Reviewerly/exHarmony huggingface dataset:
No-Arbitrage Deep Calibration for Volatility Smile and Skewness

Volatility smile and skewness are two key properties of option prices that are represented by the implied volatility (IV) surface. However, IV surface calibration through nonlinear interpolation is a complex problem due to several factors, including limited input data, low liquidity, and noise. Additionally, the calibrated surface must obey the fundamental financial principle of the absence of arbitrage, which can be modeled by various differential inequalities over the partial derivatives of the option price with respect to the expiration time and the strike price. To address these challenges, we have introduced a Derivative-Constrained Neural Network (DCNN), which is an enhancement of a multilayer perceptron (MLP) that incorporates derivatives in the objective function. DCNN allows us to generate a smooth surface and incorporate the no-arbitrage condition thanks to the derivative terms in the loss function. In numerical experiments, we train the model using prices generated with the SABR model to produce smile and skewness parameters. We carry out different settings to examine the stability of the calibrated model under different conditions. The results show that DCNNs improve the interpolation of the implied volatility surface with smile and skewness by integrating the computation of the derivatives, which are necessary and sufficient no-arbitrage conditions. The developed algorithm also offers practitioners an effective tool for understanding expected market dynamics and managing risk associated with volatility smile and skewness.
Quantum Key Distribution With an Integrated Photonic Receiver

Photonic integrated circuits (PICs) are key in advancing quantum technologies for secure communications. They offer inherent stability, low losses and compactness compared to standard fiber-based and free-space systems. Our reasearch demonstrates PIC's effectivness in enhancing quantum communications, implementing a three-state BB84 protocol with decoy-state method. We employ an integrated receiver and superconducting nanowire single photon detectors (SNSPDs) to achieve technological advancements. One of the most notable results is the extraction of a secret key over a record-breaking 45 dB channel attenuation. Our results demonstrate a remarkable 220% boost in key rate compared to our prototype fiber-based receiver over a 10 dB channel attenuation. This improvement in the secret key rate (SKR) signifies the potential of integrated photonics to advance the field of quantum communication.
Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation

Conversational Recommendation System (CRS) is a rapidly growing research area that has gained significant attention alongside advancements in language modelling techniques. However, the current state of conversational recommendation faces numerous challenges due to its relative novelty and limited existing contributions. In this study, we delve into benchmark datasets for developing CRS models and address potential biases arising from the feedback loop inherent in multi-turn interactions, including selection bias and multiple popularity bias variants. Drawing inspiration from the success of generative data via using language models and data augmentation techniques, we present two novel strategies, 'Once-Aug' and 'PopNudge', to enhance model performance while mitigating biases. Through extensive experiments on ReDial and TG-ReDial benchmark datasets, we show a consistent improvement of CRS techniques with our data augmentation approaches and offer additional insights on addressing multiple newly formulated biases.
Simple, Scalable and Effective Clustering via One-Dimensional Projections

Clustering is a fundamental problem in unsupervised machine learning with many applications in data analysis. Popular clustering algorithms such as Lloyd's algorithm and $k$-means++ can take $\Omega(ndk)$ time when clustering $n$ points in a $d$-dimensional space (represented by an $n\times d$ matrix $X$) into $k$ clusters. In applications with moderate to large $k$, the multiplicative $k$ factor can become very expensive. We introduce a simple randomized clustering algorithm that provably runs in expected time $O(\mathrm{nnz}(X) + n\log n)$ for arbitrary $k$. Here $\mathrm{nnz}(X)$ is the total number of non-zero entries in the input dataset $X$, which is upper bounded by $nd$ and can be significantly smaller for sparse datasets. We prove that our algorithm achieves approximation ratio $\smash{\widetilde{O}(k^4)}$ on any input dataset for the $k$-means objective. We also believe that our theoretical analysis is of independent interest, as we show that the approximation ratio of a $k$-means algorithm is approximately preserved under a class of projections and that $k$-means++ seeding can be implemented in expected $O(n \log n)$ time in one dimension. Finally, we show experimentally that our clustering algorithm gives a new tradeoff between running time and cluster quality compared to previous state-of-the-art methods for these tasks.
RDBench: ML Benchmark for Relational Databases

Benefiting from high-quality datasets and standardized evaluation metrics, machine learning (ML) has achieved sustained progress and widespread applications. However, while applying machine learning to relational databases (RDBs), the absence of a well-established benchmark remains a significant obstacle to the development of ML. To address this issue, we introduce ML Benchmark For Relational Databases (RDBench), a standardized benchmark that aims to promote reproducible ML research on RDBs that include multiple tables. RDBench offers diverse RDB datasets of varying scales, domains, and relational structures, organized into 4 levels. Notably, to simplify the adoption of RDBench for diverse ML domains, for any given database, RDBench exposes three types of interfaces including tabular data, homogeneous graphs, and heterogeneous graphs, sharing the same underlying task definition. For the first time, RDBench enables meaningful comparisons between ML methods from diverse domains, ranging from XGBoost to Graph Neural Networks, under RDB prediction tasks. We design multiple classification and regression tasks for each RDB dataset and report averaged results over the same dataset, further enhancing the robustness of the experimental findings. RDBench is implemented with DBGym, a user-friendly platform for ML research and application on databases, enabling benchmarking new ML methods with RDBench at ease.
Finding the saddlepoint faster than sorting

A saddlepoint of an $n \times n$ matrix $A$ is an entry of $A$ that is a maximum in its row and a minimum in its column. Knuth (1968) gave several different algorithms for finding a saddlepoint. The worst-case running time of these algorithms is $\Theta(n^2)$, and Llewellyn, Tovey, and Trick (1988) showed that this cannot be improved, as in the worst case all entries of A may need to be queried. A strict saddlepoint of $A$ is an entry that is the strict maximum in its row and the strict minimum in its column. The strict saddlepoint (if it exists) is unique, and Bienstock, Chung, Fredman, Sch\"affer, Shor, and Suri (1991) showed that it can be found in time $O(n \log{n})$, where a dominant runtime contribution is sorting the diagonal of the matrix. This upper bound has not been improved since 1991. In this paper we show that the strict saddlepoint can be found in $O(n \log^{*}{n})$ time, where $\log^{*}$ denotes the very slowly growing iterated logarithm function, coming close to the lower bound of $\Omega(n)$. In fact, we can also compute, within the same runtime, the value of a non-strict saddlepoint, assuming one exists. Our algorithm is based on a simple recursive approach, a feasibility test inspired by searching in sorted matrices, and a relaxed notion of saddlepoint.
QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Mixture-of-Experts (MoE) architectures offer a general solution to the high inference costs of large language models (LLMs) via sparse routing, bringing faster and more accurate models, at the cost of massive parameter counts. For example, the SwitchTransformer-c2048 model has 1.6 trillion parameters, requiring 3.2TB of accelerator memory to run efficiently, which makes practical deployment challenging and expensive. In this paper, we present a solution to this memory problem, in form of a new compression and execution framework called QMoE. Specifically, QMoE consists of a scalable algorithm which accurately compresses trillion-parameter MoEs to less than 1 bit per parameter, in a custom format co-designed with bespoke GPU decoding kernels to facilitate efficient end-to-end compressed inference, with minor runtime overheads relative to uncompressed execution. Concretely, QMoE can compress the 1.6 trillion parameter SwitchTransformer-c2048 model to less than 160GB (20x compression, 0.8 bits per parameter) at only minor accuracy loss, in less than a day on a single GPU. This enables, for the first time, the execution of a trillion-parameter model on affordable commodity hardware, like a single server with 4x NVIDIA A6000 or 8x NVIDIA 3090 GPUs, at less than 5% runtime overhead relative to ideal uncompressed inference. The source code and compressed models are available at github.com/IST-DASLab/qmoe.
Aesthetics-Driven Virtual Time-Lapse Photography Generation

Time-lapse videos can visualize the temporal change of dynamic scenes and present wonderful sights with drastic variance in color appearance and rapid movement that interests people. We propose an aesthetics-driven virtual time-lapse photography framework to explore the automatic generation of time-lapse videos in the virtual world, which has potential applications like artistic creation and entertainment in the virtual space. We first define shooting parameters to parameterize the time-lapse photography process and accordingly propose image, video, and time-lapse aesthetic assessments to optimize these parameters, enabling the process to be autonomous and adaptive. We also build an interactive interface to visualize the shooting process and help users conduct virtual time-lapse photography by personalizing shooting parameters according to their aesthetic preferences. Finally, we present a two-stream time-lapse aesthetic model and a time-lapse aesthetic dataset, which can evaluate the aesthetic quality of time-lapse videos. Experimental results demonstrate our method can automatically generate time-lapse videos comparable to those of professional photographers and is more efficient.
Scene Text Segmentation with Text-Focused Transformers

Text segmentation is a crucial aspect of various text-related tasks, including text erasing, text editing, and font style transfer. In recent years, multiple text segmentation datasets, such as TextSeg focusing on Latin text segmentation and BTS on bilingual text segmentation, have been proposed. However, existing methods either disregard the annotations of text location or directly use pre-trained text detectors. In general, these methods cannot fully utilize the annotations of text location in the datasets. To explicitly incorporate text location information to guide text segmentation, we propose an end-to-end text-focused segmentation framework, where text detection and segmentation are jointly optimized. In the proposed framework, we first extract multi-level global visual features through residual convolution blocks and then predict the mask of text areas using a text detection head. Subsequently, we develop a text-focused module that compels the model to pay more attention to text areas. Specifically, we introduce two types of attention masks to extract corresponding features: text-aware and instance-aware features. Finally, we employ hierarchical Transformer encoders to fuse multi-level features and predict the text mask with a text segmentation head. To evaluate the effectiveness of our method, we conduct experiments on six text segmentation benchmarks. The experimental results demonstrate that the proposed method outperforms the previous state-of-the-art (SOTA) methods by a clear margin in most cases. The code and supplementary materials are available at https://github.com/FudanVI/FudanOCR/tree/main/text-focused-Transformers https://github.com/FudanVI/FudanOCR/tree/main/text-focused-Transformers.
Karma: Adaptive Video Streaming via Causal Sequence Modeling

Optimal adaptive bitrate (ABR) decision depends on a comprehensive characterization of state transitions that involve interrelated modalities over time including environmental observations, returns, and actions. However, state-of-the-art learning-based ABR algorithms solely rely on past observations to decide the next action. This paradigm tends to cause a chain of deviations from optimal action when encountering unfamiliar observations, which consequently undermines the model generalization.
An Intelligent Learning Approach to Achieve Near-Second Low-Latency Live Video Streaming under Highly Fluctuating Networks

Fueled by the rapid advances in high-speed mobile networks, live video streaming has seen explosive growth in recent years and many DASH-based bitrate adaptive streaming algorithms were specifically proposed for low-latency video delivery. However, our investigations revealed that these algorithms are susceptible to network condition changes due to the use of solo universal adaptation logics, resulting the playback latency that has substantial variations across highly-fluctuating network environments and fails to meet the service quality requirement all the time. To tackle this challenge, this paper proposes Stateful Live Video Streaming (SLVS), which is a novel learning approach that learns the various network features and optimizes the adaptation logic separately for different network conditions, then dynamically tunes the logic at runtime, so that bitrate decision can better match the changing networks. Extensive evaluations show that SLVS can control playback latency down to 1s while improving Quality-of-Experience (QoE) by 17.7% to 31.8%. Moreover, it has strong robustness to maintain near-second latency over highly-fluctuating networks as well as long-period of video viewing.
Modeling and statistical study of a DBN for Cloud and Cloudlet environment within IoT technologies

Abstract Cloud Computing plays a major role in this, in particular by making all connected devices work together. Virtual machine (VM) migration concept and the architecture of datacenters for cloud computing have a significant impact on latency and energy cost. In order to lower latency and energy usage while maintaining SLA restrictions, this work focuses on how to employ VM migration to achieve steady physical machine use. Using the novel MVMM scheduler method for virtual machine migration, we suggest modeling and statistical analysis of a Dynamic Bayesian Network for cloud and cloudlet environments within IoT technologies. It use a Dynamic Bayesian Network study to decide where and when a particular VM migrates. Indeed, to improve latency and energy consumption by limiting the number of upcoming migrations, the statistical study computes a score for each VM candidate for migration using datacenter metrics as input..
MORE: A Multimodal Object-Entity Relation Extraction Dataset with a Benchmark Evaluation

Extracting relational facts from multimodal data is a crucial task in the field of multimedia and knowledge graphs that feeds into widespread real-world applications. The emphasis of recent studies centers on recognizing relational facts in which both entities are present in one modality and supplementary information is used from other modalities. However, such works disregard a substantial amount of multimodal relational facts that arise across different modalities, such as one entity seen in a text and another in an image. In this paper, we propose a new task, namely Multimodal Object-Entity Relation Extraction, which aims to extract "object-entity" relational facts from image and text data. To facilitate research on this task, we introduce MORE, a new dataset comprising 21 relation types and 20,136 multimodal relational facts annotated on 3,522 pairs of textual news titles and corresponding images. To show the challenges of Multimodal Object-Entity Relation Extraction, we evaluated recent state-of-the-art methods for multimodal relation extraction and conducted a comprehensive experimentation analysis on MORE. Our results demonstrate significant challenges for existing methods, underlining the need for further research on this task. Based on our experiments, we identify several promising directions for future research. The MORE dataset and code are available at https://github.com/NJUNLP/MORE.
Review of OpenStax College Success

Textbook costs remain prohibitive for students, leading many students to delay their purchasing until financial aid has been disbursed, sharing or pirating their books, or, in 43% of cases, foregoing the book (Florida Virtual Campus, 2012).Other students rely on older versions of their textbooks, whether they purchased them or rent them from the library on an intermittent basis.This creates a gap between how the class is planned to be taught and how it is actually
Bash Comment Generation via Data Augmentation and Semantic-Aware CodeBERT

Abstract Understanding Bash code is challenging for developers due to its syntax flexibility and unique features. Bash lacks sufficient training data compared to comment generation tasks in popular programming languages. Furthermore, collecting more real Bash code and corresponding comments is time-consuming and labor-intensive. In this study, we propose a two-module method named Bash2Com for Bash code comments generation. The first module, NP-GD, is a gradient-based automatic data augmentation component that enhances normalization stability when generating adversarial examples. The second module, MASA, leverages CodeBERT to learn the rich semantics of Bash code. Specifically, MASA considers the representations learned at each layer of CodeBERT as a set of semantic information that captures recursive relationships within the code. To generate comments for different Bash snippets, MASA employs LSTM and attention mechanisms to dynamically concentrate on relevant representational information. Then, we utilize the Transformer decoder and beam search algorithm to generate code comments. To evaluate the effectiveness of Bash2Com, we consider a corpus of 10,592 Bash code and Semantic-Aware CodeBERT and corresponding comments. Compared with the state-of-the-art base-lines, our experimental results show that Bash2Com can outperform all baselines by at least 10.19%, 11.81%, 2.61%, and 6.13% in terms of the performance measures BLEU-3/4, METEOR, and ROUGR-L. Moreover, the rationality of NP-GD and MASA in Bash2Com are verified by ablation studies. Finally, we conduct a human evaluation to illustrate the effectiveness of Bash2Com from practitioners’ perspectives.
Automated Visual Quality Assessment for Virtual and Augmented Reality based Digital Twins

Abstract Virtual and augmented reality digital twins are becoming increasingly prevalent in a number of industries, though the production of digital-twin systems applications is still prohibitively expensive for many smaller organisations. A key step towards reducing the cost of digital twins lies in automating the production of 3D assets, however efforts are complicated by the lack of suitable automated methods for determining the visual quality of these assets. While visual quality assessment has been an active area of research for a number of years, few publications consider this process in the context of asset creation in digital twins. In this work, we introduce a novel set of decimation algorithms to assess the visual impact of decimation, a process commonly used in the production of 3D assets which has thus far been underrepresented in the visual assessment literature. Our model combines 108 geometric and perceptual metrics to determine if a 3D object has been unacceptably distorted during decimation. Our model is trained on almost 4,000 distorted meshes, giving a significantly wider range of applicability than many models in the literature. Our results show a precision of over 97% against a set of test models, and performance tests show our model is capable of performing assessments within 2 minutes on models of up to 25,000 polygons. Based on these results we believe our model presents both a significant advance in the field of visual quality assessment and an important step towards reducing the cost of virtual and augmented reality-based digital-twins.
Cognitive behavioral therapy and applied mathematics education reform on students’ cognitive impairment

Background With more attention to students’ mental health, cognitive behavioral therapy and applied mathematics education reform are combined to study the impact of cognitive disorders on students’ psychological problems caused by depression, anxiety disorder, and irrational cognition. Subjects and Methods Data processing was conducted using SPSS26.0 and Excel software to analyze the overall situation of students’ cognitive impairment in applied mathematics before and after the educational reform under cognitive behavioral therapy. The research explores the correlation between applied mathematics scores and cognitive impairment, and analyzes the impact of cognitive behavioral therapy combined with applied mathematics education reform on students’ cognitive impairment. Results In the process of learning applied mathematics, students in the same grade have moderate cognitive impairment. Students who did not receive intervention treatment had a significant degree of cognitive impairment, and the correlation coefficient between applied mathematics scores and cognitive impairment was 0.991. Students who undergo cognitive behavioral therapy but do not consider the application of mathematical teaching reform have moderate cognitive impairment. Students who combine cognitive behavioral therapy with applied mathematics education reform have mild cognitive impairment. At this time, the correlation coefficient between applied mathematics scores and cognitive impairment is 0.893, with a significance level of 0.00&lt;0.01, indicating a strong correlation. Conclusions Cognitive behavioral therapy and applied mathematics education reform have a positive impact on students’ cognitive impairment, which has good practical value for the treatment of cognitive impairment.
Emotion Intensity Value Prediction with Machine Learning Approach on Twitter

Recognizing the intensity of the emotions is a paramount task for an affective system. By recognizing the intensity of the emotions, the system can have better human-computer interaction. The research explores several machine learning approaches with several different feature extraction method combinations to solve the emotion intensity prediction task while also analyzing and comparing it with several previous related papers. The research uses the dataset provided through theWASSA 2017 and SemEval 2018 competition. The dataset utilizes four of the eight basic emotions that Plutchik defines (anger, fear, joy, and sadness). The total data result in 19,736 rows of entry, with a total of 10,715 (54.3%) for training, 1,811 (9.17%) for validation, and 7,210 (36.53%) for testing. Three feature extraction methods are used and compared: N-gram, TFIDF, and Bag-of-Words. Meanwhile, machine learning algorithms are Linear Regression, Ridge Regression, KNearest Neighbor for Regression, Regression Tree, and Support Vector Regression (SVR). The results show that SVR with TF-IDF features has the best result of all attempted experiments, with a Pearson correlation score of 0.755 for all data and 0.647 for gold labels data. The final model also accepts newly seen data and displays the corresponding emotion label and intensity.