Miracle-dz/UniFAR-FTUs huggingface dataset:
Array DBMS: Past, Present, and (Near) Future

Array DBMSs strive to be the best systems for managing, processing, and even visualizing big 𝑁 -d arrays. The last decade blossomed with R&D in array DBMS, making it a young and fast-evolving area. We present the first comprehensive tutorial on array DBMS R&D. We start from past impactful results that are still relevant today, then we cover contemporary array DBMSs, array-oriented systems, and state-of-the-art research in array management, flavored with numerous promising R&D opportunities for future work. A great deal of our tutorial was not covered in any previous tutorial or survey article. Advanced array management research is just emerging and many R&D opportunities still “lie on the surface”. Hence, nowadays we have the most favorable conditions to start contributing to this research area. This tutorial will jump-start such efforts.
Advertising Click-Through Rate Prediction Based on CNN-LSTM Neural Network

In the era of big data information, how to effectively predict and analyze the click-through rate of information advertising is the key for enterprises in various fields to seek returns. The point rate prediction of advertising is one of the core contents of advertising calculation. The traditional shallow prediction model cannot meet the nonlinear relationship of data processing, and the manual processing of data information extraction method is very resource consuming. To solve the above problems, this paper proposes a CNN-LSTM (convolutional neural network-long short-term memory) convolution hybrid neural network algorithm to predict the click-through rate of advertisements. According to the neural network algorithm, the prediction model is constructed, and the effective features are extracted in the process of model establishment, and the prediction analysis is carried out according to the simplified LSTM neural network time serialization features. CNN convolution neural network is used to train the prediction model. This paper analyzes the characteristics of traditional prediction methods and the corresponding solutions and carries out feature learning and prediction model construction for advertising click-through rate prediction. Then, the unknown behavior of advertising users is judged and predicted. The results show that, compared with the single structure network of traditional prediction model, the prediction effect based on CNN-LSTM neural network algorithm has higher accuracy.
AI-Lyricist: Generating Music and Vocabulary Constrained Lyrics

We propose AI-Lyricist: a system to generate novel yet meaningful lyrics given a required vocabulary and a MIDI file as inputs. This task involves multiple challenges, including automatically identifying the melody and extracting a syllable template from multi-channel music, generating creative lyrics that match the input music's style and syllable alignment, and satisfying vocabulary constraints. To address these challenges, we propose an automatic lyrics generation system consisting of four modules: (1) A music structure analyzer to derive the musical structure and syllable template from a given MIDI file, utilizing the concept of expected syllable number to better identify the melody, (2) a SeqGAN-based lyrics generator optimized by multi-adversarial training through policy gradients with twin discriminators for text quality and syllable alignment, (3) a deep coupled music-lyrics embedding model to project music and lyrics into a joint space to allow fair comparison of both melody and lyric constraints, and a module called (4) Polisher, to satisfy vocabulary constraints by applying a mask to the generator and substituting the words to be learned. We trained our model on a dataset of over 7,000 music-lyrics pairs, enhanced with manually annotated labels in terms of theme, sentiment and genre. Both objective and subjective evaluations show AI-Lyricist's superior performance against the state-of-the-art for the proposed tasks.
Easy Variational Inference for Categorical Observations via a New View of Diagonal Orthant Probit Models

In pursuit of tractable Bayesian analysis of categorical data, auxiliary variable methods hold promise, but impose asymmetries on the truly unordered categories or spoil scalability via strong dependencies in posteriors over parameters. The Diag-onal Orthant Probit (DO-Probit) model proposed by Johndrow, Lum, and Dunson (AISTATS 2013) avoids these difﬁculties, treating all categories symmetrically while yielding tractable conditionally conjugate inference. However, we show that the intended DO-Probit likelihood for categorical observations, when paired with a normal prior, does not yield a conjugate posterior. Instead, we clarify that their posterior analysis is only correct for a different model that treats observations as multiple independent binary draws. This raises two questions: Other than tractability, what justiﬁes the binary model for categorical data? And how should a binary model make categorical predictions? To resolve these issues, using variational methods we obtain a lower bound of a categorical model’s marginal likelihood that can be optimized by ﬁtting the conjugate binary model. Optimizing this bound enjoys all beneﬁts advocated in the original DO-Probit work. We further extend this fast, reliable covariate-informed modeling of categorical outcomes to groups or sequences of data related in a hierarchy.
Chat More and Contribute Better: An Empirical Study of a Knowledge-Sharing Community

We analyze whether an informal second channel for communication can improve the efficiency of knowledge transfer in an electronic network of practice. We explore this question by analyzing the effect of chat rooms in the well-known Q&A forum Stack Overflow. We identify the causal effect using a difference-in-differences approach, which exploits a feed functionality that non-selectively pushed all questions from the Q&A into the relevant chat rooms. We report two main findings: First, chat rooms reduced the time until a question in the main Q&A received a satisfactory answer. Second, chat rooms disproportionately benefited new users who asked low-quality questions. Our study has clear managerial implications: A second channel for communication can complement the main channel in online communities to enhance both efficiency and inclusion.
Morphological and molecular diagnosis of invasive aspergillosis in chickens

Confirmatory diagnosis of invasive aspergillosis is paramount to ensure proper treatment and effective management of the disease in food and companion animals. Suspected invasive aspergilllosis in chickens was encountered at post-mortem. Morphological and molecular methods were employed to identify Aspergillus from samples collected from dead chickens at post-mortem. Morphologically, two species of Aspergillus were identified, namely Aspergillus fumigatus and Aspergillus flavus. Molecular identification based on polymerase chain reaction (PCR) and sequence analysis of the partial 5.8 S rRNA, complete internal transcribed spacer-2 and partial 28S rRNA sequences bolstered morphological identification to arrive at the confirmatory diagnosis of the disease. Various hotspots that differentiate A. flavus from A. fumigatus and from other Aspergillus species were identified based on multiple sequence analysis. Maximum likelihood phylogenetic tree showed that isolates from the same species were grouped in the same clade. It is important to correctly identify the Aspergillus species in order to efficiently manage the disease.
On the relation between active-region lifetimes and the autocorrelation function of light curves

Rotational modulation of stellar light curves due to dark spots encloses information on spot properties and, thus, on magnetic activity. In particular, the decay of the autocorrelation function (ACF) of light curves is presumed to be linked to spot/active-region lifetimes, given that some coherence of the signal is expected throughout their lifetime. In the literature, an exponential decay has been adopted to describe the ACF. Here, we investigate the relation between the ACF and the active-region lifetimes. For this purpose, we produce artificial light curves of rotating spotted stars with different observation, stellar, and spot properties. We find that a linear decay and respective time-scale better represent the ACF than the exponential decay. We therefore adopt a linear decay. The spot/active-region time-scale inferred from the ACF is strongly restricted by the observation length of the light curves. For 1-yr light curves our results are consistent with no correlation between the inferred and the input time-scales. The ACF decay is also significantly affected by differential rotation and spot evolution: strong differential rotation and fast spot evolution contribute to a more severe underestimation of the active-region lifetimes. Nevertheless, in both circumstances the observed time-scale is still correlated with the input lifetimes. Therefore, our analysis suggests that the ACF decay can be used to obtain a lower limit of the active-region lifetimes for relatively long-term observations. However, strategies to avoid or flag targets with fast active-region evolution or displaying stable beating patterns associated with differential rotation should be employed.
Transfer Learning of Individualized Treatment Rules from Experimental to Real-World Data

Abstract Individualized treatment effect lies at the heart of precision medicine. Interpretable individualized treatment rules (ITRs) are desirable for clinicians or policymakers due to their intuitive appeal and transparency. The gold-standard approach to estimating the ITRs is randomized experiments, where subjects are randomized to different treatment groups and the confounding bias is minimized to the extent possible. However, experimental studies are limited in external validity because of their selection restrictions, and therefore the underlying study population is not representative of the target real-world population. Conventional learning methods of optimal interpretable ITRs for a target population based only on experimental data are biased. On the other hand, real-world data (RWD) are becoming popular and provide a representative sample of the target population. To learn the generalizable optimal interpretable ITRs, we propose an integrative transfer learning method based on weighting schemes to calibrate the covariate distribution of the experiment to that of the RWD. Theoretically, we establish the risk consistency for the proposed ITR estimator. Empirically, we evaluate the finite-sample performance of the transfer learner through simulations and apply it to a real data application of a job training program. Supplementary materials for this article are available online.
DESYR: Definition and Syntactic Representation Based Claim Detection on the Web

The formulation of a claim rests at the core of argument mining. To demarcate between a claim and a non-claim is arduous for both humans and machines, owing to latent linguistic variance between the two and the inadequacy of extensive definition-based formalization. Furthermore, the increase in the usage of online social media has resulted in an explosion of unsolicited information on the web presented as informal text. To account for the aforementioned, in this paper, we propose DESYR. It is a framework that intends on annulling the said issues for informal web-based text by leveraging a combination of hierarchical representation learning (dependency-inspired Poincaré embedding), definition-based alignment, and feature projection. We do away with fine-tuning compute-heavy language models in favor of fabricating a more domain-centric but lighter approach. Experimental results indicate that DESYR builds upon the state-of-the-art system across four benchmark claim datasets, most of which were constructed with informal texts. We see an increase of 3 claim-F1 points on the LESA-Twitter dataset, an increase of 1 claim-F1 point and 9 macro-F1 points on the Online Comments (OC) dataset, an increase of 24 claim-F1 points and 17 macro-F1 points on the Web Discourse (WD) dataset, and an increase of 8 claim-F1 points and 5 macro-F1 points on the Micro Texts (MT) dataset. We also perform an extensive analysis of the results. We make a 100-D pre-trained version of our Poincaré-variant along with the source code.
Prior Is All You Need to Improve the Robustness and Safety for the First Time Deployment of Meta RL

The field of Meta Reinforcement Learning (MetaRL) has seen substantial advancements recently. In particular, off-policy methods were developed to improve the data efficiency of Meta-RL techniques. Probabilistic embeddings for actor-critic RL (PEARL) is currently one of the leading approaches for multi-MDP adaptation problems. A major drawback of many existing Meta-RL methods, including PEARL, is that they do not explicitly consider the safety of the prior policy when it is exposed to a new task for the very first time. This is very important for some real world applications, including field robots and Autonomous Vehicles (AVs). In this paper, we develop the PEARL PLUS (PEARL) algorithm, which optimizes the policy for both prior safety and posterior adaptation. Building on top of PEARL, our proposed PEARL algorithm introduces a prior regularization term in the reward function and a new Q-network for recovering the state-action value with prior context assumption, to improve the robustness and safety of the trained network exposing to a new task for the first time. The performance of the PEARL method is demonstrated by solving three safety-critical decision making problems related to robots and AVs, including two MuJoCo benchmark problems. From the simulation experiments, we show that the safety of the prior policy is significantly improved compared to that of the original PEARL method.
Analyze and Design Network Architectures by Recursion Formulas

The effectiveness of shortcut/skip-connection has been widely verified, which inspires massive explorations on neural architecture design. This work attempts to find an effective way to design new network architectures. It is discovered that the main difference between network architectures can be reflected in their recursion formulas. Based on this, a methodology is proposed to design novel network architectures from the perspective of mathematical formulas. Afterwards, a case study is provided to generate an improved architecture based on ResNet. Furthermore, the new architecture is compared with ResNet and then tested on ResNet-based networks. Massive experiments are conducted on CIFAR and ImageNet, which witnesses the significant performance improvements provided by the architecture.
Monarch: A Durable Polymorphic Memory for Data Intensive Applications

3D die stacking has often been proposed to build large-scale DRAM-based caches. Unfortunately, the power and performance overheads of DRAM limit the efficiency of high-bandwidth memories. Also, DRAM is facing serious scalability challenges that make alternative technologies more appealing. This paper examines Monarch, a resistive 3D stacked memory based on a novel reconfigurable crosspoint array called XAM. The XAM array is capable of switching between random access and content-addressable modes, which enables Monarch (i) to better utilize the in-package bandwidth and (ii) to satisfy both the random access memory and associative search requirements of various applications. Moreover, the Monarch controller ensures a given target lifetime for the resistive stack. Our simulation results on a set of parallel memory-intensive applications indicate that Monarch outperforms an ideal DRAM caching by <inline-formula><tex-math notation="LaTeX">$1.21\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>. </mml:mo><mml:mn>21</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="prasad-ieq1-3160608.gif"/></alternatives></inline-formula> on average. For in-memory hash table and string matching workloads, Monarch improves performance up to <inline-formula><tex-math notation="LaTeX">$12\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>12</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="prasad-ieq2-3160608.gif"/></alternatives></inline-formula> over the conventional high bandwidth memories.
Unsupervised Cross-Lingual Speech Emotion Recognition Using Pseudo Multilabel

Speech Emotion Recognition (SER) in a single language has achieved remarkable results through deep learning approaches in the last decade. However, cross-lingual SER remains a challenge in real-world applications due to a great difference between the source and target domain distributions. To address this issue, we propose an unsupervised cross-lingual Neural Network with Pseudo Multilabel (NNPM) that is trained to learn the emotion similarities between source domain features inside an external memory adjusted to identify emotion in cross-lingual databases. NNPM introduces a novel approach that leverages external memory to store source domain features and generates pseudo multilabel for each target domain data by computing the similarities between the external memory and the target domain features. We evaluate our approach on multiple different languages of speech emotion databases. Experimental results show our proposed approach significantly improves the weighted accuracy (WA) across multiple low-resource languages on Urdu, Skropus, ShEMO, and EMO-DB corpus. To facilitate further research, code is available at https://github.com/happyjin/NNPM
Order Optimal One-Shot Federated Learning for non-Convex Loss Functions

—We consider the problem of federated learning in a one-shot setting in which there are m machines, each observing n samples function from an unknown distribution on non-convex loss functions. Let F : [ − 1 , 1] d → R be the expected loss function with respect to this unknown distribution. The goal is to ﬁnd an estimate of the minimizer of F . Based on its observations, each machine generates a signal of bounded length B and sends it to a server. The sever collects signals of all machines and outputs an estimate of the minimizer of F . We propose a distributed learning algorithm, called Multi-Resolution Estimator for Non-Convex loss function (MRE-NC), whose expected error is bounded by max (cid:0) 1 / √ n ( mB ) 1 /d , 1 / √ mn (cid:1) , up to polylogarithmic factors. We also provide a matching lower bound on the performance of any algorithm, showing that MRE-NC is order optimal in terms of n and m . Experiments on synthetic and real data show the effectiveness of MRE-NC in distributed learning of model’s parameters for non-convex loss functions. Index
Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

Motivated by the tremendous progress, we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multi-robot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of C-SLAM in terms of robustness, communication, and resource management. We conclude by exploring the area’s current trends and promising research avenues.
A Reinforcement Learning Approach for Global Navigation Satellite System Spoofing Attack Detection in Autonomous Vehicles

A resilient positioning, navigation, and timing (PNT) system is a necessity for the robust navigation of autonomous vehicles (AVs). A global navigation satellite system (GNSS) provides satellite-based PNT services. However, a spoofer can tamper the authentic GNSS signal and could transmit wrong position information to an AV. Therefore, an AV must have the capability of real-time detection of spoofing attacks related to PNT receivers, whereby it will help the end-user (the AV in this case) to navigate safely even if the GNSS is compromised. This paper aims to develop a deep reinforcement learning (RL)-based turn-by-turn spoofing attack detection method using low-cost in-vehicle sensor data. We have utilized the Honda Research Institute Driving Dataset to create attack and non-attack datasets to develop a deep RL model and have evaluated the performance of the deep RL-based attack detection model. We find that the accuracy of the deep RL model ranges from 99.99% to 100%, and the recall value is 100%. Furthermore, the precision ranges from 93.44% to 100%, and the f1 score ranges from 96.61% to 100%. Overall, the analyses reveal that the RL model is effective in turn-by-turn spoofing attack detection.
Flavorful composite Higgs model: Connecting the 
B
 anomalies with the hierarchy problem

We present a model which connects the neutral current B anomalies with composite Higgs models. The model is based on the minimal fundamental composite Higgs model with SU(4)/Sp(4) coset. The strong dynamics spontaneously break the symmetry and introduce five Nambu-Goldstone bosons. Four of them become the Standard Model Higgs doublet and the last one, corresponding to the broken local U(1)′ symmetry, is eaten by the gauge boson. This leads to an additional TeVscale Z′ boson, which can explain the recent B anomalies. The experimental constraints and allowed parameter space are discussed in detail.
Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks

Clustered Federated Multitask Learning (CFL) was introduced as an efficient scheme to obtain reliable specialized models when data is imbalanced and distributed in a non-i.i. d. (non-independent and identically distributed) fashion amongst clients. While a similarity measure metric, like the cosine similarity, can be used to endow groups of the client with a specialized model, this process can be arduous as the server should involve all clients in each of the federated learning rounds. Therefore, it is imperative that a subset of clients is selected periodically due to the limited bandwidth and latency constraints at the network edge. To this end, this paper proposes a new client selection algorithm that aims to accelerate the convergence rate for obtaining specialized machine learning models that achieve high test accuracies for all client groups. Specifically, we introduce a client selection approach that leverages the devices' heterogeneity to schedule the clients based on their round latency and exploits the bandwidth reuse for clients that consume more time to update the model. Then, the server performs model averaging and clusters the clients based on predefined thresholds. When a specific cluster reaches a stationary point, the proposed algorithm uses a greedy scheduling algorithm for that group by selecting the clients with less latency to update the model. Extensive experiments show that the proposed approach lowers the training time and accelerates the convergence rate by up to 50% while imbuing each client with a specialized model that is fit for its local data distribution.
Compositional Diversity of Rocky Exoplanets

To test whether exoplanets are similar to Earth, knowledge of their host star composition is essential. Stellar elemental abundances and planetary orbital data show that of the ~5,000 known minerals, exoplanetary silicate mantles contain mostly olivine, orthopyroxene, and clinopyroxene, ± quartz and magnesiowüstite at the extremes, while wholly exotic mineralogies are unlikely. Understanding the geology of exoplanets requires a better marriage of geological insights to astronomical data. The study of exoplanets is like a mirror: it reflects our incomplete understanding of Earth and neighboring planets. New geological/planetary experiments, informed by exoplanet studies, are needed for effective progress.