Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian parameter estimation for dynamical models in systems biology

Dynamical systems modeling, particularly via systems of ordinary differential equations, has been used to effectively capture the temporal behavior of different biochemical components in signal transduction networks. Despite the recent advances in experimental measurements, including sensor development and ‘-omics’ studies that have helped populate protein-protein interaction networks in great detail, systems biology modeling lacks systematic methods to estimate kinetic parameters and quantify associated uncertainties. This is because of multiple reasons, including sparse and noisy experimental measurements, lack of detailed molecular mechanisms underlying the reactions, and missing biochemical interactions. Additionally, the inherent nonlinearities with respect to the states and parameters associated with the system of differential equations further compound the challenges of parameter estimation. In this study, we propose a comprehensive framework for Bayesian parameter estimation and complete quantification of the effects of uncertainties in the data and models. We apply these methods to a series of signaling models of increasing mathematical complexity. Systematic analysis of these dynamical systems showed that parameter estimation depends on data sparsity, noise level, and model structure, including the existence of multiple steady states. These results highlight how focused uncertainty quantification can enrich systems biology modeling and enable additional quantitative analyses for parameter estimation.
LiDAR Road-Atlas: An Efficient Map Representation for General 3D Urban Environment

In this work, we propose the LiDAR Road-Atlas, a compact and efficient 3D map representation, for autonomous robot or vehicle navigation in a general urban environment. The LiDAR Road-Atlas can be generated by an online mapping framework which incrementally merges local 2D occupancy grid maps (2D-OGMs). Specifically, the contributions of our method are threefold. First, we solve the challenging problem of creating local 2D-OGMs in nonstructured urban scenes based on a real-time delimitation of traversable and curb regions in a LiDAR point cloud. Second, we achieve accurate 3D mapping in multiple-layer urban road scenarios by a probabilistic fusion scheme. Third, we achieve a very efficient 3D map representation of a general environment thanks to the automatic local-OGM-induced traversable-region labeling and a sparse probabilistic local point-cloud encoding. Given the LiDAR Road-Atlas, one can achieve accurate vehicle localization, path planning, and some other tasks. Our map representation is insensitive to dynamic objects which can be filtered out in the resulting map based on a probabilistic fusion. Empirically, we compare our map representation with a couple of popular map representations in robotics society, and our map representation is more favorable in terms of efficiency, scalability, and compactness. Additionally, we also evaluate localization performance given the LiDAR Road-Atlas representations on two public datasets. With a 16-channel LiDAR sensor, our method achieves an average global localization error of 0.26 m (translation) and 1.07 (rotation) on the Apollo dataset, and 0.89 m (translation) and 1.29 (rotation) on the MulRan dataset, respectively, at 10 Hz, which validates its promising performance. The code for this work is open-sourced at https://github.com/IMRL/Lidar-road-atlas.
A Machine Learning Approach to Determine the Semantic Versioning Type of npm Packages Releases

Semantic versioning policy is widely used to indicate the level of changes in a package release. Unfortunately, there are many cases where developers do not respect the semantic versioning policy, leading to the breakage of dependent applications. To reduce such cases, we proposed using machine learning (ML) techniques to effectively predict the new release type, i.e., patch, minor, major, in order to properly determine the semantic versioning type. To perform our prediction, we mined and used a number of features about a release, such as the complexity of the changed code, change types, and development activities. We then used four ML classifiers. To evaluate the performance of the proposed ML classifiers, we conducted an empirical study on 31 JavaScript packages containing a total of approximately 6,260 releases. We started by extracting 41 release level features from historical data of packages' source code and repositories. Then, we used four machine learning classifiers, namely XGBoost, Random Forest, Decision Tree, and Logistic Regression. We found that the XGBoost classifiers performed the best, achieving median ROC AUC values of 0.78, 0.69, and 0.74 for major, minor, and patch releases, respectively. We also found that features related to the change types in a release are the best predictors group of features in determining the semantic versioning type. Finally, we studied the generalizability of determining the semantic versioning type by applying cross-package validation. Our results showed that the general classifier achieved median ROC AUC values of 0.76, 0.69, and 0.75 for major, minor, and patch releases.
Overleaf Example

Robots will experience non-stationary environment dynamics throughout their lifetime: the robot dynamics can change due to wear and tear, or its surroundings may change over time. Eventually, the robots should perform well in all of the environment variations it has encountered. At the same time, it should still be able to learn fast in a new environment. We investigate two challenges in such a lifelong learning setting: first, existing off-policy algorithms struggle with the trade-off between being conservative to maintain good performance in the old environment and learning efficiently in the new environment. We propose the Offline Distillation Pipeline to break this trade-off by separating the training procedure into interleaved phases of online interaction and offline distillation. Second, training with the combined datasets from multiple environments across the lifetime might create a significant performance drop compared to training on the datasets individually. Our hypothesis is that both the imbalanced quality and size of the datasets exacerbate the extrapolation error of the Q-function during offline training over the “weaker” dataset. We propose a simple fix to the issue by keeping the policy closer to the dataset during the distillation phase. In the experiments, we demonstrate these challenges and the proposed solutions with a simulated bipedal robot walking task across various environment changes. We show that the Offline Distillation Pipeline achieves better performance across all the encountered environments without affecting data collection. We also provide a comprehensive empirical study to support our hypothesis on the data imbalance issue.
Speech Emotion Recognition with Global-Aware Fusion on Multi-Scale Feature Representation

Speech Emotion Recognition (SER) is a fundamental task to predict the emotion label from speech data. Recent works mostly focus on using convolutional neural networks (CNNs) to learn local attention map on fixed-scale feature representation by viewing time-varied spectral features as images. However, rich emotional feature at different scales and important global information are not able to be well captured due to the limits of existing CNNs for SER. In this paper, we propose a novel GLobal-Aware Multi-scale (GLAM) neural network1 to learn multi-scale feature representation with global-aware fusion module to attend emotional information. Specifically, GLAM iteratively utilizes multiple convolutional kernels with different scales to learn multiple feature representation. Then, instead of using attention-based methods, a simple but effective global-aware fusion module is applied to grab most important emotional information globally. Experiments on the benchmark corpus IEMOCAP over four emotions demonstrates the superiority of our proposed model with 2.5% to 4.5% improvements on four common metrics compared to previous state-of-the-art approaches.
Modelling and analysis of parallel-interlinked air suspension system based on a transfer characterisation

This paper focuses on the transferring characteristic and the matching method for interlinked air suspension. Firstly, a structure analysis of parallel air suspension system is given. According to gas thermodynamics and vehicle dynamics, the half vehicle model of the parallel-interlinked air suspension is built. Secondly, the nonlinear dynamic model is linearised by using Taylor series expansion. A comparison analysis of different suspension characteristics between the interlinked and non-interlinked air suspension is carried out. Finally, an experimental study is conducted to validate the simulation results. We found that parallel interlinked air suspension has little effect on the acceleration of the vehicle body during the straight-line driving condition. Most meaningfully, interlinked air suspension could significantly decrease the amplitude of roll angle, which is an important characteristic of the attitude stability. Moreover, the proposed interlinked air springs can reduce the dynamic load of unsprung mass, and improve the road friendliness of vehicles.
Effective temperature pulses in open quantum systems

Controlling the temperature of nano-scale quantum systems is becoming increasingly important in the efforts to develop thermal devices such as quantum heat valves, heat engines, and refrigerators, and to explore fundamental concepts in quantum thermodynamics. In practice, however, it is challenging to generate arbitrary time-dependent temperatures, similarly to what has been achieved for electronic voltage pulses. To overcome this problem, we here propose a fully quantum mechanical scheme to control the time-dependent environment temperature of an open quantum system. To this end, we consider a collection of quantum harmonic oscillators that mediate the interactions between the quantum system and a thermal reservoir, and we show how an effective time-dependent temperature can be realized by modulating the oscillator frequencies in time. By doing so, we can apply effective temperature pulses to the quantum system, and it can be cooled below the temperature of the environment. Surprisingly, the scheme can be realized using only a few oscillators, and our proposal thereby paves the way for controlling the temperature of open quantum systems.
Ultrasound Shear Wave Elasticity Imaging With Spatio-Temporal Deep Learning

Ultrasound shear wave elasticity imaging is a valuable tool for quantifying the elastic properties of tissue. Typically, the shear wave velocity is derived and mapped to an elasticity value, which neglects information such as the shape of the propagating shear wave or push sequence characteristics. We present 3D spatio-temporal CNNs for fast local elasticity estimation from ultrasound data. This approach is based on retrieving elastic properties from shear wave propagation within small local regions. A large training data set is acquired with a robot from homogeneous gelatin phantoms ranging from 17.42 kPa to 126.05 kPa with various push locations. The results show that our approach can estimate elastic properties on a pixelwise basis with a mean absolute error of 5.01(437) kPa. Furthermore, we estimate local elasticity independent of the push location and can even perform accurate estimates inside the push region. For phantoms with embedded inclusions, we report a 53.93% lower MAE (7.50 kPa) and on the background of 85.24% (1.64 kPa) compared to a conventional shear wave method. Overall, our method offers fast local estimations of elastic properties with small spatio-temporal window sizes.
Ground state preparation and energy estimation on early fault-tolerant quantum computers via quantum eigenvalue transformation of unitary matrices

Under suitable assumptions, the algorithms in [Lin, Tong, Quantum 2020] can estimate the ground state energy and prepare the ground state of a quantum Hamiltonian with near-optimal query complexities. However, this is based on a block encoding input model of the Hamiltonian, whose implementation is known to require a large resource overhead. We develop a tool called quantum eigenvalue transformation of unitary matrices with real polynomials (QET-U), which uses a controlled Hamiltonian evolution as the input model, a single ancilla qubit and no multi-qubit control operations, and is thus suitable for early fault-tolerant quantum devices. This leads to a simple quantum algorithm that outperforms all previous algorithms with a comparable circuit structure for estimating the ground state energy. For a class of quantum spin Hamiltonians, we propose a new method that exploits certain anti-commutation relations and further removes the need of implementing the controlled Hamiltonian evolution. Coupled with Trotter based approximation of the Hamiltonian evolution, the resulting algorithm can be very suitable for early fault-tolerant quantum devices. We demonstrate the performance of the algorithm using IBM Qiskit for the transverse field Ising model. If we are further allowed to use multi-qubit Toffoli gates, we can then implement amplitude amplification and a new binary amplitude estimation algorithm, which increases the circuit depth but decreases the total query complexity. The resulting algorithm saturates the near-optimal complexity for ground state preparation and energy estimating using a constant number of ancilla qubits (no more than 3).
pysamoo: Surrogate-Assisted Multi-Objective Optimization in Python

Significant effort has been made to solve computationally expensive optimization problems in the past two decades, and various optimization methods incorporating surrogates into optimization have been proposed. However, most optimization toolboxes do not consist of ready-to-run algorithms for computationally expensive problems, especially in combination with other key requirements, such as handling multiple conflicting objectives or constraints. Thus, the lack of appropriate software packages has become a bottleneck for solving real-world applications. The proposed framework, pysamoo, addresses these shortcomings of existing optimization frameworks and provides multiple optimization methods for handling problems involving time-consuming evaluation functions. The framework extends the functionalities of pymoo, a popular and comprehensive toolbox for multi-objective optimization, and incorporates surrogates to support expensive function evaluations. The framework is available under the GNU Affero General Public License (AGPL) and is primarily designed for research purposes. For more information about pysamoo, readers are encouraged to visit: anyoptimization.com/projects/pysamoo.
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

We apply preference modeling and reinforcement learning from human feedback (RLHF) to ﬁnetune language models to act as helpful and harmless assistants. We ﬁnd this alignment training improves performance on almost all NLP evaluations, and is fully compatible with training for specialized skills such as python coding and summarization. We explore an iterated online mode of training, where preference models and RL policies are updated on a weekly cadence with fresh human feedback data, efﬁciently improving our datasets and models. Finally, we investigate the robustness of RLHF training, and identify a roughly linear relation between the RL reward and the square root of the KL divergence between the policy and its initialization. Alongside our main results, we perform peripheral analyses on calibration, competing objectives, and the use of OOD detection, compare our models with human writers, and provide samples from our models using prompts appearing in recent related work. Figure These plots show that PM accuracy decreases as we focus exclusively on comparisons between pairs of samples with high score. We have normalized all preference models to have the same mean score on a held-out dataset so that they’re directly comparable, and then plotted accuracy for the comparisons where both samples have scores above a speciﬁc threshold.
Discovering material information using hierarchical Reformer model on financial regulatory filings

Most applications of machine learning for finance are related to forecasting tasks for investment decisions. Instead, we aim to promote a better understanding of financial markets with machine learning techniques. Leveraging the tremendous progress in deep learning models for natural language processing, we construct a hierarchical Reformer ([15]) model capable of processing a large document level dataset, SEDAR, from canadian financial regulatory filings. Using this model, we show that it is possible to predict trade volume changes using regulatory filings. We adapt the pretraining task of HiBERT ([36]) to obtain good sentence level representations using a large unlabelled document dataset. Finetuning the model to successfully predict trade volume changes indicates that the model captures a view from financial markets and processing regulatory filings is beneficial. Analyzing the attention patterns of our model reveals that it is able to detect some indications of material information without explicit training, which is highly relevant for investors and also for the market surveillance mandate of financial regulators.
Idiomify - Building a Collocation-supplemented Reverse Dictionary of English Idioms with Word2Vec for non-native learners

The aim of idiomify is to build a collocation-supplemented reverse dictionary of idioms for the non-native learners of English. We aim to do so because the reverse dictionary could help the non-natives explore idioms on demand, and the collocations could also guide them on using idioms more adequately. The cornerstone of the project is a reliable way of mining idioms from corpora, which is however a challenge because idioms extensively vary in forms. We tackle this by automatically deriving matching rules from their base forms. We use Point-wise Mutual Inclusion (PMI), Term Frequency - Inverse Document Frequency (TF-IDF) to model collocations, since both of them are popular metric for pairwise significance. We also try Term Frequency (TF) as the baseline model. As for implementing the reverse-dictionary, three approaches could be taken: inverted index, graphs and distributional semantics. We choose to take the last approach and implement the reverse dictionary with Word2Vec, because it is the most flexible approach of all and Word2Vec is a simple yet strong baseline. Evaluating the methods has revealed rooms for improvement. We learn that we can better identify idioms with the help of slop, wildcard and reordering techniques. We also learn that we can get the best of both PMI and TF-IDF if we use machine learning to find the sweet spot. Lastly, We learn that Idiomify could be further improved with a mixture of inverted index and distributional semantics approach. The limits aside, the proposed methods are feasible, and their benefits to the non-natives are apparent, which therefore can be used to aid the non-natives in acquiring English idioms.
Quantum Entangled States of a Classically Radiating Macroscopic Spin

Entanglement constitutes a main feature that distinguishes quantum from classical physics and is a key resource of quantum technologies. Here we show, however, that entanglement may also serve as the essential ingredient for the emergence of classical behavior in a composite nonlinear radiating system. We consider the radiation from a macroscopic spin emitter, such as the collective radiation from an atomic ensemble. We introduce a new class of macroscopic spin states, the coherently radiating spin states (CRSS), defined as the asymptotic eigenstates of the SU(2) lowering operator. We find that a spin emitter in a CRSS radiates classical-like coherent light, although the CRSS itself is a quantum entangled state exhibiting spin squeezing. We further show that CRSS are naturally produced in Dicke superradiance and underlie the dissipative Dicke phase transition. Our CRSS theory thus provides new concepts for studying the quantum physics of radiation, with applications in current platforms involving collections of atoms or spins, their consideration in quantum technologies such as metrology and lasing, and the many-body theory of spin systems.
Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection

We propose the Malceiver, a hierarchical Perceiver model for Android malware detection that makes use of multi-modal features. The primary inputs are the opcode sequence and the requested permissions of a given Android APK file. To reach a malware classification decision the model combines hierarchical features extracted from the opcode sequence together with the requested permissions. The model's architecture is based on the Perceiver/PerceiverIO which allows for very long opcode sequences to be processed efficiently. Our proposed model can be easily extended to use multi-modal features. We show experimentally that this model outperforms a conventional CNN architecture for opcode sequence based malware detection. We then show that using additional modalities improves performance. Our proposed architecture opens new avenues for the use of Transformer-style networks in malware research.
Accelerated analysis of low-level injection operation for transistor-based oscillating amplifiers

The problem of the accelerated analysis of oscillating amplifiers is addressed. To achieve both computational efficiency and adequate accuracy also in case of transmission-type, transistor-based, circuit structures, a novel approach is proposed which makes use of a general reduced-order model of the injected oscillator. A perturbation-refined analysis method is thus applied, which permits to derive the first-order exact set of differential equations that describes the system behavior in the fundamental-frequency complex-envelope domain. As illustrated by the RF Meissner driven oscillator example presented, the devised approach achieves the stated goals, and lends itself as a convenient, design-oriented, tool for the fast analysis of steady-state, locking-stability and transient response of this class of nonlinear dynamical circuits.
GPU Parallel algorithm for the generation of polygonal meshes based on terminal-edge regions

This paper presents a GPU parallel algorithm to generate a new kind of polygonal meshes obtained from Delaunay triangulations. To generate the polygonal mesh, the algorithm first uses a classification system to label each edge of an input triangulation; second it builds polygons (simple or not) from terminal-edge regions using the label system, and third it transforms each non-simple polygon from the previous phase into simple ones, convex or not convex polygons. We show some preliminary experiments to test the scalability of the algorithm and compare it with the sequential version. We also run a very simple test to show that these meshes can be useful for the virtual element method.
A format for a plagiarism-proof online examination for calculus and linear algebra using Microsoft Excel

ABSTRACT As educational systems move from onsite to online due to the COVID-19 pandemic, teachers face the difficulty of designing online examination formats which minimize opportunities for dishonesty. In this paper, we expose our design of such a format: a protected Microsoft Excel spreadsheet containing short-answer questions, which was implemented in a calculus module taught by us. This format allows examiners to randomize questions with the aim that each student receives each question with different numerical details, making plagiarism impossible, while keeping the marking effort very low.