Miracle-dz/UniFAR-FTUs huggingface dataset:
Barwise Compression Schemes for Audio-Based Music Structure Analysis

Music Structure Analysis (MSA) consists in segmenting a music piece in several distinct sections. We approach MSA within a compression framework, under the hypothesis that the structure is more easily revealed by a sim-pliﬁed representation of the original content of the song. More speciﬁcally, under the hypothesis that MSA is corre-lated with similarities occurring at the bar scale, this article introduces the use of linear and non-linear compression schemes on barwise audio signals. Compressed representations capture the most salient components of the different bars in the song and are then used to infer the song structure using a dynamic programming algorithm. This work explores both low-rank approximation models such as Principal Component Analysis or Nonnegative Matrix Factorization and “piece-speciﬁc” Auto-Encoding Neural Networks, with the objective to learn latent representations speciﬁc to a given song. Such approaches do not rely on supervision nor annotations, which are well-known to be tedious to collect and possibly ambiguous in MSA description. In our experiments, several unsupervised compression schemes achieve a level of performance comparable to that of state-of-the-art supervised methods (for 3s tolerance) on the RWC-Pop dataset, showcasing the importance of the barwise compression processing for MSA.
A non-local gradient based approach of infinity Laplacian with Γ-convergence

We propose an infinity Laplacian method to address the problem of interpolation on an unstructured point cloud. In doing so, we find the labeling function with the smallest infinity norm of its gradient. By introducing the non-local gradient, the continuous functional is approximated with a discrete form. The discrete problem is convex and can be solved efficiently with the split Bregman method. Experimental results indicate that our approach provides consistent interpolations and the labeling functions obtained are globally smooth, even in the case of extreme low sampling rate. More importantly, convergence of the discrete minimizer to the optimal continuous labeling function is proved using $\Gamma$-convergence and compactness, which guarantees the reliability of the infinity Laplacian method in various potential applications.
Scale Estimation with Dual Quadrics for Monocular Object SLAM

The scale ambiguity problem is inherently unsolvable to monocular SLAM without the metric baseline between moving cameras. In this paper, we present a novel scale estimation approach based on an object-level SLAM system. To obtain the absolute scale of the reconstructed map, we formulate an optimization problem to make the scaled dimensions of objects conform to the distribution of their sizes in the physical world, without relying on any prior information about gravity direction. The dual quadric is adopted to represent objects for its ability to describe objects compactly and accurately, thus providing reliable dimensions for scale estimation. In the proposed monocular object-level SLAM system, semantic objects are initialized first from fitted 3-D oriented bounding boxes and then further optimized under constraints of 2-D detections and 3-D map points. Experiments on indoor and outdoor public datasets show that our approach outperforms existing methods in terms of accuracy and robustness.
Diffusion‐induced anisotropic cancer invasion: a novel experimental method based on tumour spheroids

Tumor invasion is strongly influenced by microenvironment and, among other parameters, chemical stimuli play an important role. An innovative methodology for the quantitative investigation of chemotaxis in vitro by live imaging of morphology of cell spheroids, in 3D collagen gel, is presented here. The assay was performed by using a chemotactic chamber to impose a controlled gradients of nutrients (glucose) on spheroids, mimicking the chemotactic stimuli naturally occurring in the proximity of blood vessels. Different tumoral cell lines (PANC-1 and HT-1080) are compared to non-tumoral ones (NIH/3T3). Morphology response is observed by means a Time-lapse workstation equipped with an incubating system and quantified by image analysis techniques. Description of invasion phenomena was based on an engineering approach, based on transport phenomena concepts. As expected, NIH/3T3 spheroids are characterized by a limited tendency of cells to invade the surrounding tissue, unlike PANC-1 and HT-1080 that show relatively stronger response to gradients.
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition

Methods that extract policy primitives from offline demonstrations using deep generative models have shown promise at accelerating reinforcement learning(RL) for new tasks. Intuitively, these methods should also help to trainsafeRLagents because they enforce useful skills. However, we identify these techniques are not well equipped for safe policy learning because they ignore negative experiences(e.g., unsafe or unsuccessful), focusing only on positive experiences, which harms their ability to generalize to new tasks safely. Rather, we model the latentsafetycontextusing principled contrastive training on an offline dataset of demonstrations from many tasks, including both negative and positive experiences. Using this late variable, our RL framework, SAFEty skill pRiors (SAFER) extracts task-specific safe primitive skills to safely and successfully generalize to new tasks. In the inference stage, policies trained with SAFER learn to compose safe skills into successful policies. We theoretically characterize why SAFER can enforce safe policy learning and demonstrate its effectiveness on several complex safety-critical robotic grasping tasks inspired by the game Operation, in which SAFERoutperforms state-of-the-art primitive learning methods in success and safety.
Coverage Probability and Spectral Efficiency Analysis of Multi-Gateway Downlink LoRa Networks

The system-level performance of multi-gateway downlink long-range (LoRa) networks is investigated in the present paper. Specifically, we first compute the active probability of a channel and the selection probability of an active end-device (ED) in the closed-form expressions. We then derive the coverage probability (Pcov) and the area spectral efficiency (ASE) under the impact of the capture effects and different spreading factor (SF) allocation schemes. Our findings show that both the Pcov and the ASE of the considered networks can be enhanced significantly by increasing both the duty cycle and the transmit power. Finally, Monte-Carlo simulations are provided to verify the accuracy of the proposed mathematical frameworks.
GrASP: Gradient-Based Affordance Selection for Planning

Planning with a learned model is arguably a key component of intelligence. There are several challenges in realizing such a component in large-scale reinforcement learning (RL) problems. One such challenge is dealing effectively with continuous action spaces when using tree-search planning (e.g., it is not feasible to consider every action even at just the root node of the tree). In this paper we present a method for selecting affordances useful for planning -- for learning which small number of actions/options from a continuous space of actions/options to consider in the tree-expansion process during planning. We consider affordances that are goal-and-state-conditional mappings to actions/options as well as unconditional affordances that simply select actions/options available in all states. Our selection method is gradient based: we compute gradients through the planning procedure to update the parameters of the function that represents affordances. Our empirical work shows that it is feasible to learn to select both primitive-action and option affordances, and that simultaneously learning to select affordances and planning with a learned value-equivalent model can outperform model-free RL.
Ergodicity and long-time behavior of the Random Batch Method for interacting particle systems

We study the geometric ergodicity and the long time behavior of the Random Batch Method for interacting particle systems, which exhibits superior numerical performance in recent large-scale scientiﬁc computing experiments. We show that for both the interacting particle system (IPS) and the random batch interacting particle system (RB–IPS), the distribution laws converge to their respective invariant distributions exponentially, and the convergence rate does not depend on the number of particles N , the time step τ for batch divisions or the batch size p . Moreover, the Wasserstein distance between the invariant distributions of the IPS and the RB–IPS is bounded by O ( √ τ ), showing that the RB–IPS can be used to sample the invariant distribution of the IPS accurately with greatly reduced computational cost.
Quench dynamics of noninteracting fermions with a delta impurity

We study the out-of-equilibrium dynamics of noninteracting fermions in one dimension and in continuum space, in the presence of a delta impurity potential at the origin whose strength g is varied at time t = 0. The system is prepared in its ground state with g = g 0 = +∞, with two different densities and Fermi wave-vectors k L and k R on the two half-spaces x > 0 and x < 0 respectively. It then evolves for t > 0 as an isolated system, with a finite impurity strength g. We compute exactly the time dependent density and current. For a fixed position x and in the large time limit t → ∞, the system reaches a non-equilibrium stationary state (NESS). We obtain analytically the correlation kernel, density, particle current, and energy current in the NESS, and characterize their relaxation, which is algebraic in time. In particular, in the NESS, we show that, away from the impurity, the particle density displays oscillations which are the non-equilibrium analog of the Friedel oscillations. In the regime of ‘rays’, x/t = ξ fixed with x, t → ∞, we compute the same quantities and observe the emergence of two light cones, associated to the Fermi velocities k L and k R in the initial state. Interestingly, we find non trivial quantum correlations between two opposite rays with velocities ξ and −ξ which we compute explicitly. We extend to a continuum setting and to a correlated initial state the analytical methods developed in a recent work of Ljubotina, Sotiriadis and Prosen, in the context of a discrete fermionic chain with an impurity. We also generalize our results to an initial state at finite temperature, recovering, via explicit calculations, some predictions of conformal field theory in the low energy limit.
A New Fusion Strategy for Spoofing Aware Speaker Verification

The performance of automatic speaker veriﬁcation (ASV) systems could be degraded by voice spooﬁng attacks. Most existing works aimed to develop standalone spooﬁng countermeasure (CM) systems. Relatively little work aimed to develop an integrated spooﬁng aware speaker veriﬁcation (SASV) system. With the recent SASV challenge aiming to encourage the development of such integration, ofﬁcial protocols and baselines have been released by the organizers. Building on these base-lines, we assume a conditional independent relation between the ASV and CM subsystems and propose a new fusion strategy for inference and training based on a probability framework. Surprisingly, these strategies signiﬁcantly improve the SASV equal error rate (EER) from 19.31% of the baseline to 1.58% on the ofﬁcial evaluation trials of the SASV challenge. We verify the effectiveness of our proposed components through ablation studies and provide insights with score distribution analysis.
Convolutional Neural Bandit for Visual-aware Recommendation

Online recommendation/advertising is ubiquitous in web business. Image displaying is considered as one of the most commonly used formats to interact with customers. Contextual multi-armed bandit has shown success in the application of advertising to solve the exploration-exploitation dilemma existing in the recommendation procedure. Inspired by the visual-aware recommendation, in this paper, we propose a contextual bandit algorithm, where the convolutional neural network (CNN) is utilized to learn the reward function along with an upper confidence bound (UCB) for exploration. We also prove a near-optimal regret bound $\tilde{\mathcal{O}}(\sqrt{T})$ when the network is over-parameterized, and establish strong connections with convolutional neural tangent kernel (CNTK). Finally, we evaluate the empirical performance of the proposed algorithm and show that it outperforms other state-of-the-art UCB-based bandit algorithms on real-world image data sets.
Lower Bounds for Differentially Private ERM: Unconstrained and Non-Euclidean

We consider the lower bounds of differentially private empirical risk minimization (DP-ERM) for convex functions in constrained/unconstrained cases with respect to the general $\ell_p$ norm beyond the $\ell_2$ norm considered by most of the previous works. We provide a simple black-box reduction approach which can generalize lower bounds in constrained case to unconstrained case. For $(\epsilon,\delta)$-DP, we achieve $\Omega(\frac{\sqrt{d \log(1/\delta)}}{\epsilon n})$ lower bounds for both constrained and unconstrained cases and any $\ell_p$ geometry where $p\geq 1$ by introducing a novel biased mean property for fingerprinting codes, where $n$ is the size of the data-set and $d$ is the dimension.
Research and Design of Novel Real-time Optical-electrical Hybrid Image Recognition System

Based on FPGA+DSP+ARM'multiprocessor, a optical-electrical hybrid image recognition system was proposed and designed, which uses TMS320C6416 and FPGA to complete target image acquisition, pre-processing and distortion-invariant processing, uses the optical processing module to realize joint image FFT and obtain joint power spectrum, uses S3C2440 to complete spectrum acquisition, amplitude-modulated filtering, IFFT and image recognition. Extensive experiments show that this miniature intelligent system can process 25 images per second, and possesses high application value.
cVEP Training Data Validation—Towards Optimal Training Set Composition from Multi-Day Data

This paper investigates the effects of the repetitive block-wise training process on the classification accuracy for a code-modulated visual evoked potentials (cVEP)-based brain–computer interface (BCI). The cVEP-based BCIs are popular thanks to their autocorrelation feature. The cVEP-based stimuli are generated by a specific code pattern, usually the m-sequence, which is phase-shifted between the individual targets. Typically, the cVEP classification requires a subject-specific template (individually created from the user’s own pre-recorded EEG responses to the same stimulus target), which is compared to the incoming electroencephalography (EEG) data, using the correlation algorithms. The amount of the collected user training data determines the accuracy of the system. In this offline study, previously recorded EEG data collected during an online experiment with 10 participants from multiple sessions were used. A template matching target identification, with similar models as the task-related component analysis (TRCA), was used for target classification. The spatial filter was generated by the canonical correlation analysis (CCA). When comparing the training models from one session with the same session’s data (intra-session) and the model from one session with the data from the other session (inter-session), the accuracies were (94.84%, 94.53%) and (76.67%, 77.34%) for intra-sessions and inter-sessions, respectively. In order to investigate the most reliable configuration for accurate classification, the training data blocks from different sessions (days) were compared interchangeably. In the best training set composition, the participants achieved an average accuracy of 82.66% for models based only on two training blocks from two different sessions. Similarly, at least five blocks were necessary for the average accuracy to exceed 90%. The presented method can further improve cVEP-based BCI performance by reusing previously recorded training data.
On the joint optimization of content caching and recommendations

Content caching has been experiencing revived interest within the context of current and next generation wireless networks. It brings content closer to users, decreasing the aggregate delivery costs and service delays for network providers. Recommender systems have become integral components of content provision sites. They offer per - sonalized recommendations, increasing the individual user satisfaction and engagement with the content provider’s platform. Traditionally, caching and recommendation decisions are taken separately. However, there is a recent persistent trend where both network and content providers tend to deploy their own content delivery solutions. In light of this, we explore how the phenomenally conflicting objectives of content caching and recommendation can be jointly addressed. In this chapter we approach recommender systems as network traffic engineering tools that actively shape content demand to serve both user-and network-centric performance objectives. We introduce a model that captures the coupling between caching decisions and issued recommendations. Based on experimental evidence, we describe the impact of recommendations on user content requests and present a systematic way of engineering the user recommendations. Our viewpoint to recommender systems raises some concerns, not least ethical ones. Hence, we introduce a measure called preference distortion tolerance to quantify how much the engineered recommendations distort the original user content preferences. We describe the “recommendation window” as a way to controllably bound the distortion that recommendations will undergo and discuss specific properties that the recommender system should have in order to ensure that the “Quality of Recommendations (QoR)” is kept high by the issued recommendations. We formulate a joint optimization problem for the content to cache and recommend to each user, aiming to maximize the cache hit ratio. The preference distortion tolerance is embedded as a constraint to this problem formulation and marks an equally important dimension for assessing possible solutions. We prove that the problem lacks those prop - erties that would guarantee its approximability, and devise a low-complexity practical
Climate Change Vulnerability and Sustainable Urbanisation in Sub-Saharan African

This study investigates the role of climate change vulnerability in sustainable urbanisation using a panel of 31 Sub-Saharan African countries over the period 2000-2017. The study employs a dynamic model based on a sequential linear panel data estimator. The results show that the high levels of climate change vulnerability are associated with a decrease in sustainable urbanisation in Sub-Saharan Africa. More specifically, only the adaptative capacity component of climate change vulnerability has a significant negative influence on sustainable urbanization. The role of climate change vulnerability on sustainable urbanization seems to be robust to the sensitivity analysis and the GMM estimator. Otherwise, the results also show that GDP per capita and Foreign Direct Investment positively affect sustainable urbanisation, while industrialisation and migration rate have negative effects on it in Sub-Saharan Africa.
Corporate Social Responsibility Practices: A Strategy for Indian Startups to Sustain

The need for creating an environment for sustainable growth of startups for the well-being of an economy cannot be overemphasized. Many experts have proposed that corporate social responsibility (CSR) practices are integral to the long-term survival and success of startups (Jenkins, Confronting the challenges of participatory culture: Media education for the 21st century, 2009). At the same time, they also argue that it is very challenging for startups to adopt these practices effectively (Salamzadeh, 2015). In this background, this is an empirical study of promoters of startups in a developing economy, India, to test the linkages between CSR practices and startup success. The study empirically examines the perception of the promoters regarding these linkages, studies the CSR practices adopted by them, and identifies the challenges faced by them in using CSR for sustainable growth. The findings of the study reveal that there is a distinct relationship between startup success (sustainability) and CSR activities. The article empirically proves that the sustainability of a startup is positively dependent upon internal, external, and environmental CSR initiatives. It also concludes that definite structural interventions are required by policymakers to achieve the twin objectives of startup success and societal well-being.
Quantitative Fluorescence Imaging of Perfusion—An Algorithm to Predict Anastomotic Leakage

This study tests fluorescence imaging-derived quantitative parameters for perfusion evaluation of the gastric tube during surgery and correlates these parameters with patient outcomes in terms of anastomotic leakage. Poor fundus perfusion is seen as a major factor for the development of anastomotic leakage and strictures. Fluorescence perfusion imaging may reduce the incidence of complications. Parameters for the quantification of the fluorescence signal are still lacking. Quantitative parameters in terms of maximal intensity, mean slope and influx timepoint were tested for significant differences between four perfusion areas of the gastric tube in 22 patients with a repeated ANOVA test. These parameters were compared with patient outcomes. Maximal intensity, mean slope and influx timepoint were significantly different between the base of the gastric tube and the fundus (p < 0.0001). Patients who developed anastomotic leakage showed a mean slope of almost 0 in Location 4. The distance of the demarcation of ICG to the fundus was significantly higher in the three patients who developed anastomotic leakage (p < 0.0001). This study presents quantitative intra-operative perfusion imaging with fluorescence. Quantification of the fluorescence signal allows for early risk stratification of necrosis.
Lighting- and Personal Characteristic-Aware Markov Random Field Model for Facial Image Relighting System

In this study, we proposed an example-based facial image relighting framework to relight an unseen input 2D facial image from a specified source lighting condition to a target lighting. Such a relighting framework is highly challenging, since the highlight or shadow areas in the relighted image usually follow the specific facial feature characteristics of the input image. When facial images have to be lighted in a specified lighting condition style followed a few provided pairwise lighting examples, the problem becomes even more complex. In contrast to existing learning-based relighting framework that directly predicts the intensity of the target image, we use a personal and lighting-specific transformation to formulate the appearance correlation between source and target lighting conditions. We propose a lighting- and personal characteristic-aware Markov Random Field Model to estimate the transformation parameter of the input subject, which is integrated with additional classifiers to determine input facial regions should be enhanced or maintained. Experimental results show that the proposed kernel facial relighting model can avoid overfitting problem and generates vivid and recognizable results despite the scarcity of training samples. Furthermore, the relighted results successfully simulate the individual lighting effects produced by the specific personal characteristics of the input image, such as the nose and cheek shadows. Finally, the effectiveness of the proposed framework is demonstrated using a robust face verification test for images taken under side-light conditions.