Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition

Speech emotion recognition (SER) plays a vital role in improving the interactions between humans and machines by inferring human emotion and affective states from speech signals. Whereas recent works primarily focus on mining spatiotemporal information from hand-crafted features, we explore how to model the temporal patterns of speech emotions from dynamic temporal scales. Towards that goal, we introduce a novel temporal emotional modeling approach for SER, termed Temporal-aware bI-direction Multi-scale Network (TIM-Net), which learns multi-scale contextual affective representations from various time scales. Specifically, TIM-Net first employs temporal-aware blocks to learn temporal affective representation, then integrates complementary information from the past and the future to enrich contextual representations, and finally fuses multiple time scale features for better adaptation to the emotional variation. Extensive experimental results on six benchmark SER datasets demonstrate the superior performance of TIM-Net, gaining 2.34% and 2.61% improvements of the average UAR and WAR over the second-best on each corpus. The source code is available at https://github.com/Jiaxin-Ye/TIM-Net_SER.
A note on secondary K-theory

We prove that Toen's secondary Grothendieck ring is isomorphic to the Grothendieck ring of smooth proper pretriangulated dg categories previously introduced by Bondal, Larsen and Lunts. Along the way, we show that those short exact sequences of dg categories in which the first term is smooth proper and the second term is proper are necessarily split. As an application, we prove that the canonical map from the derived Brauer group to the secondary Grothendieck ring has the following injective properties: in the case of a commutative ring of characteristic zero, it distinguishes between dg Azumaya algebras associated to non-torsion cohomology classes and dg Azumaya algebras associated to torsion cohomology classes (=ordinary Azumaya algebras); in the case of a field of characteristic zero, it is injective; in the case of a field of positive characteristic p>0, it restricts to an injective map on the p-primary component of the Brauer group.
Casimir tests of scalar-tensor theories

We compute bounds and forecasts on screened modified gravity theories, specialising to the chameleon model in Casimir force experiments. In particular, we investigate the classical interaction between a plate and sphere subject to a screened interaction of the chameleon type. We compare numerical simulations of the field profile and the classical pressure exerted on the sphere to analytical approximations for these non-linear field theories. In particular, we focus on the proximity force approximation (PFA) and show that, within the range of sphere sizes $R$ and plate-sphere distance $D$ simulated numerically, the PFA does not reproduce the numerical results. This differs from the case of linear field theories such as Newtonian gravity and a Yukawa model where the PFA coincides with the exact results. We show that for chameleon theories, the screening factor approximation (SFA) whereby the sphere is modelled as a screened sphere embedded in the external field due to the plates, fares better and can be used in the regime $D\gtrsim R$ to extract constraints and forecasts from existing and forthcoming data. In particular, we forecast that future Casimir experiments would corroborate the closing of the parameter space for the simplest of chameleon models at the dark energy scale.
The Mass Accretion Rate and Stellar Properties in Class I Protostars

Stars collect most of their mass during the protostellar stage, yet the accretion luminosity and stellar parameters, which are needed to compute the mass accretion rate, are poorly constrained for the youngest sources. The aim of this work is to fill this gap, computing the stellar properties and the accretion rates for a large sample of Class I protostars located in nearby (<500 pc) star-forming regions and analyzing their interplay. We used a self-consistent method to provide accretion and stellar parameters by modeling the spectral energy distribution and using veiling information from near-IR observations when possible. We calculated accretion and stellar properties for the first time for 50 young stars. We focused our analysis on the 39 confirmed protostars, finding that their mass accretion rate varies between ∼10−8 and ∼10−4 M ⊙ yr−1 in a stellar mass range between ∼0.1 and 3 M ⊙. We find systematically larger mass accretion rates for our Class I sample than for Class II objects. Although the mass accretion rate we found is high, it still suggests that either stars collect most of their mass before the Class I stage, or eruptive accretion is needed during the overall protostellar phase. Indeed, our results suggest that for a large number of protostars the disk can be unstable, which can result in accretion bursts and disk fragmentation in the past or in the future.
Reverberation as Supervision For Speech Separation

This paper proposes reverberation as supervision (RAS), a novel unsupervised loss function for single-channel reverberant speech separation. Prior methods for unsupervised separation required the synthesis of mixtures of mixtures or assumed the existence of a teacher model, making them difficult to consider as potential methods explaining the emergence of separation abilities in an animal’s auditory system. We assume the availability of two-channel mixtures at training time, and train a neural network to separate the sources given one of the channels as input such that the other channel may be predicted from the separated sources. As the relationship between the room impulse responses (RIRs) of each channel depends on the locations of the sources, which are unknown to the network, the network cannot rely on learning that relationship. Instead, our proposed loss function fits each of the separated sources to the mixture in the target channel via Wiener filtering, and compares the resulting mixture to the ground-truth one. We show that minimizing the scale-invariant signal-to-distortion ratio (SI-SDR) of the predicted right-channel mixture with respect to the ground truth implicitly guides the network towards separating the left-channel sources. On a semi-supervised reverberant speech separation task based on the WHAMR! dataset, using training data where just 5% (resp., 10%) of the mixtures are labeled with associated isolated sources, we achieve 70% (resp., 78%) of the SI-SDR improvement obtained when training with supervision on the full training set, while a model trained only on the labeled data obtains 43% (resp., 45%).
Performance Limit of Fiber-Longitudinal Power Profile Estimation Methods

This paper presents analytical results on longitudinal power profile estimation (PPE) methods, which visualize signal power evolution in optical fibers at a coherent receiver. The PPE can be formulated as an inverse problem of the nonlinear Schrödinger equation, where the nonlinear coefficient (and thus signal power) is reconstructed from boundary conditions, i.e., transmitted and received signals. Two types of PPE methods are reviewed and analyzed, including correlation-based methods (CMs) and minimum-mean-square-error-based methods (MMSEs). The analytical expressions for their output power profiles and spatial resolution are provided, and thus the theoretical performance limits of the two PPE methods and their differences are clarified. The derived equations indicate that the estimated power profiles of CMs can be understood as the convolution of a true power profile and a smoothing function. Consequently, the spatial resolution and measurement accuracy of CMs are limited, even under noiseless and distortionless conditions. Closed-form formulas for the spatial resolution of CMs are shown to be inversely proportional to the product of a chromatic dispersion coefficient and the square of signal bandwidth. With MMSEs, such a convolution effect is canceled out and the estimated power profiles approach a true power profile under a fine spatial step size.
Characterizing a supernova’s standing accretion shock instability with neutrinos and gravitational waves

We perform a novel multi-messenger analysis for the identification and parameter estimation of the Standing Accretion Shock Instability (SASI) in a core collapse supernova with neutrino and gravitational wave (GW) signals. In the neutrino channel, this method performs a likelihood ratio test for the presence of SASI in the frequency domain. For gravitational wave signals we process an event with a modified constrained likelihood method. Using simulated supernova signals, the properties of the Hyper-Kamiokande neutrino detector, and O3 LIGO Interferometric data, we produce the two-dimensional probability density function (PDF) of the SASI activity indicator and calculate the probability of detection $P_\mathrm{D}$ as well as the false identification probability $P_\mathrm{FI}$. We discuss the probability to establish the presence of the SASI as a function of the source distance in each observational channel, as well as jointly. Compared to a single-messenger approach, the joint analysis results in $P_\mathrm{D}$ (at $P_\mathrm{FI}=0.1$) of SASI activities that is larger by up to $\approx~40\%$ for a distance to the supernova of 5 kpc. We also discuss how accurately the frequency and duration of the SASI activity can be estimated in each channel separately. Our methodology is suitable for implementation in a realistic data analysis and a multi-messenger setting.
Notes on Aharoni's rainbow cycle conjecture

In 2017, Ron Aharoni made the following conjecture about rainbow cycles in edge-coloured graphs: If $G$ is an $n$-vertex graph whose edges are coloured with $n$ colours and each colour class has size at least $r$, then $G$ contains a rainbow cycle of length at most $\lceil \frac{n}{r} \rceil$. One motivation for studying Aharoni's conjecture is that it is a strengthening of the Caccetta-H\"aggkvist conjecture on digraphs from 1978. In this article, we present a survey of Aharoni's conjecture, including many recent partial results and related conjectures. We also present two new results. Our main new result is for the $r=3$ case of Aharoni's conjecture. We prove that if $G$ is an $n$-vertex graph whose edges are coloured with $n$ colours and each colour class has size at least 3, then $G$ contains a rainbow cycle of length at most $\frac{4n}{9}+7$. We also discuss how our approach might generalise to larger values of $r$.
Improving Children's Speech Recognition by Fine-tuning Self-supervised Adult Speech Representations

Children's speech recognition is a vital, yet largely overlooked domain when building inclusive speech technologies. The major challenge impeding progress in this domain is the lack of adequate child speech corpora; however, recent advances in self-supervised learning have created a new opportunity for overcoming this problem of data scarcity. In this paper, we leverage self-supervised adult speech representations and use three well-known child speech corpora to build models for children's speech recognition. We assess the performance of fine-tuning on both native and non-native children's speech, examine the effect of cross-domain child corpora, and investigate the minimum amount of child speech required to fine-tune a model which outperforms a state-of-the-art adult model. We also analyze speech recognition performance across children's ages. Our results demonstrate that fine-tuning with cross-domain child corpora leads to relative improvements of up to 46.08% and 45.53% for native and non-native child speech respectively, and absolute improvements of 14.70% and 31.10%. We also show that with as little as 5 hours of transcribed children's speech, it is possible to fine-tune a children's speech recognition system that outperforms a state-of-the-art adult model fine-tuned on 960 hours of adult speech.
Impact of spiking neurons leakages and network recurrences on event-based spatio-temporal pattern recognition

Spiking neural networks coupled with neuromorphic hardware and event-based sensors are getting increased interest for low-latency and low-power inference at the edge. However, multiple spiking neuron models have been proposed in the literature with different levels of biological plausibility and different computational features and complexities. Consequently, there is a need to define the right level of abstraction from biology in order to get the best performance in accurate, efficient and fast inference in neuromorphic hardware. In this context, we explore the impact of synaptic and membrane leakages in spiking neurons. We confront three neural models with different computational complexities using feedforward and recurrent topologies for event-based visual and auditory pattern recognition. Our results showed that, in terms of accuracy, leakages are important when there are both temporal information in the data and explicit recurrence in the network. Additionally, leakages do not necessarily increase the sparsity of spikes flowing in the network. We also investigated the impact of heterogeneity in the time constant of leakages. The results showed a slight improvement in accuracy when using data with a rich temporal structure, thereby validating similar findings obtained in previous studies. These results advance our understanding of the computational role of the neural leakages and network recurrences, and provide valuable insights for the design of compact and energy-efficient neuromorphic hardware for embedded systems.
An approach for Test Impact Analysis on the Integration Level in Java programs

Test Impact Analysis is an approach to obtain a subset of tests impacted by code changes. This approach is mainly applied to unit testing where the link between the code and its associated tests is easy to obtain. On the integration level, however, it is not straightforward to find such a link programmatically, especially when the integration tests are held into separate repositories. We propose an approach for selecting integration tests based on the runtime analysis of code changes to reduce the test execution overhead. We provide a set of tools and a framework that can be plugged into existing CI/CD pipelines. We have evaluated the approach on a range of open-source Java programs and found $\approx$50\% reduction in tests on average, and above 80\% in a few cases. We have also applied the approach to a large-scale commercial system in production and found similar results.
Deep Instance Segmentation and Visual Servoing to Play Jenga with a Cost-Effective Robotic System

The game of Jenga is a benchmark used for developing innovative manipulation solutions for complex tasks. Indeed, it encourages the study of novel robotics methods to successfully extract blocks from a tower. A Jenga game involves many traits of complex industrial and surgical manipulation tasks, requiring a multi-step strategy, the combination of visual and tactile data, and the highly precise motion of a robotic arm to perform a single block extraction. In this work, we propose a novel, cost-effective architecture for playing Jenga with e.Do, a 6DOF anthropomorphic manipulator manufactured by Comau, a standard depth camera, and an inexpensive monodirectional force sensor. Our solution focuses on a visual-based control strategy to accurately align the end-effector with the desired block, enabling block extraction by pushing. To this aim, we trained an instance segmentation deep learning model on a synthetic custom dataset to segment each piece of the Jenga tower, allowing for visual tracking of the desired block’s pose during the motion of the manipulator. We integrated the visual-based strategy with a 1D force sensor to detect whether the block could be safely removed by identifying a force threshold value. Our experimentation shows that our low-cost solution allows e.DO to precisely reach removable blocks and perform up to 14 consecutive extractions in a row.
Disentangling Variational Autoencoders

A variational autoencoder (VAE) is a probabilistic machine learning framework for posterior inference that projects an input set of high-dimensional data to a lower-dimensional, latent space. The latent space learned with a VAE offers exciting opportunities to develop new data-driven design processes in creative disciplines, in particular, to automate the generation of multiple novel designs that are aesthetically reminiscent of the input data but that were unseen during training. However, the learned latent space is typically disorganized and entangled: traversing the latent space along a single dimension does not result in changes to single visual attributes of the data. The lack of latent structure impedes designers from deliberately controlling the visual attributes of new designs generated from the latent space. This paper presents an experimental study that investigates latent space disentanglement. We implement three different VAE models from the literature and train them on a publicly available dataset of 60,000 images of hand-written digits. We perform a sensitivity analysis to find a small number of latent dimensions necessary to maximize a lower bound to the log marginal likelihood of the data. Furthermore, we investigate the trade-offs between the quality of the reconstruction of the decoded images and the level of disentanglement of the latent space. We are able to automatically align three latent dimensions with three interpretable visual properties of the digits: line weight, tilt and width. Our experiments suggest that i) increasing the contribution of the Kullback-Leibler divergence between the prior over the latents and the variational distribution to the evidence lower bound, and ii) conditioning input image class enhances the learning of a disentangled latent space with a VAE.
Enhancing visibility of graphene on arbitrary substrates by microdroplet condensation

In order to take advantage of the enormous potential of graphene for future electronic micro-circuits and other applications it is necessary to develop reliable, rapid and widely applicable methods to visualize graphene based structures. We report here on a micro-droplet condensation technique, which allows for quick visual identification of graphene on a variety of substrates, including some which were previously considered unsuitable for the visualization of carbon layers. The technique should also be applicable to visualize artificially patterned graphene structures which are expected to be key technologically enabling components in electronic micro-circuits and other applications.
Mathematical Modeling of Drug Use: The Dynamics of Monosubstance Dependence for Two Addictive Drugs

Based on previous work done in this ﬁeld, we build a dynamical system that describes changes in drug addiction in an isolated population when two addictive substances are available simultaneously. We then use our model to investigate whether the system captures the process of users switching drug habits. One of the motivations for this project is to mathematically check the conjecture that being addicted to a less-addictive substance will eﬀectively lead individuals to become dependent on more addictive and potentially more dangerous drugs. We introduce additional assumptions, under which our model is reduced to a competitive Lotka-Volterra system. This dynamical system has three or four ﬁxed points, stability of which then gives an implication about the outcomes of the competition between addictive substances and, therefore, the fate of individuals in the population. From the analysis of the reduced model, we determine that there actually exist parameter regimes that capture the following dynamics: depending on the initial distribution of the drug preference; either the use of both drugs will die out, the usage of one of the drugs will become prevalent, or addictions to both drugs will coexist.
Investigating the Frequency Distortion of Word Embeddings and Its Impact on Bias Metrics

Recent research has shown that static word embeddings can encode word frequency information. However, little has been studied about this phenomenon and its effects on downstream tasks. In the present work, we systematically study the association between frequency and semantic similarity in several static word embeddings. We find that Skip-gram, GloVe and FastText embeddings tend to produce higher semantic similarity between high-frequency words than between other frequency combinations. We show that the association between frequency and similarity also appears when words are randomly shuffled. This proves that the patterns found are not due to real semantic associations present in the texts, but are an artifact produced by the word embeddings. Finally, we provide an example of how word frequency can strongly impact the measurement of gender bias with embedding-based metrics. In particular, we carry out a controlled experiment that shows that biases can even change sign or reverse their order by manipulating word frequencies.
Agent-State Construction with Auxiliary Inputs

In many, if not every realistic sequential decision-making task, the decision-making agent is not able to model the full complexity of the world. The environment is often much larger and more complex than the agent, a setting also known as partial observability. In such settings, the agent must leverage more than just the current sensory inputs; it must construct an agent state that summarizes previous interactions with the world. Currently, a popular approach for tackling this problem is to learn the agent-state function via a recurrent network from the agent's sensory stream as input. Many impressive reinforcement learning applications have instead relied on environment-specific functions to aid the agent's inputs for history summarization. These augmentations are done in multiple ways, from simple approaches like concatenating observations to more complex ones such as uncertainty estimates. Although ubiquitous in the field, these additional inputs, which we term auxiliary inputs, are rarely emphasized, and it is not clear what their role or impact is. In this work we explore this idea further, and relate these auxiliary inputs to prior classic approaches to state construction. We present a series of examples illustrating the different ways of using auxiliary inputs for reinforcement learning. We show that these auxiliary inputs can be used to discriminate between observations that would otherwise be aliased, leading to more expressive features that smoothly interpolate between different states. Finally, we show that this approach is complementary to state-of-the-art methods such as recurrent neural networks and truncated back-propagation through time, and acts as a heuristic that facilitates longer temporal credit assignment, leading to better performance.