evaluation problem: Given a join query Q and a stream of updates, we would like to construct a data structure that supports constant-delay enumeration of the query output after each update. We show that a stream of N insert-only updates (to an initially empty database) can be executed in total time O(N^{w(Q)}), where w(Q) is the fractional hypertree width of Q. This matches the complexity of the static query evaluation problem for Q and a database of size N. One corollary is that the average time per single-tuple insert is constant for acyclic joins. In contrast, we show that a stream of N insert-and-delete updates to Q can be executed in total time O(N^{w(Q')}), where Q' is obtained from Q by extending every relational atom with extra variables that represent the"lifespans"of tuples in Q. We show that this reduction is optimal in the sense that the static evaluation runtime of Q' provides a lower bound on the total update time of Q. Our approach recovers the optimal single-tuple update time for known queries such as the hierarchical and Loomis-Whitney join queries.
Non-gravitational signals of dark energy under a gauge symmetry

We investigate non-gravitational signals of dark energy within the framework of gauge symmetry in the dark energy sector. Traditionally, dark energy has been primarily studied through gravitational effects within general relativity or its extensions. On the other hand, the gauge principles have played a central role in the standard model sector and dark matter sector. If the dark energy field operates under a gauge symmetry, it introduces the possibility of studying all major components of the present universe under the same gauge principle. This approach marks a significant shift from conventional methodologies, offering a new avenue to explore dark energy.
Partial Rewriting for Multi-Stage ASR

For many streaming automatic speech recognition tasks, it is important to provide timely intermediate streaming results, while refining a high quality final result. This can be done using a multi-stage architecture, where a small left-context only model creates streaming results and a larger left- and right-context model produces a final result at the end. While this significantly improves the quality of the final results without compromising the streaming emission latency of the system, streaming results do not benefit from the quality improvements. Here, we propose using a text manipulation algorithm that merges the streaming outputs of both models. We improve the quality of streaming results by around 10%, without altering the final results. Our approach introduces no additional latency and reduces flickering. It is also lightweight, does not require retraining the model, and it can be applied to a wide variety of multi-stage architectures.
Context-Aware Sequential Model for Multi-Behaviour Recommendation

Sequential recommendation models are crucial for next-item recommendations in online platforms, capturing complex patterns in user interactions. However, many focus on a single behavior, overlooking valuable implicit interactions like clicks and favorites. Existing multi-behavioral models often fail to simultaneously capture sequential patterns. We propose CASM, a Context-Aware Sequential Model, leveraging sequential models to seamlessly handle multiple behaviors. CASM employs context-aware multi-head self-attention for heterogeneous historical interactions and a weighted binary cross-entropy loss for precise control over behavior contributions. Experimental results on four datasets demonstrate CASM's superiority over state-of-the-art approaches.
HiER: Highlight Experience Replay and Easy2Hard Curriculum Learning for Boosting Off-Policy Reinforcement Learning Agents

Even though reinforcement-learning-based algorithms achieved superhuman performance in many domains, the field of robotics poses significant challenges as the state and action spaces are continuous, and the reward function is predominantly sparse. In this work, we propose: 1) HiER: highlight experience replay that creates a secondary replay buffer for the most relevant experiences, 2) E2H-ISE: an easy2hard data collection curriculum-learning method based on controlling the entropy of the initial state-goal distribution and with it, indirectly, the task difficulty, and 3) HiER+: the combination of HiER and E2H-ISE. They can be applied with or without the techniques of hindsight experience replay (HER) and prioritized experience replay (PER). While both HiER and E2H-ISE surpass the baselines, HiER+ further improves the results and significantly outperforms the state-of-the-art on the push, slide, and pick-and-place robotic manipulation tasks. Our implementation and further media materials are available on the project site.
A parallelized cellular Potts model that enables simulations at tissue scale

The Cellular Potts Model (CPM) is a widely used simulation paradigm for systems of interacting cells that has been used to study scenarios ranging from plant development to morphogenesis, tumour growth and cell migration. Despite their wide use, CPM simulations are considered too computationally intensive for three-dimensional (3D) models at organ scale. CPMs have been difficult to parallelise because of their inherently sequential update scheme. Here, we present a Graphical Processing Unit (GPU)-based parallelisation scheme that preserves local update statistics and is up to 3-4 orders of magnitude faster than serial implementations. We show several examples where our scheme preserves simulation behaviors that are drastically altered by existing parallelisation methods. We use our framework to construct tissue-scale models of liver and lymph node environments containing millions of cells that are directly based on microscopy-imaged tissue structures. Thus, our GPU-based CPM framework enables in silico studies of multicellular systems of unprecedented scale.
Hierarchical Graph Pattern Understanding for Zero-Shot VOS

The optical flow guidance strategy is ideal for obtaining motion information of objects in the video. It is widely utilized in video segmentation tasks. However, existing optical flow-based methods have a significant dependency on optical flow, which results in poor performance when the optical flow estimation fails for a particular scene. The temporal consistency provided by the optical flow could be effectively supplemented by modeling in a structural form. This paper proposes a new hierarchical graph neural network (GNN) architecture, dubbed hierarchical graph pattern understanding (HGPU), for zero-shot video object segmentation (ZS-VOS). Inspired by the strong ability of GNNs in capturing structural relations, HGPU innovatively leverages motion cues (\ie, optical flow) to enhance the high-order representations from the neighbors of target frames. Specifically, a hierarchical graph pattern encoder with message aggregation is introduced to acquire different levels of motion and appearance features in a sequential manner. Furthermore, a decoder is designed for hierarchically parsing and understanding the transformed multi-modal contexts to achieve more accurate and robust results. HGPU achieves state-of-the-art performance on four publicly available benchmarks (DAVIS-16, YouTube-Objects, Long-Videos and DAVIS-17). Code and pre-trained model can be found at \url{https://github.com/NUST-Machine-Intelligence-Laboratory/HGPU}.
The number of polygons inscribed into one and circumscribed around another polygon

In the paper Poncelet type problem about the number of convex $n$-gons inscribed into one convex $n$-gon and circumscribed around another convex $n$-gon, is studied. It is proved that this number is at most 4. This contrasts with Poncelet type porisms where usually infinitude of such polygons is proved, provided that one polygon exists. An inequality involving ratio of lengths of line segments is used. Alternative way of using Maclaurin-Braikenridge's conic generation method is also discussed. Properties related to constructibility with straightedge and compass are also studied. A new proof, based on mathematical induction, of generalized Maclaurin-Braikenridge's theorem is given.
Derivations, holonomy groups and heterotic geometry

We investigate the superalgebra of derivations generated by the fundamental forms on manifolds with reduced structure group. In particular, we point out a relation between the algebra of derivations of heterotic geometries that admit Killing spinors and the commutator algebra of holonomy symmetries in sigma models. We use this to propose a Lie bracket on the space of fundamental forms of all heterotic geometries with a non-compact holonomy group and present the associated derivation algebras. We also explore the extension of these results to heterotic geometries with compact holonomy groups and, more generally, to manifolds with reduced structure group. A brief review of the classification of heterotic geometries that admit Killing spinors and an extension of this classification to some heterotic inspired geometries are also included.
Taming Waves: A Physically-Interpretable Machine Learning Framework for Realizable Control of Wave Dynamics

Controlling systems governed by partial differential equations is an inherently hard problem. Specifically, control of wave dynamics is challenging due to additional physical constraints and intrinsic properties of wave phenomena such as dissipation, attenuation, reflection, and scattering. In this work, we introduce an environment designed for the study of the control of acoustic waves by actuated metamaterial designs. We utilize this environment for the development of a novel machine-learning method, based on deep neural networks, for efficiently learning the dynamics of an acoustic PDE from samples. Our model is fully interpretable and maps physical constraints and intrinsic properties of the real acoustic environment into its latent representation of information. Within our model we use a trainable perfectly matched layer to explicitly learn the property of acoustic energy dissipation. Our model can be used to predict and control scattered wave energy. The capabilities of our model are demonstrated on an important problem in acoustics, which is the minimization of total scattered energy. Furthermore, we show that the prediction of scattered energy by our model generalizes in time and can be extended to long time horizons. We make our code repository publicly available.
Tracking Skiers from the Top to the Bottom

Skiing is a popular winter sport discipline with a long history of competitive events. In this domain, computer vision has the potential to enhance the understanding of athletes' performance, but its application lags behind other sports due to limited studies and datasets. This paper makes a step forward in filling such gaps. A thorough investigation is performed on the task of skier tracking in a video capturing his/her complete performance. Obtaining continuous and accurate skier localization is preemptive for further higher-level performance analyses. To enable the study, the largest and most annotated dataset for computer vision in skiing, SkiTB, is introduced. Several visual object tracking algorithms, including both established methodologies and a newly introduced skier-optimized baseline algorithm, are tested using the dataset. The results provide valuable insights into the applicability of different tracking methods for vision-based skiing analysis. SkiTB, code, and results are available at https://machinelearning.uniud. it/datasets/skitb.
Competition of disorder and electron-phonon coupling in 2H-TaSe$_{2-x}$S$_x$ ($0\le x\le 2$) as evidenced by Raman spectroscopy

The vibrational properties of 2H-TaSe$_{2-x}$S$_x$ ($0 \le x \le 2$) single crystals were probed using Raman spectroscopy and density functional theory calculations. The end members revealed two out of four symmetry-predicted Raman active modes, together with the pronounced two-phonon structure, attributable to the enhanced electron-phonon coupling. Additional peaks become observable due to crystallographic disorder for the doped samples. The evolution of the E$_{2g}^2$ mode Fano parameter reveals that the disorder has weak impact on electron-phonon coupling, which is also supported by the persistence of two-phonon structure in doped samples. As such, this research provides thorough insights into the lattice properties, the effects of crystallographic disorder on Raman spectra, and the interplay of this disorder with the electron-phonon coupling in 2H-TaSe$_{2-x}$S$_x$ compounds.
Semantic-Aware Transformation-Invariant RoI Align

Great progress has been made in learning-based object detection methods in the last decade. Two-stage detectors often have higher detection accuracy than one-stage detectors, due to the use of region of interest (RoI) feature extractors which extract transformation-invariant RoI features for different RoI proposals, making refinement of bounding boxes and prediction of object categories more robust and accurate. However, previous RoI feature extractors can only extract invariant features under limited transformations. In this paper, we propose a novel RoI feature extractor, termed Semantic RoI Align (SRA), which is capable of extracting invariant RoI features under a variety of transformations for two-stage detectors. Specifically, we propose a semantic attention module to adaptively determine different sampling areas by leveraging the global and local semantic relationship within the RoI. We also propose a Dynamic Feature Sampler which dynamically samples features based on the RoI aspect ratio to enhance the efficiency of SRA, and a new position embedding, i.e., Area Embedding, to provide more accurate position information for SRA through an improved sampling area representation. Experiments show that our model significantly outperforms baseline models with slight computational overhead. In addition, it shows excellent generalization ability and can be used to improve performance with various state-of-the-art backbones and detection methods. The code is available at https://github.com/cxjyxxme/SemanticRoIAlign.
Dynamic Hysteresis Across a Dissipative Multi-Mode Phase Transition

Dissipative phase transitions are characteristic features in open quantum systems. Key signatures are the dynamical switching between different states in the vicinity of the phase transition and the appearance of hysteresis. Here, we experimentally study dynamic sweeps across a first order dissipative phase transition in a multi-mode driven-dissipative system. In contrast to previous studies, we perform sweeps of the dissipation strength instead of the driving strength. We extract exponents for the scaling of the hysteresis area in dependence of the sweep time and study the $g^{(2)}(0)$ correlations, which show non-trivial behavior. By changing the temperature of the system we investigate the importance of coherently pumping the system. We compare our results to numerical calculations done for a single mode variant of the system, and find surprisingly good agreement. Furthermore, we identify and discuss the differences between a scan of the dissipation strength and a scan of the driving strength.
On a Functional Definition of Intelligence

Without an agreed-upon definition of intelligence, asking"is this system intelligent? ""is an untestable question. This lack of consensus hinders research, and public perception, on Artificial Intelligence (AI), particularly since the rise of generative- and large-language models. Most work on precisely capturing what we mean by"intelligence"has come from the fields of philosophy, psychology, and cognitive science. Because these perspectives are intrinsically linked to intelligence as it is demonstrated by natural creatures, we argue such fields cannot, and will not, provide a sufficiently rigorous definition that can be applied to artificial means. Thus, we present an argument for a purely functional, black-box definition of intelligence, distinct from how that intelligence is actually achieved; focusing on the"what", rather than the"how". To achieve this, we first distinguish other related concepts (sentience, sensation, agency, etc.) from the notion of intelligence, particularly identifying how these concepts pertain to artificial intelligent systems. As a result, we achieve a formal definition of intelligence that is conceptually testable from only external observation, that suggests intelligence is a continuous variable. We conclude by identifying challenges that still remain towards quantifiable measurement. This work provides a useful perspective for both the development of AI, and for public perception of the capabilities and risks of AI.
VITA: A Multi-modal LLM-based System for Longitudinal, Autonomous, and Adaptive Robotic Mental Well-being Coaching

Recently, several works have explored if and how robotic coaches can promote and maintain mental well-being in different settings. However, findings from these studies revealed that these robotic coaches are not ready to be used and deployed in real-world settings due to several limitations that span from technological challenges to coaching success. To overcome these challenges, this paper presents VITA, a novel multi-modal LLM-based system that allows robotic coaches to autonomously adapt to the coachee's multi-modal behaviours (facial valence and speech duration) and deliver coaching exercises in order to promote mental well-being in adults. We identified five objectives that correspond to the challenges in the recent literature, and we show how the VITA system addresses these via experimental validations that include one in-lab pilot study (N=4) that enabled us to test different robotic coach configurations (pre-scripted, generic, and adaptive models) and inform its design for using it in the real world, and one real-world study (N=17) conducted in a workplace over 4 weeks. Our results show that: (i) coachees perceived the VITA adaptive and generic configurations more positively than the pre-scripted one, and they felt understood and heard by the adaptive robotic coach, (ii) the VITA adaptive robotic coach kept learning successfully by personalising to each coachee over time and did not detect any interaction ruptures during the coaching, (iii) coachees had significant mental well-being improvements via the VITA-based robotic coach practice. The code for the VITA system is openly available via: https://github.com/Cambridge-AFAR/VITA-system.
An indication of time-dependent seismic behavior—An assessment of paleoseismic evidence from the Arava Fault, Israel

We discuss the implications of 56 kyr of paleoseismic data from a fault on the southern segment of the Arava Fault system in Israel. At least nine events are evident, beginning 37 ka. We study in detail the frequency-magnitude relation for the fault and develop a Bayesian analysis to compare the characteristic earthquake (CE) model and the Gutenberg-Richter (GR) model. Our analysis leads to the conclusion that there was a change in seismic behavior at about 14 ka. From ∼37 ka to ∼14 ka, the seismic activity is best described by the CE model. From ∼14 ka to the present, there is no strong indication favoring one model over the other; the earthquake record is somewhat more consistent with the classical GR equation than with the CE model. If one does assume that the CE model is appropriate for the younger episode, then there is evidence of a change in the characteristic magnitude. Thus, there is evidence of a change in earthquake behavior on the fault segment ∼14 ka.