Miracle-dz/UniFAR-FTUs huggingface dataset:
Bake off redux: a review and experimental evaluation of recent time series classification algorithms

In 2017, a research paper compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the University of California, Riverside (UCR) archive. This study, commonly referred to as a `bake off', identified that only nine algorithms performed significantly better than the Dynamic Time Warping (DTW) and Rotation Forest benchmarks that were used. The study categorised each algorithm by the type of feature they extract from time series data, forming a taxonomy of five main algorithm types. This categorisation of algorithms alongside the provision of code and accessible results for reproducibility has helped fuel an increase in popularity of the TSC field. Over six years have passed since this bake off, the UCR archive has expanded to 112 datasets and there have been a large number of new algorithms proposed. We revisit the bake off, seeing how each of the proposed categories have advanced since the original publication, and evaluate the performance of newer algorithms against the previous best-of-category using an expanded UCR archive. We extend the taxonomy to include three new categories to reflect recent developments. Alongside the originally proposed distance, interval, shapelet, dictionary and hybrid based algorithms, we compare newer convolution and feature based algorithms as well as deep learning approaches. We introduce 30 classification datasets either recently donated to the archive or reformatted to the TSC format, and use these to further evaluate the best performing algorithm from each category. Overall, we find that two recently proposed algorithms, Hydra+MultiROCKET and HIVE-COTEv2, perform significantly better than other approaches on both the current and new TSC problems.
Integral Field Spectroscopy of the Cometary Starburst Galaxy NGC 4861

Using the PMAS Integral Field Unit on the Calar Alto 3.5m telescope we observed the southern component (Markarian 59) of the ‘cometary’ starburst galaxy NGC 4861. Mrk 59 is centred on a giant nebula and concentration of stars 1 kpc in diameter. Strong $\rm H\alpha$ emission points to a star-formation rate (SFR) at least 0.47 $\rm M_{\odot }yr^{-1}$. Mrk 59 has a very high [OIII]$\rm \lesssim 5007/H\beta$ ratio, reaching 7.35 in the central nebula, with a second peak at a star-forming hotspot further north. Fast outflows are not detected but nebular motion and galaxy rotation produce relative velocities up to 40 km $\rm s^{-1}$. Spectral analysis of different regions with ‘Fitting Analysis using Differential evolution Optimisation’ (FADO) finds that the stars in the central and ‘spur’ nebulae are very young, $\rm \le 125 Myr$ with a large $\rm <10 Myr$ contribution. Older stars ($\rm \sim 1 Gyr$), make up the northern disk component, while the other regions show mixtures of 1 Gyr age with very young stars. This and the high specific SFR $\rm \sim 3.5 Gyr^{-1}$ imply a bimodal star formation history, with Mrk 59 formed in ongoing starbursts fuelled by a huge gas inflow, turning the galaxy into an asymmetric ‘green pea’ or blue compact dwarf. We map the HeIIλ4686 emission, and identify a broad component from the central nebula, consistent with the emission of ∼300 Wolf-Rayet stars. About a third of the HeIIλ4686 flux is a narrow line emitted from a more extended area covering the central and spur nebulae, and may have a different origin.
Communication-Constrained Bandits under Additive Gaussian Noise

We study a distributed stochastic multi-armed bandit where a client supplies the learner with communication-constrained feedback based on the rewards for the corresponding arm pulls. In our setup, the client must encode the rewards such that the second moment of the encoded rewards is no more than $P$, and this encoded reward is further corrupted by additive Gaussian noise of variance $\sigma^2$; the learner only has access to this corrupted reward. For this setting, we derive an information-theoretic lower bound of $\Omega\left(\sqrt{\frac{KT}{\mathtt{SNR} \wedge1}} \right)$ on the minimax regret of any scheme, where $ \mathtt{SNR} := \frac{P}{\sigma^2}$, and $K$ and $T$ are the number of arms and time horizon, respectively. Furthermore, we propose a multi-phase bandit algorithm, $\mathtt{UE\text{-}UCB++}$, which matches this lower bound to a minor additive factor. $\mathtt{UE\text{-}UCB++}$ performs uniform exploration in its initial phases and then utilizes the {\em upper confidence bound }(UCB) bandit algorithm in its final phase. An interesting feature of $\mathtt{UE\text{-}UCB++}$ is that the coarser estimates of the mean rewards formed during a uniform exploration phase help to refine the encoding protocol in the next phase, leading to more accurate mean estimates of the rewards in the subsequent phase. This positive reinforcement cycle is critical to reducing the number of uniform exploration rounds and closely matching our lower bound.
GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapters

This report describes GMU’s sentiment analysis system for the SemEval-2023 shared task AfriSenti-SemEval. We participated in all three sub-tasks: Monolingual, Multilingual, and Zero-Shot. Our approach uses models initialized with AfroXLMR-large, a pre-trained multilingual language model trained on African languages and fine-tuned correspondingly. We also introduce augmented training data along with original training data. Alongside finetuning, we perform phylogeny-based adapter-tuning to create several models and ensemble the best models for the final submission. Our system achieves the best F1-score on track 5: Amharic, with 6.2 points higher F1-score than the second-best performing system on this track. Overall, our system ranks 5th among the 10 systems participating in all 15 tracks.
COUPA: An Industrial Recommender System for Online to Offline Service Platforms

Aiming at helping users locally discover retail services (e.g., entertainment and dining) on Online to Offline (O2O) service platforms, we propose COUPA, an industrial system targeting for characterizing user preference with inspiring considerations of time and position aware preferences. We carefully implement and deploy COUPA in Alipay with a cooperation of edge, streaming and batch computing, as well as a two-stage online serving mode, to support several popular recommendation scenarios. Extensive experiments reveal the superior performance of COUPA for recommendation.
The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium

The symmetric generalized eigenvalue problem (SGEP) is a fundamental concept in numerical linear algebra. It captures the solution of many classical machine learning problems such as canonical correlation analysis, independent components analysis, partial least squares, linear discriminant analysis, principal components and others. Despite this, most general solvers are prohibitively expensive when dealing with streaming data sets (i.e., minibatches) and research has instead concentrated on finding efficient solutions to specific problem instances. In this work, we develop a game-theoretic formulation of the top-$k$ SGEP whose Nash equilibrium is the set of generalized eigenvectors. We also present a parallelizable algorithm with guaranteed asymptotic convergence to the Nash. Current state-of-the-art methods require $O(d^2k)$ runtime complexity per iteration which is prohibitively expensive when the number of dimensions ($d$) is large. We show how to modify this parallel approach to achieve $O(dk)$ runtime complexity. Empirically we demonstrate that this resulting algorithm is able to solve a variety of SGEP problem instances including a large-scale analysis of neural network activations.
Analytical equivalent circuits for three-dimensional metamaterials and metagratings

In recent times, three-dimensional (3D) metamaterials have undergone a revolution driven mainly by the popularization of 3D-printing techniques, which has enabled the implementation of modern microwave and photonic devices with advanced functionalities. However, the analysis of 3D metamaterials is complex and computationally costly in comparison to their 1D and 2D counterparts due to the intricate geometries involved. In this paper, we present a fully-analytical framework based on Floquet-Bloch modal expansions of the electromagnetic fields and integral-equation methods for the analysis of 3D metamaterials and metagratings. Concretely, we focus on 3D configurations formed by periodic arrangements of rectangular waveguides with longitudinal slot insertions. The analytical framework is computationally efficient compared to full-wave solutions and also works under oblique incidence conditions. Furthermore, it comes associated with an equivalent circuit that allows to gain physical insight into the scattering and diffraction phenomena. The analytical equivalent circuit is tested against full-wave simulations in commercial software CST. Simulation results show that the proposed 3D structures provide independent polarization control of the two orthogonal polarizations states. This key property is of potential interest for the production of full-metal polarizers, such as the one illustrated.
Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis

Under the global COVID-19 crisis, accurate diagnosis of COVID-19 from Chest X-ray (CXR) images is critical. To reduce intra- and inter-observer variability, during the radiological assessment, computer-aided diagnostic tools have been utilized to supplement medical decision-making and subsequent disease management. Computational methods with high accuracy and robustness are required for rapid triaging of patients and aiding radiologists in the interpretation of the collected data. In this study, we propose a novel multi-feature fusion network using parallel attention blocks to fuse the original CXR images and local-phase feature-enhanced CXR images at multi-scales. We examine our model on various COVID-19 datasets acquired from different organizations to assess the generalization ability. Our experiments demonstrate that our method achieves state-of-art performance and has improved generalization capability, which is crucial for widespread deployment.
Vision-based Estimation of Fatigue and Engagement in Cognitive Training Sessions

Computerized cognitive training (CCT) is a scalable, well-tolerated intervention that has promise for slowing cognitive decline. Outcomes from CCT are limited by a lack of effective engagement, which is decreased by factors such as mental fatigue, particularly in older adults at risk for dementia. There is a need for scalable, automated measures that can monitor mental fatigue during CCT. Here, we develop and validate a novel Recurrent Video Transformer (RVT) method for monitoring real-time mental fatigue in older adults with mild cognitive impairment from video-recorded facial gestures during CCT. The RVT model achieved the highest balanced accuracy(78%) and precision (0.82) compared to the prior state-of-the-art models for binary and multi-class classification of mental fatigue and was additionally validated via significant association (p=0.023) with CCT reaction time. By leveraging dynamic temporal information, the RVT model demonstrates the potential to accurately measure real-time mental fatigue, laying the foundation for future personalized CCT that increase effective engagement.
Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures

During the past decade, Deep Learning (DL) algorithms, programming systems and hardware have converged with the High Performance Computing (HPC) counterparts. Nevertheless, the programming methodology of DL and HPC systems is stagnant, relying on highly-optimized, yet platform-specific and inflexible vendor-optimized libraries. Such libraries provide close-to-peak performance on specific platforms, kernels and shapes thereof that vendors have dedicated optimizations efforts, while they underperform in the remaining use-cases, yielding non-portable codes with performance glass-jaws. This work introduces a framework to develop efficient, portable DL and HPC kernels for modern CPU architectures. We decompose the kernel development in two steps: 1) Expressing the computational core using Tensor Processing Primitives (TPPs): a compact, versatile set of 2D-tensor operators, 2) Expressing the logical loops around TPPs in a high-level, declarative fashion whereas the exact instantiation (ordering, tiling, parallelization) is determined via simple knobs. We demonstrate the efficacy of our approach using standalone kernels and end-to-end workloads that outperform state-of-the-art implementations on diverse CPU platforms.
Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory

We consider the development of adaptive, instance-dependent algorithms for interactive decision making (bandits, reinforcement learning, and beyond) that, rather than only performing well in the worst case, adapt to favorable properties of real-world instances for improved performance. We aim for instance-optimality, a strong notion of adaptivity which asserts that, on any particular problem instance, the algorithm under consideration outperforms all consistent algorithms. Instance-optimality enjoys a rich asymptotic theory originating from the work of \citet{lai1985asymptotically,graves1997asymptotically}, but non-asymptotic guarantees have remained elusive outside of certain special cases. Even for problems as simple as tabular reinforcement learning, existing algorithms do not attain instance-optimal performance until the number of rounds of interaction is doubly exponential in the number of states. In this paper, we take the first step toward developing a non-asymptotic theory of instance-optimal decision making with general function approximation. We introduce a new complexity measure, the Allocation-Estimation Coefficient (AEC), and provide a new algorithm, $\mathsf{AE}^2$, which attains non-asymptotic instance-optimal performance at a rate controlled by the AEC. Our results recover the best known guarantees for well-studied problems such as finite-armed and linear bandits and, when specialized to tabular reinforcement learning, attain the first instance-optimal regret bounds with polynomial dependence on all problem parameters, improving over prior work exponentially. We complement these results with lower bounds that show that i) existing notions of statistical complexity are insufficient to derive non-asymptotic guarantees, and ii) under certain technical conditions, boundedness of the AEC is necessary to learn an instance-optimal allocation of decisions in finite time.
Deep learning based Auto Tuning for Database Management System

The management of database system configurations is a challenging task, as there are hundreds of configuration knobs that control every aspect of the system. This is complicated by the fact that these knobs are not standardized, independent, or universal, making it difficult to determine optimal settings. An automated approach to address this problem using supervised and unsupervised machine learning methods to select impactful knobs, map unseen workloads, and recommend knob settings was implemented in a new tool called OtterTune and is being evaluated on three DBMSs, with results demonstrating that it recommends configurations as good as or better than those generated by existing tools or a human expert.In this work, we extend an automated technique based on Ottertune [1] to reuse training data gathered from previous sessions to tune new DBMS deployments with the help of supervised and unsupervised machine learning methods to improve latency prediction. Our approach involves the expansion of the methods proposed in the original paper. We use GMM clustering to prune metrics and combine ensemble models, such as RandomForest, with non-linear models, like neural networks, for prediction modeling.
What Causes Exceptions in Machine Learning Applications? Mining Machine Learning-Related Stack Traces on Stack Overflow

Machine learning (ML), including deep learning, has recently gained tremendous popularity in a wide range of applications. However, like traditional software, ML applications are not immune to the bugs that result from programming errors. Explicit programming errors usually manifest through error messages and stack traces. These stack traces describe the chain of function calls that lead to an anomalous situation, or exception. Indeed, these exceptions may cross the entire software stack (including applications and libraries). Thus, studying the patterns in stack traces can help practitioners and researchers understand the causes of exceptions in ML applications and the challenges faced by ML developers. To that end, we mine Stack Overflow (SO) and study 11,449 stack traces related to seven popular Python ML libraries. First, we observe that ML questions that contain stack traces gain more popularity than questions without stack traces; however, they are less likely to get accepted answers. Second, we observe that recurrent patterns exists in ML stack traces, even across different ML libraries, with a small portion of patterns covering many stack traces. Third, we derive five high-level categories and 25 low-level types from the stack trace patterns: most patterns are related to python basic syntax, model training, parallelization, data transformation, and subprocess invocation. Furthermore, the patterns related to subprocess invocation, external module execution, and remote API call are among the least likely to get accepted answers on SO. Our findings provide insights for researchers, ML library providers, and ML application developers to improve the quality of ML libraries and their applications.
Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur

Rendering novel view images is highly desirable for many applications. Despite recent progress, it remains challenging to render high-fidelity and view-consistent novel views of large-scale scenes from in-the-wild images with inevitable artifacts (e.g., motion blur). To this end, we develop a hybrid neural rendering model that makes image-based representation and neural 3D representation join forces to render high-quality, view-consistent images. Besides, images captured in the wild inevitably contain artifacts, such as motion blur, which deteriorates the quality of rendered images. Accordingly, we propose strategies to simulate blur effects on the rendered images to mitigate the negative influence of blurriness images and reduce their importance during training based on precomputed quality-aware weights. Extensive experiments on real and synthetic data demonstrate our model surpasses state-of-the-art point-based methods for novel view synthesis. The code is available at https://daipengwa.github. io/Hybrid-Rendering-ProjectPage/.
Blockchain Large Language Models

This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions. The proposed tool, BlockGPT, generates tracing representations of blockchain activity and trains from scratch a large language model to act as a real-time Intrusion Detection System. Unlike traditional methods, BlockGPT is designed to offer an unrestricted search space and does not rely on predefined rules or patterns, enabling it to detect a broader range of anomalies. We demonstrate the effectiveness of BlockGPT through its use as an anomaly detection tool for Ethereum transactions. In our experiments, it effectively identifies abnormal transactions among a dataset of 68M transactions and has a batched throughput of 2284 transactions per second on average. Our results show that, BlockGPT identifies abnormal transactions by ranking 49 out of 124 attacks among the top-3 most abnormal transactions interacting with their victim contracts. This work makes contributions to the field of blockchain transaction analysis by introducing a custom data encoding compatible with the transformer architecture, a domain-specific tokenization technique, and a tree encoding method specifically crafted for the Ethereum Virtual Machine (EVM) trace representation.
Soliton Microcomb Generation in a III-V Photonic Crystal Cavity

Photonic crystals, material structures in which the dielectric function varies periodically in one, two, or three dimensions, can provide exquisite control over the propagation and confinement of light. By tailoring their band structure, exceptional optical effects can be achieved, such as slow light propagation or, through the creation of photonic bandgaps, optical cavities with both a high quality factor and a small mode volume. Photonic crystal cavities have been used to realize compact nano-lasers and achieve strong coupling to quantum emitters, such as semiconductor quantum dots, color centers, or cold atoms. A useful attribute of photonic crystals is the ability to create chirped mirrors. Chirping has underpinned advances in ultra-fast lasers based on bulk mirrors, but has yet to be fully exploited in integrated photonics, where it could provide a means to engineer otherwise unattainable dispersion profiles for a range of nonlinear optical applications, including soliton frequency comb generation. The vast majority of integrated resonators for frequency combs make use of microring geometries, where only waveguide width and height are varied to engineer dispersion. Generation of frequency combs has been demonstrated with one-dimensional photonic crystal cavities made of silicon nitride, but the low index contrast prevents formation of broad soliton combs. We overcome these challenges by using a photonic-crystal Fabry-P\'erot resonator made of gallium phosphide, a material with a high refractive index and a Kerr nonlinearity 200 times larger than that of silicon nitride. We employ chirped photonic crystal mirrors to provide anomalous dispersion. With subharmonic pulsed pumping at an average power of 23.6 mW, we are able to access stable dissipative Kerr frequency combs. We demonstrate soliton formation with a 3-dB bandwidth of 3.0 THz, corresponding to a pulse duration of 60 fs.
CEDR-API: Productive, Performant Programming of Domain-Specific Embedded Systems

As the computing landscape evolves, system designers continue to explore design methodologies that leverage increased levels of heterogeneity to push performance within limited size, weight, power, and cost budgets. One such methodology is to build Domain-Specific System on Chips (DSSoCs) that promise increased productivity through narrowed scope of their target application domain. In previous works, we have proposed CEDR, an open source, unified compilation and runtime framework for DSSoC architectures that allows applications, scheduling heuristics, and accelerators to be co-designed in a cohesive manner that maximizes system performance. In this work, we present changes to the application development workflow that enable a more productive and expressive API-based programming methodology. These changes allow for more rapid integration of new applications without sacrificing application performance. Towards the design of heterogeneous SoCs with rich set of accelerators, in this study we experimentally study the impact of increase in workload complexity and growth in the pool of compute resources on execution time of dynamically arriving workloads composed of real-life applications executed over architectures emulated on Xilinx ZCU102 MPSoC and Nvidia Jetson AGX Xavier. We expand CEDR into the application domain of autonomous vehicles, and we find that API-based CEDR achieves a runtime overhead reduction of 19.5% with respect to the original CEDR.