Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing Students’ Solutions to an Open-ended Problem in an Introductory Programming Course with and without Explicit Modeling Interventions

Engineers must understand how to build, apply, and adapt various types of models in order to be successful. Throughout undergraduate engineering education, modeling is fundamental for many core concepts, though it is rarely explicitly taught. There are many benefits to explicitly teaching modeling, particularly in the first years of an engineering program. The research questions that drove this study are: (1) How do students’ solutions to a complex, open-ended problem (both written and coded solutions) develop over the course of multiple submissions? and (2) How do these developments compare across groups of students that did and did not participate in a course centered around modeling? Students’ solutions to an open-ended problem across multiple sections of an introductory programming course were explored. These sections were all divided across two groups: (1) experimental group - these sections discussed and utilized mathematical and computational models explicitly throughout the course, and (2) comparison group - these sections focused on developing algorithms and writing code with a more traditional approach. All sections required students to complete a common open-ended problem that consisted of two versions of the problem (the first version with smaller data set and the other a larger data set). Each version had two submissions – (1) a mathematical model or algorithm (i.e. students’ written solution potentially with tables and figures) and (2) a computational model or program (i.e. students’ MATLAB code). The students’ solutions were graded by student graders after completing two required training sessions that consisted of assessing multiple sample student solutions using the rubrics to ensure consistency across grading. The resulting assessments of students’ works based on the rubrics were analyzed to identify patterns students’ submissions and comparisons across sections. The results identified differences existing in the mathematical and computational model development between students from the experimental and comparison groups. The students in the experimental group were able to better address the complexity of the problem. Most groups demonstrated similar levels and types of change across the submissions for the other dimensions related to the purpose of model components, addressing the users’ anticipated needs, and communicating their solutions. These findings help inform other researchers and instructors how to help students develop mathematical and computational modeling skills, especially in a programming course. This work is part of a larger NSF study about the impact of varying levels of modeling interventions related to different types of models on students’ awareness of different types of models and their applications, as well as their ability to apply and develop different types of models
Determination of Λ MS at ﬁve loops from holographic QCD

The recent determination of the β –function of the QCD running coupling α MS ( Q 2 ) at 5-loops, together with improvements in determining the holographic QCD nonperturbative scale parameter κ , allows a high accuracy computation of the perturbative QCD scale parameter Λ MS . We ﬁnd Λ (3) MS = 0 . 339 ± 0 . 019 GeV for n f = 3, in excellent agreement with the world average, Λ (3) MS = 0 . 332 ± 0 . 019 GeV. The convergence of the method used for this determination is discussed.
An original model for multi-target learning of logical rules for knowledge graph reasoning

Large-scale knowledge graphs provide structured representations of human knowledge. How- ever, as it is impossible to collect all knowledge, knowledge graphs are usually incomplete. Reasoning based on existing facts paves a way to discover missing facts. In this paper, we study the problem of learning logical rules for reasoning on knowledge graphs for completing missing factual triplets. Learning logical rules equips a model with strong interpretability as well as the ability to generalize to similar tasks. We propose a model able to fully use training data which also considers multi-target scenarios. In addition, considering the deﬁciency in evaluating the performance of models and the quality of mined rules, we further propose two novel indicators to helpwiththeproblem. Experimentalresultsempiricallydemonstratethatourmodeloutperforms state-of-the-art methods on ﬁve benchmark datasets. The results also prove the eﬀectiveness of the indicators.
Service Mining Architecture for Web-based Services using Agents

current IT scenario, web service is the fast emerging technology for integrating disparate IT systems and assets. Both academic and industrial organization in business process environment faced a thorny problem, to flexibly modify or update existing components and to connect heterogeneous application to meet the specific requirements of business. There are number of services published and available as runtime usable services in Internet. Lack of semantic base in search mechanisms of UDDI make it difficult for users to find a required web services. In this paper, we are extending web mining concept to service mining having knowledge of service deployed in the registry to automatically discover an efficient service which meets users specific requirements. Agents are used to discover an accurate service which meets specific requirements of user more efficiently using agents than the existing methods in service mining. This work also discussed about the quality attributes with organized set of design related questions which helps an evaluator to analyze the ability of the architecture to meet quality requirements, and provides a brief sample evaluation. ATAM method of software architecture evaluation is used to evaluate the proposed model. The assessment justifies the proposal in terms of the performance attributes such as reliability, availability, modifiability, security and interoperability etc.
Classification of Bark Beetle-Induced Forest Tree Mortality using Deep Learning

Bark beetle outbreaks can dramatically impact forest ecosystems and services around the world. For the development of effective forest policies and management plans, the early detection of infested trees is essential. Despite the visual symptoms of bark beetle infestation, this task remains challenging, considering overlapping tree crowns and non-homogeneity in crown foliage discolouration. In this work, a deep learning based method is proposed to effectively classify different stages of bark beetle attacks at the individual tree level. The proposed method uses RetinaNet architecture (exploiting a robust feature extraction backbone pre-trained for tree crown detection) to train a shallow subnetwork for classifying the different attack stages of images captured by unmanned aerial vehicles (UAVs). Moreover, various data augmentation strategies are examined to address the class imbalance problem, and consequently, the affine transformation is selected to be the most effective one for this purpose. Experimental evaluations demonstrate the effectiveness of the proposed method by achieving an average accuracy of 98.95%, considerably outperforming the baseline method by approximately 10%.
Search for a dark leptophilic scalar produced in association with 
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A dark leptophilic scalar $(\phi_L)$ is a hypothetical particle that couples only to leptons rather than quarks. We report on a search for $\phi_L$ in the $e^+e^- \to \tau^+ \tau^- \phi_L, ~\phi_L \to \ell^+ \ell^- ~(\ell = e, \mu)$ process using 626~\invfb of data collected by the Belle experiment near the \Y4S resonance. We validate the backgrounds with multiple control regions in data, using a novel multiclass multivariate event classifier. In absence of a signal, we quote upper limits at the 90\% confidence level on the coupling between $\phi_L$ and leptons. Our bounds, obtained in a blinded approach, are 19\% more constraining than the previous limits, averaged over the mass range $0.04 \leq m_{\phi_L} \leq 6.5~\gev$. We exclude the parameter space below 4~\gev favored by measurement of the anomalous magnetic moment of the muon.
Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions

We present a method for finding optimal hedging policies for arbitrary initial portfolios and market states. We develop a novel actor-critic algorithm for solving general risk-averse stochastic control problems and use it to learn hedging strategies across multiple risk aversion levels simultaneously. We demonstrate the effectiveness of the approach with a numerical example in a stochastic volatility environment.
Action-Specific Perception & Performance on a Fitts's Law Task in Virtual Reality: The Role of Haptic Feedback

While user's perception and performance are predominantly examined independently in virtual reality, the Action-Specific Perception (ASP) theory postulates that the performance of an individual on a task modulates this individual's spatial and time perception pertinent to the task's components and procedures. This paper examines the association between performance and perception and the potential effects that tactile feedback modalities could generate. This paper reports a user study (N=24), in which participants performed a standardized Fitts's law target acquisition task by using three feedback modalities: visual, visuo-electrotactile, and visuo-vibrotactile. The users completed 3 Target Sizes × 2 Distances × 3 feedback modalities = 18 trials. The size perception, distance perception, and (movement) time perception were assessed at the end of each trial. Performance-wise, the results showed that electrotactile feedback facilitates a significantly better accuracy compared to vibrotactile and visual feedback, while vibrotactile provided the worst accuracy. Electrotactile and visual feedback enabled a comparable reaction time, while the vibrotactile offered a substantially slower reaction time than visual feedback. Although amongst feedback types the pattern of differences in perceptual aspects were comparable to performance differences, none of them was statistically significant. However, performance indeed modulated perception. Significant action-specific effects on spatial and time perception were detected. Changes in accuracy modulate both size perception and time perception, while changes in movement speed modulate distance perception. Also, the index of difficulty was found to modulate all three perceptual aspects. However, individual differences appear to affect the magnitude of action-specific effects. These outcomes highlighted the importance of haptic feedback on performance, and importantly the significance of action-specific effects on spatial and time perception in VR, which should be considered in future VR studies.
Approximately Hadamard matrices and Riesz bases in random frames

An $n \times n$ matrix with $\pm 1$ entries that acts on ${\mathbb {R}}^{n}$ as a scaled isometry is called Hadamard. Such matrices exist in some, but not all dimensions. Combining number-theoretic and probabilistic tools, we construct matrices with $\pm 1$ entries that act as approximate scaled isometries in ${\mathbb {R}}^{n}$ for all $n \in {\mathbb {N}}$. More precisely, the matrices we construct have condition numbers bounded by a constant independent of $n$. Using this construction, we establish a phase transition for the probability that a random frame contains a Riesz basis. Namely, we show that a random frame in ${\mathbb {R}}^{n}$ formed by $N$ vectors with independent identically distributed coordinate having a nondegenerate symmetric distribution contains many Riesz bases with high probability provided that $N \ge \exp (Cn)$. On the other hand, we prove that if the entries are sub-Gaussian, then a random frame fails to contain a Riesz basis with probability close to $1$ whenever $N \le \exp (cn)$, where $c<C$ are constants depending on the distribution of the entries.
Z-polyregular functions

This paper introduces a robust class of functions from finite words to integers that we call Z-polyregular functions. We show that it admits natural characterizations in terms of logics, Z-rational expressions, Z-rational series and transducers. We then study two subclass membership problems. First, we show that the asymptotic growth rate of a function is computable, and corresponds to the minimal number of variables required to represent it using logical formulas. Second, we show that first-order definability of Z-polyregular functions is decidable. To show the latter, we introduce an original notion of residual transducer, and provide a semantic characterization based on aperiodicity.
PodcastMix: A dataset for separating music and speech in podcasts

We introduce PodcastMix, a dataset formalizing the task of separating background music and foreground speech in podcasts. We aim at deﬁning a benchmark suitable for training and eval-uating (deep learning) source separation models. To that end, we release a large and diverse training dataset based on programatically generated podcasts. However, current (deep learning) models can incur into generalization issues, specially when trained on synthetic data. To target potential generalization issues, we release an evaluation set based on real podcasts for which we design objective and subjective tests. Out of our experiments with real podcasts, we ﬁnd that current (deep learning) models may have generalization issues. Yet, these can perform competently, e.g., our best baseline separates speech with a mean opinion score of 3.84 (rating “overall separation quality” from 1 to 5). The dataset and baselines are accessible online.
Computing Instance-Optimal Kernels in Two Dimensions

Let $P$ be a set of $n$ points in $\Re^2$. For a parameter $\varepsilon\in (0,1)$, a subset $C\subseteq P$ is an \emph{$\varepsilon$-kernel} of $P$ if the projection of the convex hull of $C$ approximates that of $P$ within $(1-\varepsilon)$-factor in every direction. The set $C$ is a \emph{weak $\varepsilon$-kernel} of $P$ if its directional width approximates that of $P$ in every direction. Let $\mathsf{k}_{\varepsilon}(P)$ (resp.\ $\mathsf{k}^{\mathsf{w}}_{\varepsilon}(P)$) denote the minimum-size of an $\varepsilon$-kernel (resp. weak $\varepsilon$-kernel) of $P$. We present an $O(n\mathsf{k}_{\varepsilon}(P)\log n)$-time algorithm for computing an $\varepsilon$-kernel of $P$ of size $\mathsf{k}_{\varepsilon}(P)$, and an $O(n^2\log n)$-time algorithm for computing a weak $\varepsilon$-kernel of $P$ of size ${\mathsf{k}}^{\mathsf{w}}_{\varepsilon}(P)$. We also present a fast algorithm for the Hausdorff variant of this problem. In addition, we introduce the notion of \emph{$\varepsilon$-core}, a convex polygon lying inside $\mathsf{ch}(P)$, prove that it is a good approximation of the optimal $\varepsilon$-kernel, present an efficient algorithm for computing it, and use it to compute an $\varepsilon$-kernel of small size.
Pick your Neighbor: Local Gauss-Southwell Rule for Fast Asynchronous Decentralized Optimization

In decentralized optimization environments, each agent i in a network of n nodes has its own private function fi, and nodes communicate with their neighbors to cooperatively minimize the aggregate objective $\sum _{i = 1}^n{f_i}$. In this setting, synchronizing the nodes’ updates incurs significant communication overhead and computational costs, so much of the recent literature has focused on the analysis and design of asynchronous optimization algorithms, where agents activate and communicate at arbitrary times without needing a global synchronization enforcer. However, most works assume that when a node activates, it selects the neighbor to contact based on a fixed probability (e.g., uniformly at random), a choice that ignores the optimization landscape at the moment of activation. Instead, in this work we introduce an optimization-aware selection rule that chooses the neighbor providing the highest dual cost improvement (a quantity related to a dualization of the problem based on consensus). This scheme is related to the coordinate descent (CD) method with the Gauss-Southwell (GS) rule for coordinate updates; in our setting however, only a subset of coordinates is accessible at each iteration (because each node can communicate only with its neighbors), so the existing literature on GS methods does not apply. To overcome this difficulty, we develop a new analytical framework for smooth and strongly convex fi that covers the class of set-wise CD algorithms –a class that directly applies to decentralized scenarios, but is not limited to them– and we show that the proposed setwise GS rule achieves a speedup factor of up to the maximum degree in the network (which is in the order of Θ(n) for highly connected graphs). The speedup predicted by our analysis is validated in numerical experiments with synthetic data.
Band splitting induced Berry flux and intrinsic anomalous Hall conductivity in the NiCoMnGa quaternary Heusler compound

The anomalous transport properties of Heusler compounds become a hotspot of research in recent years due to their unique band structure and possible application in spintronics. In this paper, we report the anomalous Hall eﬀect in polycrystalline NiCoMnGa quaternary Heusler compound by experimental means and theoretical calculations. The experimental anomalous Hall conductivity (AHC) was found at about 256 S/cm at 10K with an intrinsic contribution of ∼ 121 S/cm . The analysis of Hall data reveals the presence of both extrinsic and intrinsic contributions in AHE. Our theoretical calculations show that a pair of spin-orbit coupled band formed by the band splitting due to spin-orbit interaction (SOI) at the Fermi level produces a ﬁnite Berry ﬂux in the system that provides the intrinsic AHC about 100 S/cm , which is in good agreement with the experiment.
Multimodal Open-Vocabulary Video Classification via Pre-Trained Vision and Language Models

Utilizing vision and language models (VLMs) pre-trained on large-scale image-text pairs is becoming a promising paradigm for open-vocabulary visual recognition. In this work, we extend this paradigm by leveraging motion and audio that naturally exist in video. We present \textbf{MOV}, a simple yet effective method for \textbf{M}ultimodal \textbf{O}pen-\textbf{V}ocabulary video classification. In MOV, we directly use the vision encoder from pre-trained VLMs with minimal modifications to encode video, optical flow and audio spectrogram. We design a cross-modal fusion mechanism to aggregate complimentary multimodal information. Experiments on Kinetics-700 and VGGSound show that introducing flow or audio modality brings large performance gains over the pre-trained VLM and existing methods. Specifically, MOV greatly improves the accuracy on base classes, while generalizes better on novel classes. MOV achieves state-of-the-art results on UCF and HMDB zero-shot video classification benchmarks, significantly outperforming both traditional zero-shot methods and recent methods based on VLMs. Code and models will be released.
Laparoscopic Anatomy of the Abdomen in Dorsal Recumbent Male Donkey

Laparoscopic anatomy of the abdomen in dorsal recumbent male donkey was an applied anatomical study. Provide an accurate and detailed description of the laparoscopic anatomy of the abdomen of donkey positioned in dorsal recumbency was considered a diagnostic and a therapeutic surgical importance. The laparoscopy was performed on ten adult apparently healthy male donkeys in average of 7-12 years old, in order to record the normal laparoscopic anatomy of abdomen in dorsal recumbency. The preoperative techniques were considered. General anesthesia was induced and maintained with total intravenous agents. Laparoscope was delivered into the abdomen via an umbilical main cannula. Laparoscope and associated video recording system were used to obtain a clear, magnified and panoramic shoots of the abdominal cavity. The anatomical findings were more real and vital than that of fixed condition. The prestaltic motility as well as the vascularization of the serous layer of peritoneum and the abdominal organs was notified. The study was fully described two dangerous triangular areas; TD and TP especially during endoscopic repair of inguinal hernia. The work considered as a gate to further endoscopic anatomy and the donkey was an ideal experimental model. traditional surgical procedures with minimal intra and post-operative complications and as well as tissue damage. Food withholding period, degree of pneumoperitoneum and animal position are main factors affecting organ arrangement and relationship within the abdominal cavity, another factor affecting the endoscopic view of abdominal cavity is the endoscope port position.
HyperRes: Efﬁcient Hypernetwork-Based Continuous Image Restoration

Continuous image restoration attempts to provide a model that can restore images with unseen degradation levels during training at inference time. Existing methods are limited in terms of either the accuracy of the restoration, the range of degradation levels they can support, or the size of the model they require. We introduce a novel approach that achieves the optimal accuracy of multiple dedicated models for a wide range of degradation levels with the same number of parameters as a single base model. We present a hypernetwork that can efﬁciently generate an image restoration network to best adapt to the required level of degradation. Experiments on popular datasets show that our approach outperforms the state-of-the-art for a variety of image restoration tasks, including denoising, DeJPEG, and super-resolution.
Safe and Adaptive Control Approaches for Mobility Assistance Robots

This thesis introduces context-aware, user, and environment-adaptive and safe control approaches for mobility assistance robots (MARs) that support the elderly and patients in three main operational modes of sit-to-stand transfers from a chair, walking and human's fall prevention assistance. The key principle of the concepts for control design in this thesis is to understand the human motor control models and decision-making formalisms, then formulate their mathematical principles and finally employ them into novel and human-inspired control design of MARs.
Machine Learning to Generate Adjustable Dose Distributions in Head-and-Neck Cancer Radiation Therapy

In this work, we propose a Machine Learning model that generates an adjustable 3D dose distribution for external beam radiation therapy for head-and-neck cancer treatments. In contrast to existing Machine Learning methods that provide a single model, we create pairs of models for each organ-at-risk, namely lower-extreme and upper-extreme models. These model pairs for an organ-at-risk propose doses that give lower and higher doses to that organ-at-risk, while also encapsulating the dose trade-oﬀ to other organs-at-risk. By weighting and combining the model pairs for all organs-at-risk, we are able to dynamically create adjustable dose distributions that can be used, in real-time, to move doses between organs-at-risk, thereby customizing the dose distribution to the needs of a particular patient. We leverage a key observation that the training data set inherently contains the clinical trade-oﬀs. We show that the adjustable distributions are able to provide reasonable clinical dose latitude in the trade-oﬀ of doses between organs-at-risk.