Miracle-dz/UniFAR-FTUs huggingface dataset:
Skyrmionic device for three dimensional magnetic field sensing enabled by spin-orbit torques

Magnetic skyrmions are topologically protected local magnetic solitons that are promising for storage, logic or general computing applications. In this work, we demonstrate that we can use a skyrmion device based on [W/CoFeB/MgO] 1 0 multilayers for three-dimensional magnetic field sensing enabled by spin-orbit torques (SOT). We stabilize isolated chiral skyrmions and stripe domains in the multilayers, as shown by magnetic force microscopy images and micromagnetic simulations. We perform magnetic transport measurements to show that we can sense both in-plane and out-of-plane magnetic fields by means of a differential measurement scheme in which the symmetry of the SOT leads to cancelation of the DC offset. With the magnetic parameters obtained by vibrating sample magnetometry and ferromagnetic resonance measurements, we perform finite-temperature micromagnetic simulations, where we investigate the fundamental origin of the sensing signal. We identify the topological transformation between skyrmions, stripes and type-II bubbles that leads to a change in the resistance that is read-out by the anomalous Hall effect. Our study presents a novel application for skyrmions, where a differential measurement sensing concept is applied to quantify external magnetic fields paving the way towards more energy efficient applications in skyrmionics based spintronics.
XAV: A High-Performance Regular Expression Matching Engine for Packet Processing

Regular expression matching is the core function of various network security applications such as network intrusion detection systems. With the network bandwidth increases, it is a great challenge to implement regular expression matching for line rate packet processing. To this end, a novel scheme named XAV targeting high-performance regular expression matching is proposed in this paper. XAV first employs anchor DFA to tackle the state explosion problem of DFA. Then based on anchor DFA, two techniques including pre-filtering and regex decomposition are utilized to improve the average time complexity. Through implementing XAV with an FPGA-CPU architecture, comprehensive experiments show that a high matching throughput of up to 75 Gbps can be achieved for the large and complex Snort rule-set. Compared to state-of-the-art software schemes, XAV achieves two orders of magnitude of performance improvement. While compared to state-of-the-art FPGA-based schemes, XAV achieves more than 2.5x performance improvement with the same hardware resource consumption.
Evaluating the Performance of LLMs on Technical Language Processing tasks

In this paper we present the results of an evaluation study of the perfor-mance of LLMs on Technical Language Processing tasks. Humans are often confronted with tasks in which they have to gather information from dispar-ate sources and require making sense of large bodies of text. These tasks can be significantly complex for humans and often require deep study including rereading portions of a text. Towards simplifying the task of gathering in-formation we evaluated LLMs with chat interfaces for their ability to provide answers to standard questions that a human can be expected to answer based on their reading of a body of text. The body of text under study is Title 47 of the United States Code of Federal Regulations (CFR) which describes regula-tions for commercial telecommunications as governed by the Federal Com-munications Commission (FCC). This has been a body of text of interest be-cause our larger research concerns the issue of making sense of information related to Wireless Spectrum Governance and usage in an automated manner to support Dynamic Spectrum Access. The information concerning this wireless spectrum domain is found in many disparate sources, with Title 47 of the CFR being just one of many. Using a range of LLMs and providing the required CFR text as context we were able to quantify the performance of those LLMs on the specific task of answering the questions below.
Drive System Dynamics Compensator for a Mechanical System Emulator

A system compensator is designed to cancel the mechanical characteristics of a power electronics-controlled induction motor drive with a coupled electrical generator system. The emulation scheme using the compensator enables accurate emulation of high-speed and high-power mechanical system dynamics. The compensator is developed based on the system transient response of the test rig, considering the full operating range of the test system. The design process of the compensator is described, and it is validated in both the time and frequency domains. Finally, the effectiveness of the compensator is demonstrated by simulation and experimental emulation of aero gas engine dynamics.
Habitat Selection of Invasive Sika Deer Cervus nippon Living in a UK Lowland Heathland-Woodland-Grassland Mosaic: Implications for Habitat Conservation Management

Understanding the factors determining the choice and use of habitats by invasive species is key to the conservation management of habitats and may also enable species to be harnessed as conservation tools. Here we explore the habitat use of an invasive population of sika deer, Cervus nippon on internationally important heathland in a landscape of heathland, grassland and woodland in southern UK. We used radio telemetry to test two hypotheses 1) grasses form a major part of the diet of non-native UK sika deer throughout the year 2) deer select grassland habitats above other habitats available. Results showed that although grasses form a major part of their diet, the strongest habitat selection was for heathland, the habitat that offered the least nutrient reward but which offered a source of roughage in the diet and some harbourage from human disturbance. This has implications for the conservation management of heathlands used by sika deer as it strongly indicates that heathland is a vulnerable habitat due to being favoured by sika deer but that its vulnerability can be reduced by coupling population control with targeted habitat management action such as increased disturbance or the removal of harbourage.
A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services

As Machine Learning systems become increasingly popular across diverse application domains, including those with direct human implications, the imperative of equity and algorithmic fairness has risen to prominence in the Artificial Intelligence community. On the other hand, in the context of Shared Micromobility Systems, the exploration of fairness-oriented approaches remains limited. Addressing this gap, we introduce a pioneering investigation into the balance between performance optimization and algorithmic fairness in the operation and control of Shared Micromobility Services. Our study leverages the Q-Learning algorithm in Reinforcement Learning, benefiting from its convergence guarantees to ensure the robustness of our proposed approach. Notably, our methodology stands out for its ability to achieve equitable outcomes, as measured by the Gini index, across different station categories--central, peripheral, and remote. Through strategic rebalancing of vehicle distribution, our approach aims to maximize operator performance while simultaneously upholding fairness principles for users. In addition to theoretical insights, we substantiate our findings with a case study or simulation based on synthetic data, validating the efficacy of our approach. This paper underscores the critical importance of fairness considerations in shaping control strategies for Shared Micromobility Services, offering a pragmatic framework for enhancing equity in urban transportation systems.
Learning Multiple-Nonterminal Synchronous Grammars for Statistical Machine Translation

Recent work in machine translation has evolved from the traditional word and phrase based models to include hierarchical phrase based and syntax-based models. These advances are motivated by the desire to integrate richer knowledge within the translation process to explicitly address limitations of the purely lexical phrasebased model. Generalized phrases as discussed in (Chiang, 2005) attempt to directly address the limitiations of purely lexical phrases, and have shown significant improvements in translation quality by introducing constructs for sub-phrase representation. However, generalizations are represented by a single sub-phrase category (and a glue rule for serial combination), providing the ability (and risk) of inserting any available sub-phrase into a larger phrase. The first contribution of this dissertation work is syntax-augmented machine translation (SAMT), an extension to Chiang’s model that provides multiple generalization types based on the phrase-structure parse trees of the training target sentences. We report improvements over strong phrase-based as well as hierarchical phrase-based baselines for French-to-English, Chinese-to-English, and Urdu-toEnglish. Syntax-based models such as SAMT typically rely on word-alignments and parse trees of the training sentence pairs, which are assumed to be correct. In reality, these alignments and parses are not human-generated, but instead result from the most probable configuration of a stochastic model. We provide a method to induce grammars over hidden alignments and parses, approximated from N -best lists. We present results showing improvements for hierarchical phrase-based MT as well as SAMT when using the widened pipeline. The SAMT model presupposes the availability of phrase-structure parse trees for the target training sentences. However, syntactic parsers are only available for a limited set of languages. We propose a labeling approach that is based merely on part-of-speech analysis of the source or target language (or even both). When using English POS tags in our labeling approach we achieve improvements in translation quality over Chiang’s hierarchical phrase-based MT model. We propose the application of the model to automatically learned word tags as future dissertation work. We further propose to induce a multi-nonterminal grammar from training data without any linguistic annotations based on a generative model over initial rules whose labels are latent variables. Our algorithm’s underlying model assigns class labels to phrase pairs. These labeled phrase pairs are then used as initial rules fed to the rule extraction algorithm of Chiang (2005), resulting in a synchronous gram-
Visualizing code clone outbreak: An industrial case study

This paper describes an industrial experience on code clone visualization. Cloning source code fragments is a common practice in software development process. However, uncontrolled proliferation of code clones causes a serious problem in terms of software maintenance. In this paper, we briefly share our experience on code clone visualization especially for stakeholders who are not software experts. We describe our prototype tool for code clone visualization, and the feedback we have received with analyzing an enterprise business system.
Parametric Encoding with Attention and Convolution Mitigate Spectral Bias of Neural Partial Differential Equation Solvers

Deep neural networks (DNNs) are increasingly used to solve partial differential equations (PDEs) that naturally arise while modeling a wide range of systems and physical phenomena. However, the accuracy of such DNNs decreases as the PDE complexity increases and they also suffer from spectral bias as they tend to learn the low-frequency solution characteristics. To address these issues, we introduce Parametric Grid Convolutional Attention Networks (PGCANs) that can solve PDE systems without leveraging any labeled data in the domain. The main idea of PGCAN is to parameterize the input space with a grid-based encoder whose parameters are connected to the output via a DNN decoder that leverages attention to prioritize feature training. Our encoder provides a localized learning ability and uses convolution layers to avoid overfitting and improve information propagation rate from the boundaries to the interior of the domain. We test the performance of PGCAN on a wide range of PDE systems and show that it effectively addresses spectral bias and provides more accurate solutions compared to competing methods.
Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks

Graph data, also known as complex network data, is omnipresent across various domains and applications. Prior graph neural network models primarily focused on extracting task-specific structural features through supervised learning objectives, but they fell short in capturing the inherent semantic and structural features of the entire graph. In this paper, we introduce the semantic-structural attention-enhanced graph convolutional network (SSA-GCN), which not only models the graph structure but also extracts generalized unsupervised features to enhance vertex classification performance. The SSA-GCN's key contributions lie in three aspects: firstly, it derives semantic information through unsupervised feature extraction from a knowledge graph perspective; secondly, it obtains structural information through unsupervised feature extraction from a complex network perspective; and finally, it integrates these features through a cross-attention mechanism. By leveraging these features, we augment the graph convolutional network, thereby enhancing the model's generalization capabilities. Our experiments on the Cora and CiteSeer datasets demonstrate the performance improvements achieved by our proposed method. Furthermore, our approach also exhibits excellent accuracy under privacy settings, making it a robust and effective solution for graph data analysis.
ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search

Retrieval-based code question answering seeks to match user queries in natural language to relevant code snippets. Previous approaches typically rely on pretraining models using crafted bi-modal and uni-modal datasets to align text and code representations. In this paper, we introduce ProCQA, a large-scale programming question answering dataset extracted from the StackOverflow community, offering naturally structured mixed-modal QA pairs. To validate its effectiveness, we propose a modality-agnostic contrastive pre-training approach to improve the alignment of text and code representations of current code language models. Compared to previous models that primarily employ bimodal and unimodal pairs extracted from CodeSearchNet for pre-training, our model exhibits significant performance improvements across a wide range of code retrieval benchmarks.
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Weighted RAIM for Precision Approach

The use of differential GPS is becoming increasingly popular for real-time navigation systems. As these systems migrate to safety-of-life applications (e.g. precision approach and landing), their integrity becomes more important than their accuracy. One method for increasing both accuracy and integrity is the use of weighting in the navigation solution. This method uses a priori information to weight certain satellites (e.g. those at higher elevation) over other satellites. The accuracy increases because we better use the information available. The integrity increases because satellites that are more likely to introduce error contribute less to the solution. A weighted position solution by itself does not provide sufficient integrity to support precision approach. However, this method can be combined with a weighted form of Receiver Autonomous Integrity Monitoring (RAIM) to increase the level of integrity. RAIM uses redundant measurements to check the consistency of an overdetermined solution. This check is crucial because only a user can detect certain error types (e.g. severe airframe multipath or local interference). A differential reference station can detect many types of errors. However, it is only at the user where all the information is combined. The use of RAIM (or some form of integrity at the user) must be combined together with integrity checking at the reference station to provide the overall safety of the system. Weighted RAIM is investigated for application to Category I precision approach as supported by a Wide Area Augmentation System (WAAS). This paper details how to implement weighted RAIM and how to use geometry selection to guarantee a certain level of protection. Also, we provide information on the availability of these geometries. The results are based upon analysis, Monte Carlo simulation and actual data collected from Stanford University’s wide-area differential GPS network.
Language Rectified Flow: Advancing Diffusion Language Generation with Probabilistic Flows

Recent works have demonstrated success in controlling sentence attributes ($e.g.$, sentiment) and structure ($e.g.$, syntactic structure) based on the diffusion language model. A key component that drives theimpressive performance for generating high-quality samples from noise is iteratively denoise for thousands of steps. While beneficial, the complexity of starting from the noise and the learning steps has limited its implementation to many NLP real-world applications. This paper proposes Language Rectified Flow ({\ours}). Our method is based on the reformulation of the standard probabilistic flow models. Language rectified flow learns (neural) ordinary differential equation models to transport between the source distribution and the target distribution, hence providing a unified and effective solution to generative modeling and domain transfer. From the source distribution, our language rectified flow yields fast simulation and effectively decreases the inference time. Experiments on three challenging fine-grained control tasks and multiple high-quality text editing show that our method consistently outperforms its baselines. Extensive experiments and ablation studies demonstrate that our method can be general, effective, and beneficial for many NLP tasks.
A comparison of Human, GPT-3.5, and GPT-4 Performance in a University-Level Coding Course

This study evaluates the performance of ChatGPT variants, GPT-3.5 and GPT-4, both with and without prompt engineering, against solely student work and a mixed category containing both student and GPT-4 contributions in university-level physics coding assignments using the Python language. Comparing 50 student submissions to 50 AI-generated submissions across different categories, and marked blindly by three independent markers, we amassed $n = 300$ data points. Students averaged 91.9% (SE:0.4), surpassing the highest performing AI submission category, GPT-4 with prompt engineering, which scored 81.1% (SE:0.8) - a statistically significant difference (p = $2.482 \times 10^{-10}$). Prompt engineering significantly improved scores for both GPT-4 (p = $1.661 \times 10^{-4}$) and GPT-3.5 (p = $4.967 \times 10^{-9}$). Additionally, the blinded markers were tasked with guessing the authorship of the submissions on a four-point Likert scale from `Definitely AI' to `Definitely Human'. They accurately identified the authorship, with 92.1% of the work categorized as 'Definitely Human' being human-authored. Simplifying this to a binary `AI' or `Human' categorization resulted in an average accuracy rate of 85.3%. These findings suggest that while AI-generated work closely approaches the quality of university students' work, it often remains detectable by human evaluators.
Horoballs and the subgradient method

To explore convex optimization on Hadamard spaces, we consider an iteration in the style of a subgradient algorithm. Traditionally, such methods assume that the underlying spaces are manifolds and that the objectives are geodesically convex: the methods are described using tangent spaces and exponential maps. By contrast, our iteration applies in a general Hadamard space, is framed in the underlying space itself, and relies instead on horospherical convexity of the objective level sets. For this restricted class of objectives, we prove a complexity result of the usual form. Notably, the complexity does not depend on a lower bound on the space curvature.
Towards Autonomic Distribution of Existing Object Oriented Programs

By harnessing computational power of distributed heterogeneous resources, it is possible to build a large scale integrated system so that a centralized program is partitioned and distributed to those resources in a way that results in both efficient execution of the program and maximized resource utilization. However, building such a system is a staggering challenge because of the associated complexities and required user intervention. This paper proposes an autonomic distributed architecture that statically analyzes the existing Java application, partitions it to self-managed components that handles the complexities related to distribution and coordination without user involvement. An efficient static analysis mechanism is implemented that identifies run time program instances and their dependencies in terms of a graph. It is observed that such a view of the program is essential towards self optimization and self management
Quantum Networks Enhanced by Distributed Quantum Memories

Building large-scale quantum communication networks has its unique challenges. Here, we demonstrate that a network-wide synergistic usage of quantum memories distributed in a quantum communication network offers a fundamental advantage. We first map the problem of quantum communication with local usage of memories into a classical continuum percolation model. Then, we show that this mapping can be improved through a cooperation of entanglement distillation and relay protocols via remote access to distributed memories. This improved mapping, which we term $\alpha$-percolation, can be formulated in terms of graph-merging rules, analogous to the decimation rules of the renormalization group treatment of disordered quantum magnets. These rules can be performed in any order, yielding the same optimal result, which is characterized by the emergence of a ``positive feedback'' mechanism and the formation of spatially disconnected ``hopping'' communication components -- both marking significant improvements in quantum network connectivity.
Ramsey multiplicity of apices of trees

A graph $H$ is common if its Ramsey multiplicity, i.e., the minimum number of monochromatic copies of $H$ contained in any $2$-edge-coloring of $K_n$, is asymptotically the same as the number of monochromatic copies in the random $2$-edge-coloring of $K_n$. Erd\H{o}s conjectured that every complete graph is common, which was disproved by Thomason in the 1980s. Till today, a classification of common graphs remains a widely open challenging problem. Grzesik, Lee, Lidick\'y and Volec [Combin. Prob. Comput. 31 (2022), 907--923] conjectured that every $k$-apex of any connected Sidorenko graph is common. We prove for $k\le 5$ that the $k$-apex of any tree is common.