Miracle-dz/UniFAR-FTUs huggingface dataset:
in Computer Science and Software Engineering

— Current research in affective computing shows that agents are more effective if they have emotions. Emotional agents are able to simulate and express emotions as well as make decisions to achieve intended goal based on emotional status. This undergraduate research project focuses on designing simulated emotional agents in a game engine. Self-efficacy has been proven to be one of the determining factors in students’ academic achievements. In this research we model agents’ characteristics or behaviors that promote students’ interest in education by improving their science self-efficacy. The Blender Game Engine is used to design, test and evaluate the designed agents and strategies that can aid students in improving their perceived self-efficacy in the Science Technology Engineering and Mathematics (STEM) area. The educational game scenario is based on existing study and theory of self-efficacy. We intend to use this research as a new approach to improve students’ science self-efficacy and to promote the use of emotional intelligent agents for education purpose.
Sensor Data Fusion in Top-View Grid Maps using Evidential Reasoning with Advanced Conflict Resolution

We present a new method to combine evidential top-view grid maps estimated based on heterogeneous sensor sources. Dempster's combination rule that is usually applied in this context provides undesired results with highly conflicting inputs. Therefore, we use more advanced evidential reasoning techniques and improve the conflict resolution by modeling the reliability of the evidence sources. We propose a data-driven reliability estimation to optimize the fusion quality using the Kitti-360 dataset. We apply the proposed method to the fusion of LiDAR and stereo camera data and evaluate the results qualitatively and quantitatively. The results demonstrate that our proposed method robustly combines measurements from heterogeneous sensors and successfully resolves sensor conflicts.
Veronese subalgebras and Veronese morphisms for a class of Yang-Baxter algebras

We study $d$-Veronese subalgebras $A^{(d)}$ of Yang-Baxter algebras $A_X= A(K, X, r)$ related to finite nondegenerate involutive set-theoretic solutions $(X, r)$ of the Yang-Baxter equation, where $K$ is a field and $d\geq 2$ is an integer. We find an explicit presentation of the $d$-Veronese $A^{(d)}$ in terms of one-generators and quadratic relations. We introduce the notion of a $d$-Veronese solution $(Y, r_Y)$, canonically associated to $(X,r)$ and use its Yang-Baxter algebra $A_Y= A(K, Y, r_Y)$ to define a Veronese morphism $v_{n,d}:A_Y \rightarrow A_X $. We prove that the image of $v_{n,d}$ is the $d$-Veronese subalgebra $A^{(d)}$, and find explicitly a minimal set of generators for its kernel. The results agree with their classical analogues in the commutative case. We show that the Yang-Baxter algebra $A(K, X, r)$ is a PBW algebra if and only if $(X,r)$ is a square-free solution. In this case the $d$-Veronese $A^{(d)}$ is also a PBW algebra.
INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL

Model-based reinforcement learning (RL) algorithms designed for handling complex visual observations typically learn some sort of latent state representation, either explicitly or implicitly. Standard methods of this sort do not distinguish between functionally relevant aspects of the state and irrelevant distractors, instead aiming to represent all available information equally. We propose a modified objective for model-based RL that, in combination with mutual information maximization, allows us to learn representations and dynamics for visual model-based RL without reconstruction in a way that explicitly prioritizes functionally relevant factors. The key principle behind our design is to integrate a term inspired by variational empowerment into a state-space model based on mutual information. This term prioritizes information that is correlated with action, thus ensuring that functionally relevant factors are captured first. Furthermore, the same empowerment term also promotes faster exploration during the RL process, especially for sparse-reward tasks where the reward signal is insufficient to drive exploration in the early stages of learning. We evaluate the approach on a suite of vision-based robot control tasks with natural video backgrounds, and show that the proposed prioritized information objective outperforms state-of-the-art model based RL approaches with higher sample efficiency and episodic returns. https://sites.google. com/view/information-empowerment
Luminous Supernovae: Unveiling a Population between Superluminous and Normal Core-collapse Supernovae

Stripped-envelope core-collapse supernovae can be divided into two broad classes: the common Type Ib/c supernovae (SNe Ib/c), powered by the radioactive decay of 56Ni, and the rare superluminous supernovae (SLSNe), most likely powered by the spin-down of a magnetar central engine. Up to now, the intermediate regime between these two populations has remained mostly unexplored. Here, we present a comprehensive study of 40 luminous supernovae (LSNe), SNe with peak magnitudes of M r = −19 to −20 mag, bound by SLSNe on the bright end and by SNe Ib/c on the dim end. Spectroscopically, LSNe appear to form a continuum between Type Ic SNe and SLSNe. Given their intermediate nature, we model the light curves of all LSNe using a combined magnetar plus radioactive decay model and find that they are indeed intermediate, not only in terms of their peak luminosity and spectra, but also in their rise times, power sources, and physical parameters. We subclassify LSNe into distinct groups that are either as fast evolving as SNe Ib/c or as slow evolving as SLSNe, and appear to be either radioactively or magnetar powered, respectively. Our findings indicate that LSNe are powered by either an overabundant production of 56Ni or by weak magnetar engines, and may serve as the missing link between the two populations.
Pre-training Protein Models with Molecular Dynamics Simulations for Drug Binding

The latest biological ﬁndings discover that the motionless ’lock-and-key’ theory is no longer applicable and the ﬂexibility of both the receptor and ligand plays a signiﬁcant role in helping understand the principles of the binding afﬁnity prediction. Based on this mechanism, molecular dynamics (MD) simulations have been invented as a useful tool to investigate the dynamical properties of this molecular system. However, the computational expenditure prohibits the growth of reported protein trajectories. To address this insufﬁciency, we present a novel spatial-temporal pre-training protocol, PretrainMD, to grant the protein encoder the capacity to capture the time-dependent geometric mobility along MD trajectories. Speciﬁcally, we introduce two sorts of self-supervised learning tasks: an atom-level denoising generative task and a protein-level snapshot ordering task. We validate the effectiveness of PretrainMD through the PDBbind dataset for both linear-probing and ﬁne-tuning. Extensive experiments show that our PretrainMD exceeds most state-of-the-art methods and achieves comparable performance. More importantly, through visualization we discover that the learned representations by pre-training on MD trajectories without any label from the downstream task follow similar patterns of the magnitude of binding afﬁnities. This strongly aligns with the fact that the motion of the interactions of protein and ligand maintains the key information of their binding. Our work provides a promising perspective of self-supervised pre-training for protein representations with very ﬁne temporal
Neutron Star Radii, Deformabilities, and Moments of Inertia from Experimental and Ab Initio Theory Constraints of the 208Pb Neutron Skin Thickness

Recent experimental and ab initio theory investigations of the 208Pb neutron skin thickness have the potential to inform the neutron star equation of state. In particular, the strong correlation between the 208Pb neutron skin thickness and the pressure of neutron matter at normal nuclear densities leads to modified predictions for the radii, tidal deformabilities, and moments of inertia of typical 1.4M⊙ neutron stars. In the present work, we study the relative impact of these recent analyses of the 208Pb neutron skin thickness on bulk properties of neutron stars within a Bayesian statistical analysis. Two models for the equation of state prior are employed in order to highlight the role of the highly uncertain high-density equation of state. From our combined Bayesian analysis of nuclear theory, nuclear experiment, and observational constraints on the dense matter equation of state, we find at the 90% credibility level R1.4=12.36−0.73+0.38 km for the radius of a 1.4M⊙ neutron star, R2.0=11.96−0.71+0.94 km for the radius of a 2.0M⊙ neutron star, Λ1. 4=440−144+103 for the tidal deformability of a 1.4M⊙ neutron star, and I1.338=1.425−0.146+0.074×1045gcm2 for the moment of inertia of PSR J0737-3039A whose mass is 1.338M⊙.
Topology and geometry of data manifold in deep learning

Despite significant advances in the field of deep learning in applications to various fields, explaining the inner processes of deep learning models remains an important and open question. The purpose of this article is to describe and substantiate the geometric and topological view of the learning process of neural networks. Our attention is focused on the internal representation of neural networks and on the dynamics of changes in the topology and geometry of the data manifold on different layers. We also propose a method for assessing the generalizing ability of neural networks based on topological descriptors. In this paper, we use the concepts of topological data analysis and intrinsic dimension, and we present a wide range of experiments on different datasets and different configurations of convolutional neural network architectures. In addition, we consider the issue of the geometry of adversarial attacks in the classification task and spoofing attacks on face recognition systems. Our work is a contribution to the development of an important area of explainable and interpretable AI through the example of computer vision.
Semiclassical energy density of kinks and solitons

We compute semiclassical corrections to the energy density of kinks in $\phi^4$ theory and of solitons in the sine-Gordon model in $1+1$ dimensions, using local and covariant renormalization techniques from quantum field theory in curved spacetimes. For the semiclassical correction to the energy, we recover the known results. Our analysis highlights a subtlety in the definition of a conserved stress tensor for scalar field theories in $1+1$ dimension.
Rendering Nighttime Image Via Cascaded Color and Brightness Compensation

Image signal processing (ISP) is crucial for camera imaging, and neural networks (NN) solutions are extensively deployed for daytime scenes. The lack of sufficient nighttime image dataset and insights on nighttime illumination characteristics poses a great challenge for high-quality rendering using existing NN ISPs. To tackle it, we first built a high-resolution nighttime RAW-RGB (NR2R) dataset with white balance and tone mapping annotated by expert professionals. Meanwhile, to best capture the characteristics of nighttime illumination light sources, we develop the CBUnet, a two-stage NN ISP to cascade the compensation of color and brightness attributes. Experiments show that our method has better visual quality compared to traditional ISP pipeline, and is ranked at the second place in the NTIRE 2022 Night Photography Rendering Challenge [13] for two tracks by respective People’s and Professional Photographer’s choices. The code and relevant materials are avaiable on our website 1.
A review of the use of optimal transport distances for high resolution seismic imaging based on the full waveform

the vertical direction. It generates important time-delays in the waveform, which in turn prevents least-squares FWI from converging towards a correct estimation of the subsurface velocity due to cycle-skipping. We show how the use of the graph-space optimal transport strategy can help mitigate this eﬀect.
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages

We present the MASSIVE dataset–Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation. MASSIVE contains 1M realistic, parallel, labeled virtual assistant utterances spanning 51 languages, 18 domains, 60 intents, and 55 slots. MASSIVE was created by tasking professional translators to localize the English-only SLURP dataset into 50 typologically diverse languages from 29 genera. We also present modeling results on XLM-R and mT5, including exact match accuracy, intent classification accuracy, and slot-filling F1 score. We have released our dataset, modeling code, and models publicly.
Expert-Calibrated Learning for Online Optimization with Switching Costs

We study online convex optimization with switching costs, a practically important but also extremely challenging problem due to the lack of complete offline information. By tapping into the power of machine learning (ML) based optimizers, ML-augmented online algorithms (also referred to as expert calibration in this paper) have been emerging as state of the art, with provable worst-case performance guarantees. Nonetheless, by using the standard practice of training an ML model as a standalone optimizer and plugging it into an ML-augmented algorithm, the average cost performance can be highly unsatisfactory. In order to address the "how to learn" challenge, we propose EC-L2O (expert-calibrated learning to optimize), which trains an ML-based optimizer by explicitly taking into account the downstream expert calibrator. To accomplish this, we propose a new differentiable expert calibrator that generalizes regularized online balanced descent and offers a provably better competitive ratio than pure ML predictions when the prediction error is large. For training, our loss function is a weighted sum of two different losses --- one minimizing the average ML prediction error for better robustness, and the other one minimizing the post-calibration average cost. We also provide theoretical analysis for EC-L2O, highlighting that expert calibration can be even beneficial for the average cost performance and that the high-percentile tail ratio of the cost achieved by EC-L2O to that of the offline optimal oracle (i.e., tail cost ratio) can be bounded. Finally, we test EC-L2O by running simulations for sustainable datacenter demand response. Our results demonstrate that EC-L2O can empirically achieve a lower average cost as well as a lower competitive ratio than the existing baseline algorithms.
Visualizing the turbulent energy cascade: Fourier and physical space

The transfer of kinetic energy from large to small scales is a hallmark of turbulent ﬂuids. While it is generally well understood in a statistical sense, a mechanistic understanding of the exact processes is still lacking. In this manuscript, we ﬁlter the velocity ﬁeld in bands of wavenumbers distributed logarithmically to investigate the transfer of energy in Fourier space, an approach which also provides a way of visualizing the energy cascade in real space. We use this method to study how energy is transferred to small scales in diﬀerent ﬂow conﬁgurations. In the case of a statistically steady homogeneous isotropic turbulent ﬂow at moderate Reynolds numbers, we demonstrate that the transfer between bands of wavenumbers is well described by a phenomenological model originally proposed by Tennekes & Lumley (1972). We then apply this method to investigate the formation of small scales during the interaction between two vortex tubes and show that the transfer of energy in these problems correlates with the dynamics of vorticity formation at ever decreasing scales. This enticing correlation between vortices and energy transfer, however, does not fully capture the transfer of energy in a structureless homogeneous isotropic turbulent ﬂow, where strain also plays an important role.
Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation

For autonomous quadruped robot navigation in various complex environments, a typical SOTA system is composed of four main modules -- mapper, global planner, local planner, and command-tracking controller -- in a hierarchical manner. In this paper, we build a robust and safe local planner which is designed to generate a velocity plan to track a coarsely planned path from the global planner. Previous works used waypoint-based methods (e.g. Proportional-Differential control and pure pursuit) which simplify the path tracking problem to local point-goal navigation. However, they suffer from frequent collisions in geometrically complex and narrow environments because of two reasons; the global planner uses a coarse and inaccurate model and the local planner is unable to track the global plan sufficiently well. Currently, deep learning methods are an appealing alternative because they can learn safety and path feasibility from experience more accurately. However, existing deep learning methods are not capable of planning for a long horizon. In this work, we propose a learning-based fully autonomous navigation framework composed of three innovative elements: a learned forward dynamics model (FDM), an online sampling-based model-predictive controller, and an informed trajectory sampler (ITS). Using our framework, a quadruped robot can autonomously navigate in various complex environments without a collision and generate a smoother command plan compared to the baseline method. Furthermore, our method can reactively handle unexpected obstacles on the planned path and avoid them. Project page https://awesomericky.github. io/projects/FDM_ITS_navigation/.
Cross-Lingual Phrase Retrieval

Cross-lingual retrieval aims to retrieve relevant text across languages. Current methods typically achieve cross-lingual retrieval by learning language-agnostic text representations in word or sentence level. However, how to learn phrase representations for cross-lingual phrase retrieval is still an open problem. In this paper, we propose , a cross-lingual phrase retriever that extracts phrase representations from unlabeled example sentences. Moreover, we create a large-scale cross-lingual phrase retrieval dataset, which contains 65K bilingual phrase pairs and 4.2M example sentences in 8 English-centric language pairs. Experimental results show that outperforms state-of-the-art baselines which utilize word-level or sentence-level representations. also shows impressive zero-shot transferability that enables the model to perform retrieval in an unseen language pair during training. Our dataset, code, and trained models are publicly available at github.com/cwszz/XPR/.
Massively Parallel Computation and Sublinear-Time Algorithms for Embedded Planar Graphs

While algorithms for planar graphs have received a lot of attention, few papers have focused on the additional power that one gets from assuming an embedding of the graph is available. While in the classic sequential setting, this assumption gives no additional power (as a planar graph can be embedded in linear time), we show that this is far from being the case in other settings. We assume that the embedding is straight-line, but our methods also generalize to non-straight-line embeddings. Speciﬁcally, we focus on sublinear-time computation and massively parallel computation (MPC). Our main technical contribution is a sublinear-time algorithm for computing a relaxed version of an r -division. We then show how this can be used to estimate Lipschitz additive graph parameters. This includes, for example, the maximum matching, maximum independent set, or the minimum dominating set. We also show how this can be used to solve some property testing problems with respect to the vertex edit distance. In the second part of our paper, we show an MPC algorithm that computes an r -division of the input graph. We show how this can be used to solve various classical graph problems with space per machine of O ( n 2 / 3+ (cid:15) ) for some (cid:15) > 0, and while performing O (1) rounds. One needs Ω(log n ) rounds with n 1 − Ω(1) space per machine without the embedding to solve these problems, assuming the 1-vs-2-cycles conjecture. Among the problems solved by our approach are: counting connected components, bipartition, minimum spanning tree problem, O (1)-approximate shortest paths, O (1)-approximate diameter/radius. Our techniques however also apply to other problems. In conjunction with existing algorithms for computing the Delaunay triangulation, our results imply an MPC algorithm for Euclidean minimum spanning three that uses O ( n 2 / 3+ (cid:15) ) space per machine and performs O (
Reducing the Depth of Quantum FLT-Based Inversion Circuit

Works on quantum computing and cryptanalysis has increased significantly in the past few years. Various constructions of quantum arithmetic circuits, as one of the essential components in the field, has also been proposed. However, there has only been a few studies on finite field inversion despite its essential use in realizing quantum algorithms, such as in Shor's algorithm for Elliptic Curve Discrete Logarith Problem (ECDLP). In this study, we propose to reduce the depth of the existing quantum Fermat's Little Theorem (FLT)-based inversion circuit for binary finite field. In particular, we propose follow a complete waterfall approach to translate the Itoh-Tsujii's variant of FLT to the corresponding quantum circuit and remove the inverse squaring operations employed in the previous work by Banegas et al., lowering the number of CNOT gates (CNOT count), which contributes to reduced overall depth and gate count. Furthermore, compare the cost by firstly constructing our method and previous work's in Qiskit quantum computer simulator and perform the resource analysis. Our approach can serve as an alternative for a time-efficient implementation.
On The Cross-Modal Transfer from Natural Language to Code through Adapter Modules

Pre-trained neural Language Models (PTLM), such as CodeBERT, are recently used in software engineering as models pre-trained on large source code corpora. Their knowledge is transferred to downstream tasks (e.g. code clone detection) via fine-tuning. In natural language processing (NLP), other alternatives for transferring the knowledge of PTLMs are explored through using adapters, compact, parameter efficient modules inserted in the layers of the PTLM. Although adapters are known to facilitate adapting to many downstream tasks compared to fine-tuning the model that require retraining all of the models' parameters- which owes to the adapters' plug and play nature and being parameter efficient-their usage in software engineering is not explored. Here, we explore the knowledge transfer using adapters and based on the Naturalness Hypothesis proposed by Hindle et. al [12]. Thus, studying the bimodality of adapters for two tasks of cloze test and code clone detection, compared to their benchmarks from the CodeXGLUE platform. These adapters are trained using programming languages and are inserted in a PTLM that is pre-trained on English corpora (N-PTLM). Three programming languages, $\mathrm{C}/\mathrm{C}++$, Python, and Java, are studied along with extensive experiments on the best setup used for adapters. Improving the results of the N-PTLM confirms the success of the adapters in knowledge transfer to software engineering, which sometimes are in par with or exceed the results of a PTLM trained on source code; while being more efficient in terms of the number of parameters, memory usage, and inference time. Our results can open new directions to build smaller models for more software engineering tasks. We open source all the scripts and the trained adapters.