Miracle-dz/UniFAR-FTUs huggingface dataset:
Process Mining in the Education Domain

Given the ever changing needs of the job markets, education and training centers are increasingly held accountable for student success. Therefore, education and training centers have to focus on ways to streamline their offers and educational processes in order to achieve the highest level of quality in curriculum contents and managerial decisions. Educational process mining is an emerging field in the educational data mining (EDM) discipline, concerned with developing methods to better understand students' learning habits and the factors influencing their performance. It aims, particularly, at discovering, analyzing, and providing a visual representation of complete educational processes. In this paper, in continuity of the work presented in (1), we investigate further the potential, challenges and feasibility of the educational process mining in the field of professional trainings. First, we focus on the mining and the analysis of social networks, from educational event logs, between courses units, resources or training providers. Second, we propose a clustering approach to decompose educational processes following key performance indicators. We have experimented this approach using the ProM Framework.
Assessing postural instability during cerebral hypoperfusion using sub-millimeter monocular 3D sway tracking

Postural instability is prevalent in aging and neurodegenerative disease, decreasing quality of life and independence. Quantitatively monitoring balance control is important for assessing treatment efficacy and rehabilitation progress. However, existing technologies for assessing postural sway are complex and expensive, limiting their widespread utility. Here, we propose a monocular imaging system capable of assessing sub-millimeter 3D sway dynamics. By physically embedding anatomical targets with known \textit{a priori} geometric models, 3D central and upper body kinematic motion was automatically assessed through geometric feature tracking and 3D kinematic motion inverse estimation from a set of 2D frames. Sway was tracked in 3D and compared between control and hypoperfusion conditions. The proposed system demonstrated high agreement with a commercial motion capture system (error $4.4 \times 10^{-16} \pm 0.30$~mm, $r^2=0.9773$). Significant differences in sway dynamics were observed in early stance central anterior-posterior sway (control: $147.1 \pm 7.43$~mm, hypoperfusion: $177.8 \pm 15.3$~mm; $p=0.039$) and mid stance upper body coronal sway (control: $106.3 \pm 5.80$~mm, hypoperfusion: $128.1 \pm 18.4$~mm; $p=0.040$) commensurate with cerebral blood flow (CBF) perfusion deficit, followed by recovered sway dynamics during late stance governed by CBF recovery. This inexpensive single-camera system enables quantitative 3D sway monitoring for assessing neuromuscular balance control in weakly constrained environments.
CallƐ: an effect system for method calls

Effect systems are used to statically reason about the effects an expression may have when evaluated. In the literature, such effects include various behaviours as diverse as memory accesses and exception throwing. Here we present CallƐ, an object-oriented language that takes a flexible approach where effects are just method calls: this works well because ordinary methods often model things like I/O operations, access to global state, or primitive language operations such as thread creation. CallƐ supports both flexible and fine-grained control over such behaviour, in a way designed to minimise the complexity of annotations. CallƐ’s effect system can be used to prevent OO code from performing privileged operations, such as querying a database, modifying GUI widgets, exiting the program, or performing network communication. It can also be used to ensure determinism, by preventing methods from (indirectly) calling non-deterministic primitives like random number generation or file reading.
A supervised neural network for drag prediction of arbitrary 2D shapes in low Reynolds number flows

Despite the significant breakthrough of neural networks in the last few years, their spreading in the field of computational fluid dynamics is very recent, and many applications remain to explore. In this paper, we explore the drag prediction capabilities of convolutional neural networks for laminar, low-Reynolds number flows past arbitrary 2D shapes. A set of random shapes exhibiting a rich variety of geometrical features is built using Bezier curves. The efficient labelling of the shapes is provided using an immersed method to solve a unified Eulerian formulation of the Navier-Stokes equation. The network is then trained and optimized on the obtained dataset, and its predictive efficiency assessed on several real-life shapes, including NACA airfoils.
Minimax Theorems for Finite Blocklength Lossy Joint Source-Channel Coding over an AVC

Motivated by applications in the security of cyber-physical systems, we pose the finite blocklength communication problem in the presence of a jammer as a zero-sum game between the encoder-decoder team and the jammer, by allowing the communicating team as well as the jammer only locally randomized strategies. The communicating team's problem is non-convex under locally randomized codes, and hence, in general, a minimax theorem need not hold for this game. However, we show that approximate minimax theorems hold in the sense that the minimax and maximin values of the game approach each other asymptotically. In particular, for rates strictly below a critical threshold, both the minimax and maximin values approach zero, and for rates strictly above it, they both approach unity. We then show a second order minimax theorem, i.e., for rates exactly approaching the threshold with along a specific scaling, the minimax and maximin values approach the same constant value, that is neither zero nor one. Critical to these results is our derivation of finite blocklength bounds on the minimax and maximin values of the game and our derivation of second order dispersion-based bounds.
Gravitational waves from double-inflection-point inflation

We study the production of gravitational waves from primordial scalar perturbations in double-inflection-point inflation, in which one of the inflection points predicts the power spectra consistent with CMB observations at large scales and the other generates a large peak in the power spectrum of scalar perturbations at small scales. We find that the reduced gravitational waves at low frequencies can be detected by future space-based laser interferometers.
Weakly Supervised Person Re-ID: Differentiable Graphical Learning and a New Benchmark

Person reidentification (Re-ID) benefits greatly from the accurate annotations of existing data sets (e.g., CUHK03 and Market-1501), which are quite expensive because each image in these data sets has to be assigned with a proper label. In this work, we ease the annotation of Re-ID by replacing the accurate annotation with inaccurate annotation, i.e., we group the images into bags in terms of time and assign a bag-level label for each bag. This greatly reduces the annotation effort and leads to the creation of a large-scale Re-ID benchmark called SYSU- $30k$ . The new benchmark contains 30k individuals, which is about 20 times larger than CUHK03 (1.3k individuals) and Market-1501 (1.5k individuals), and 30 times larger than ImageNet (1k categories). It sums up to 29606918 images. Learning a Re-ID model with bag-level annotation is called the weakly supervised Re-ID problem. To solve this problem, we introduce a differentiable graphical model to capture the dependencies from all images in a bag and generate a reliable pseudolabel for each person’s image. The pseudolabel is further used to supervise the learning of the Re-ID model. Compared with the fully supervised Re-ID models, our method achieves state-of-the-art performance on SYSU- $30k$ and other data sets. The code, data set, and pretrained model will be available at https://github.com/wanggrun/SYSU-30k.
Joint Radio Resource Allocation and 3D Beam-forming in MISO-NOMA-based Network with Profit Maximization for Mobile Virtual Network Operators

Massive connections and high data rate services are key players in 5G ecosystem and beyond. To satisfy the requirements of these types of services, non orthogonal multiple Access (NOMA) and 3-dimensional beam-forming (3DBF) can be exploited. In this paper, we devise a novel joint radio resource allocation and 3D multiple input single output (MISO) BF algorithm in NOMA-based heterogeneous networks (HetNets) at which our main aim is to maximize the profit of mobile virtual network operators (MVNOs). To this end, we consider multiple infrastructure providers (InPs) and MVNOs serving multiple users. Each InP has multiple access points as base stations (BSs) with specified spectrum and multi-beam antenna array in each transmitter that share its spectrum with users by employing NOMA. To realize this, we formulate a novel optimization problem at which the main aim is to maximize the revenue of MVNOs, subject to resource limitations and quality of service (QoS) constraints. Since our proposed optimization problem is non-convex, NP-hard and mathematically intractable, we transform it into a convex one by introducing a new optimization variable and converting the variables with adopting successive convex approximation. More importantly, the proposed solution is assessed and compared with the alternative search method and the optimal solution that is obtained with adopting the exhaustive search method. In addition, the proposed algorithm is studied from the computational complexity, convergence, and performance perspective. Our simulation results demonstrate that NOMA-3DBF has better performance and increases system throughput and revenue of MVNOs compared to orthogonal multiple access with 2DBF by approximately 64%. Especially, by exploiting 3DBF the revenue of MVNOs is improved by nearly 27% in contrast to 2DBF in a high order of antennas.
Polyphonic Sound Event and Sound Activity Detection: A Multi-Task Approach

Polyphonic Sound Event Detection (SED) in real-world recordings is a challenging task because of the dynamic polyphony level, intensity, and duration of sound events. Current polyphonic SED systems fail to model the temporal structure of sound events explicitly and instead attempt to look at which sound events are present at each audio frame. Consequently, the event-wise detection performance is much lower than the segment-wise detection performance. In this work, we propose a joint model approach to improve the temporal localization of sound events using a multi-task learning setup. The first task predicts which sound events are present at each time frame; we call this branch ‘Sound Event Detection (SED) model’, while the second task predicts if a sound event is present or not at each frame; we call this branch ‘Sound Activity Detection (SAD) model’. We verify the proposed joint model by comparing it with a separate implementation of both tasks aggregated together from individual task predictions. Our experiments on the URBAN-SED dataset show that the proposed joint model can alleviate False Positive (FP) and False Negative (FN) errors and improve both the segment-wise and the event-wise metrics.
Optimized Sharing of Coefficients in Parallel Filter Banks

Filters are the basic and most important blocks of most signal processing applications. In many applications, a group of parallel filters are used as filter banks. Parallel filter banks naturally require much more computations. Especially on chip applications, the resources are limited and shared among many algorithms. For this purpose, many filter optimization schemes are proposed to reduce the number of resources that filtering operations require. In this work, a novel optimization algorithm is proposed to decrease the number of operations in a group of parallel filters. The filter coefficients are grouped in a two stage process which enables increased coefficient sharing between different filters. The algorithm is capable of decreasing the number of registers, look-up tables and DSP48s by up to 50\% of a regular parallel filter bank, without requiring increased sampling rate.
The constructive Kan-Quillen model structure: two new proofs

We present two new proofs of Simon Henry's result that the category of simplicial sets admits a constructive counterpart of the classical Kan-Quillen model structure. Our proofs are entirely self-contained and avoid complex combinatorial arguments on anodyne extensions. We also give new constructive proofs of the left and right properness of the model structure.
Productization Challenges of Contextual Multi-Armed Bandits

Contextual Multi-Armed Bandits is a well-known and accepted online optimization algorithm, that is used in many Web experiences to tailor content or presentation to users' traffic. Much has been published on theoretical guarantees (e.g. regret bounds) of proposed algorithmic variants, but relatively little attention has been devoted to the challenges encountered while productizing contextual bandits schemes in large scale settings. This work enumerates several productization challenges we encountered while leveraging contextual bandits for two concrete use cases at scale. We discuss how to (1) determine the context (engineer the features) that model the bandit arms; (2) sanity check the health of the optimization process; (3) evaluate the process in an offline manner; (4) add potential actions (arms) on the fly to a running process; (5) subject the decision process to constraints; and (6) iteratively improve the online learning algorithm. For each such challenge, we explain the issue, provide our approach, and relate to prior art where applicable.
Photoemission from hybrid states of Cl@C60 before and after a stabilizing charge transfer

Photoionization calculations of the endofullerene molecule Cl@C60 with an open-shell chlorine atom are performed in the linear response density functional theory based on a spherical jellium model of C60. Cross sections for atom–fullerene hybrid photoemission studied show the effects of the hybridization symmetry, the giant plasmon and the molecular cavity. Comparisons with the results of Ar@C60 provide insights into the role of a shell-closing electron and its influence on the dynamics. The results for Cl@C60 are further compared with those of a more stable configuration that results after a C60 electron transfers to Cl forming Cl−@C60+. This comparison reveals noticeable differences in the ionization properties of the antibonding hybrid state while the bonding hybrid remains nearly unaltered showing a magnification covering the entire giant plasmon energy range. These predictions should raise interest in probing the molecular configuration using photoelectron spectroscopy.
Highly parallel algorithm for the Ising ground state searching problem

Finding an energy minimum in the Ising model is an exemplar objective, associated with many combinatorial optimization problems, that is computationally hard in general, but occurs in all areas of modern science. There are several numerical methods, providing solution for the medium size Ising spin systems. However, they are either computationally slow and badly parallelized, or do not give sufficiently good results for the large systems. In this paper, we present a highly parallel algorithm, called Mean-field Annealing from a Random State (MARS), incorporating the best features of the classical simulated annealing (SA) and Mean-Field Annealing (MFA) methods. The algorithm is based on the mean-field descent from a randomly selected configuration and temperature. Since a single run requires little computational effort, the effectiveness can be achieved by massive parallelisation. MARS shows excellent performance both on the large Ising spin systems and on the set of exemplary maximum cut benchmark instances in terms of both solution quality and computational time.
Bag-of-Audio-Words based on Autoencoder Codebook for Continuous Emotion Prediction

In this paper we present a novel approach for extracting a Bag-of-Words (BoW) representation based on a Neural Network codebook. The conventional BoW model is based on a dictionary (codebook) built from elementary representations which are selected randomly or by using a clustering algorithm on a training dataset. A metric is then used to assign unseen elementary representations to the closest dictionary entries in order to produce a histogram. In the proposed approach, an autoencoder (AE) encompasses the role of both the dictionary creation and the assignment metric. The dimension of the encoded layer of the AE corresponds to the size of the dictionary and the output of its neurons represents the assignment metric. Experimental results for the continuous emotion prediction task on the AVEC 2017 audio dataset have shown an improvement of the Concordance Correlation Coefficient (CCC) from 0.225 to 0.322 for arousal dimension and from 0.244 to 0.368 for valence dimension relative to the conventional BoW version implemented in a baseline system.
Approximate Message Passing for Indoor THz Channel Estimation

Compressed sensing (CS) deals with the problem of reconstructing a sparse vector from an under-determined set of observations. Approximate message passing (AMP) is a technique used in CS based on iterative thresholding and inspired by belief propagation in graphical models. Due to the high transmission rate and a high molecular absorption, spreading loss and reflection loss, the discrete-time channel impulse response (CIR) of a typical indoor THz channel is very long and exhibits an approximately sparse characteristic. In this paper, we develop AMP based channel estimation algorithms for indoor THz communications. The performance of these algorithms is compared to the state of the art. We apply AMP with soft- and hard-thresholding. Unlike the common applications in which AMP with hard-thresholding diverges, the properties of the THz channel favor this approach. It is shown that THz channel estimation via hard-thresholding AMP outperforms all previously proposed methods and approaches the oracle based performance closely.
A Targeted Acceleration and Compression Framework for Low bit Neural Networks

1 bit deep neural networks (DNNs), of which both the activations and weights are binarized , are attracting more and more attention due to their high computational efficiency and low memory requirement . However, the drawback of large accuracy dropping also restrict s its application. In this paper, we propose a novel Targeted Acceleration and Compression (TAC) framework to improve the performance of 1 bit deep neural networks W e consider that the acceleration and compression effects of binarizing fully connected layer s are not sufficient to compensate for the accuracy loss caused by it In the proposed framework, t he convolutional and fully connected layer are separated and optimized i ndividually . F or the convolutional layer s , both the activations and weights are binarized. For the fully connected layer s, the binarization operation is re placed by network pruning and low bit quantization. The proposed framework is implemented on the CIFAR 10, CIFAR 100 and ImageNet ( ILSVRC 12 ) datasets , and experimental results show that the proposed TAC can significantly improve the accuracy of 1 bit deep neural networks and outperforms the state of the art by more than 6 percentage points .
Decoding and Repair Schemes for Shift-XOR Regenerating Codes

Decoding and repair schemes are proposed for shift-exclusive-or (shift-XOR) product-matrix (PM) regenerating codes, which outperform the existing schemes in terms of both communication and computation costs. In particular, for the shift-XOR minimum bandwidth regenerating (MBR) codes, our decoding and repair schemes have the optimal transmission bandwidth and can be implemented in-place without extra storage space for intermediate XOR results. Technically, our schemes involve an in-place algorithm for solving a system of shift-XOR equations, called shift-XOR elimination, which does not have the bandwidth overhead generated by shift operations as in the previous zigzag algorithm and has lower computation complexities compared with the zigzag algorithm. The decoding and repair of shift-XOR MBR/MSR codes are decomposed into a sequence of systems of shift-XOR equations, and hence can be solved by a sequence of calls to the shift-XOR elimination. As the decompositions of the decoding and repair depend only on the PM construction, but not the specific shift and XOR operations, our decoding and repair schemes can be extended to other MBR/MSR codes using the PM construction. Due to its fundamental role, the shift-XOR elimination is of independent interest.
Anomalous relaxation and multiple timescales in the quantum 
XY
 model with boundary dissipation

The relaxation of many-body system is still a challenging problem that has not well been understood. In this work we exactly calculate the dynamics of the quantum $XY$ model with boundary dissipation, in which the density matrix in terms of Majorana operators can be decoupled into independent subspaces represented by different number of Majorana fermions. The relaxation is characterized by multiply time scales, and in the long-time limit it is determined by the single particle relaxation process in a typical time scale $T^*$. For the bulk bands, we find $T^* \propto N^3/\gamma n^2 $ in the weak dissipation limit; and $T^* \propto \gamma N^3/ n^2$ in the strong dissipation limit, where $N$ is the chain length, $\gamma$ is the dissipation rate and $n$ is the band index. For the edge modes $T^* \propto 1/\gamma$, indicating of most vulnerable to dissipation in the long chain limit. These results are counter-intuitive because it means any weak dissipation can induce relaxation, while strong dissipation can induce weak relaxation. We find that these two limits correspond to two different physics, which are explained based on the first and second-order perturbation theory in an equivalent non-Hermitian model.Furthermore, we show that even in the long chain limit the relaxation may exhibit strong odd-even effect. These results shade new insight into the dynamics of topological qubits in environment.