Miracle-dz/UniFAR-FTUs huggingface dataset:
Opportunities and Challenges in Neural Dialog Tutoring

Designing dialog tutors has been challenging as it involves modeling the diverse and complex pedagogical strategies employed by human tutors. Although there have been significant recent advances in neural conversational systems using large language models and growth in available dialog corpora, dialog tutoring has largely remained unaffected by these advances. In this paper, we rigorously analyze various generative language models on two dialog tutoring datasets for language learning using automatic and human evaluations to understand the new opportunities brought by these advances as well as the challenges we must overcome to build models that would be usable in real educational settings.We find that although current approaches can model tutoring in constrained learning scenarios when the number of concepts to be taught and possible teacher strategies are small, they perform poorly in less constrained scenarios.Our human quality evaluation shows that both models and ground-truth annotations exhibit low performance in terms of equitable tutoring, which measures learning opportunities for students and how engaging the dialog is.To understand the behavior of our models in a real tutoring setting, we conduct a user study using expert annotators and find a significantly large number of model reasoning errors in 45% of conversations. Finally, we connect our findings to outline future work.
Prostate Lesion Estimation using Prostate Masks from Biparametric MRI

Biparametric MRI has emerged as an alternative to multi-parametric prostate MRI, which eliminates the need for the potential harms to the patient due to the contrast medium. One major issue with biparametric MRI is diﬃculty to detect clinically signiﬁcant prostate cancer (csPCA). Deep learning algorithms have emerged as an alternative solution to detect csPCA in cohort studies. We present a workﬂow which predicts csPCA on biparametric prostate MRI PI-CAI 2022 Challenge with over 10,000 carefully-curated prostate MRI exams. We propose to to segment the prostate gland ﬁrst to the central gland (transition + central zone) and the peripheral gland. Then we utilize these predcitions in combination with T2, ADC and DWI images to train an ensemble nnU-Net model. Finally, we utilize clinical indices PSA and ADC intensity distributions of lesion regions to reduce the false positives. Our method achieves top results on open-validation stage with a AUROC of 0.888 and AP of 0.732.
Oscillometric Blood Pressure Measurement Using a Hybrid Deep Morpho-Temporal Representation Learning Framework

— Objective : Oscillometric monitors are the most common automated blood pressure (BP) measurement devices used in non-specialist settings. However, their accuracy and reliability vary under different settings and for different age groups and health conditions. A main limitation of the existing oscillometric monitors is their underlying analysis algorithms that are unable to fully capture the BP information encoded in the pattern of the recorded oscillometric pulses. Methods : In this paper, we propose a new 2D oscillometric data representation that enables a full characterization of arterial system and empowers the application of deep learning to extract the most informative features correlated with BP. A hybrid convolutional-recurrent neural network was developed to capture the oscillometric pulses morphological information as well as their temporal evolution over the cuff deflation period from the 2D structure, and estimate BP. Results : The performance of the proposed method was verified on three oscillometric databases collected from the wrist and upper arms of 245 individuals. It was found that it achieves a mean error and a standard deviation of error of as low as 0.08 mmHg and 2.4 mmHg in the estimation of systolic BP, and 0.04 mmHg and 2.2 mmHg in the estimation of diastolic BP, respectively. Conclusion : Our proposed method outperformed the state-of-the-art techniques and satisfied the current international standards for BP monitors by a wide margin. Significance : The proposed method shows promise toward robust and objective BP estimation in a variety of patients and monitoring situations.
Using I4.0 digital twins in agriculture

Agriculture is a huge domain where an enormous landscape of systems interact to support agricultural processes, which are becoming increasingly digital. From the perspective of agricultural service providers, a prominent challenge is interoperability. In the Fraunhofer lighthouse project Cognitive Agriculture (COGNAC), we investigated how the usage of Industry 4.0 digital twins (I4.0 DTs) can help overcome this challenge. This paper contributes architecture drivers and a solution concept using I4.0 DTs in the agricultural domain. Furthermore, we discuss the opportunities and limitations offered by I4.0 DTs for the agricultural domain.
An Optimal Control Strategy for Execution of Large Stock Orders Using LSTMs

In this paper, we simulate the execution of a large stock order with real data and general power law in the Almgren and Chriss model. The example that we consider is the liquidation of a large position executed over the course of a single trading day in a limit order book. Transaction costs are incurred because large orders walk the order book, that is, they consume order book liquidity beyond the best bid/ask. We model the order book with a power law that is proportional to trading volume, and thus transaction costs are inversely proportional to a power of trading volume. We obtain a policy approximation by training a long short term memory (LSTM) neural network to minimize transaction costs accumulated when execution is carried out as a sequence of smaller suborders. Using historical S&P100 price and volume data, we evaluate our LSTM strategy relative to strategies based on time-weighted average price (TWAP) and volume-weighted average price (VWAP). For execution of a single stock, the input to the LSTM is the cross section of data on all 100 stocks, including prices, volumes, TWAPs and VWAPs. By using this data cross section, the LSTM should be able to exploit inter-stock co-dependence in volume and price movements, thereby reducing transaction costs for the day. Our tests on S&P100 data demonstrate that in fact this is so, as our LSTM strategy consistently outperforms TWAP and VWAP-based strategies.
Standardization allows for efficient unbiased estimation in observational studies and in indirect treatment comparisons: A comprehensive simulation study

We evaluate how inverse probability of treatment weighting (IPTW) and standardization-based approaches compare for obtaining marginal estimates of the odds-ratio and the hazards ratio. Specifically, we consider how the two methods compare in two different scenarios: (1) in a single comparative study (either randomized or non-randomized), and (2) in an anchored indirect treatment comparison of randomized controlled trials. For each scenario, we conduct a simulation study to investigate the relative efficiency of each method and the potential for bias. We conclude that standardization-based procedures may be more efficient at providing unbiased estimates than those based on IPTW.
Earthquake Magnitude and b value prediction model using Extreme Learning Machine

Earthquake prediction has been a challenging research area for many decades, where the future occurrence of this highly uncertain calamity is predicted. In this paper, several parametric and non-parametric features were calculated, where the non-parametric features were calculated using the parametric features. $8$ seismic features were calculated using Gutenberg-Richter law, the total recurrence, and the seismic energy release. Additionally, criterions such as Maximum Relevance and Maximum Redundancy were applied to choose the pertinent features. These features along with others were used as input for an Extreme Learning Machine (ELM) Regression Model. Magnitude and time data of $5$ decades from the Assam-Guwahati region were used to create this model for magnitude prediction. The Testing Accuracy and Testing Speed were computed taking the Root Mean Squared Error (RMSE) as the parameter for evaluating the mode. As confirmed by the results, ELM shows better scalability with much faster training and testing speed (up to a thousand times faster) than traditional Support Vector Machines. The testing RMSE came out to be around $0.097$. To further test the model's robustness -- magnitude-time data from California was used to calculate the seismic indicators which were then fed into an ELM and then tested on the Assam-Guwahati region. The model proves to be robust and can be implemented in early warning systems as it continues to be a major part of Disaster Response and management.
What Drives Virtual Influencer's Impact?

In the midst of the influencer marketing boom, more and more companies are shifting resources from real to virtual (or computer-generated) influencers. But while virtual influencers have the potential to engage consumers and drive action, some posts resonate and boost sales, while others do not. What makes some virtual influencer posts more impactful? This work examines how including someone else in photos shapes consumer responses to virtual influencers’ posts. A multimethod investigation, combining automated image and text analysis of thousands of social media posts with controlled experiments, demonstrates that companion presence boosts impact. These effects are driven by trust. Companion presence makes virtual influencers seem more human, which makes them seem more trustworthy, and thus increases the impact of their posts. Taken together, the findings shed light on how others’ presence shapes responses to virtual influencer content, reveal a psychological mechanism through which companions affect consumer perceptions, and provide actionable insights for designing more impactful social media content.
Fair and skill-diverse student group formation via constrained k-way graph partitioning

Forming the right combination of students in a group promises to enable a powerful and effective environment for learning and collaboration. However, defining a group of students is a complex task which has to satisfy multiple constraints. This work introduces an unsupervised algorithm for fair and skill-diverse student group formation. This is achieved by taking account of student course marks and sensitive attributes provided by the education office. The skill sets of students are determined using unsupervised dimensionality reduction of course mark data via the Laplacian eigenmap. The problem is formulated as a constrained graph partitioning problem, whereby the diversity of skill sets in each group are maximised, group sizes are upper and lower bounded according to available resources, and `balance' of a sensitive attribute is lower bounded to enforce fairness in group formation. This optimisation problem is solved using integer programming and its effectiveness is demonstrated on a dataset of student course marks from Imperial College London.
Quantitative nonlinear spectroscopy: a direct comparison of degenerate four-wave mixing with cavity ring-down spectroscopy applied to NaH

Cavity ring-down spectroscopy and degenerate four-wave mixing have been applied for spectroscopic studies, temperature determination, and measurement of relative dipole transition moments of photochemically produced sodium hydride. In our experiment, NaH was formed within a heat-pipe oven after 3p excitation of sodium in a hydrogen atmosphere with a second dye laser. The reaction product NaH was probed in the near ultraviolet part of the spectrum at 382 nm with 1-4-/spl mu/s delay to the excitation pulse using both a linear and nonlinear spectroscopic technique with a pulsed dye laser source (pulsewidth 15-ns full-width half-maximum). The strengths and limitations of these two spectroscopic techniques are discussed. Since the two processes depend on different parameters and the image-forming beams have different properties, it is difficult to define a single criterion for comparison. But our measurements indicate that cavity ring-down spectroscopy is a powerful tool with a sensitivity better than 10/sup 9/ particles per cm/sup 3/ and quantum state which is comparable to degenerate four-wave mixing in our case.
Flow Cytometry with Anti-Diffraction Light Sheet (ADLS) by Spatial Light Modulation

Flow cytometry is a widespread and powerful technique whose resolution is determined by its capacity to accurately distinguish fluorescently positive populations from negative ones. However, most informative results are discarded while performing the measurements of conventional flow cytometry, e.g., the cell size, shape, morphology, and distribution or location of labeled exosomes within the unpurified biological samples. Herein, we propose a novel approach using an anti-diffraction light sheet with anisotroic feature to excite fluorescent tags. Constituted by an anti-diffraction Bessel–Gaussian beam array, the light sheet is 12 μm wide, 12 μm high, and has a thickness of ~0.8 μm. The intensity profile of the excited fluorescent signal can, therefore, reflect the size and allow samples in the range from O (100 nm) to 10 μm (e.g., blood cells) to be transported via hydrodynamic focusing in a microfluidic chip. The sampling rate is 500 kHz, which provides a capability of high throughput without sacrificing the spatial resolution. Consequently, the proposed anti-diffraction light sheet flow cytometry (ADLSFC) can obtain more informative results than the conventional methodologies, and is able to provide multiple characteristics (e.g., the size and distribution of fluorescent signal) helping to distinguish the target samples from the complex backgrounds.
Differentiable Forward Kinematics for TensorFlow 2

Robotic systems are often complex and depend on the integration of a large number of software components. One important component in robotic systems provides the calculation of forward kinematics, which is required by both motion-planning and perception related components. End-to-end learning systems based on deep learning require passing gradients across component boundaries.Typical software implementations of forward kinematics are not differentiable, and thus prevent the construction of gradient-based, end-to-end learning systems. In this paper we present a library compatible with ROS-URDF that computes forward kinematics while simultaneously giving access to the gradients w.r. t. joint configurations and model parameters, allowing gradient-based learning and model identification. Our Python library is based on Tensorflow~2 and is auto-differentiable. It supports calculating a large number of kinematic configurations on the GPU in parallel, yielding a considerable performance improvement compared to sequential CPU-based calculation. https://github.com/lumoe/dlkinematics.git
Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets

Recent advances in computer vision have led to significant progress in the generation of realistic image data, with denoising diffusion probabilistic models proving to be a particularly effective method. In this study, we demonstrate that diffusion models can effectively generate fully-annotated microscopy image data sets through an unsupervised and intuitive approach, using rough sketches of desired structures as the starting point. The proposed pipeline helps to reduce the reliance on manual annotations when training deep learning-based segmentation approaches and enables the segmentation of diverse datasets without the need for human annotations. We demonstrate that segmentation models trained with a small set of synthetic image data reach accuracy levels comparable to those of generalist models trained with a large and diverse collection of manually annotated image data, thereby offering a streamlined and specialized application of segmentation models.
Practical Synchronization for OTFS

In the existing literature on joint timing and frequency synchronization of orthogonal time frequency space modulation (OTFS), practically infeasible impulse pilot with large peak-to-average power ratio (PAPR) is deployed. Hence, in this paper, we propose a timing offset (TO) and carrier frequency offset (CFO) estimation for OTFS over a linear time-varying (LTV) channel, using a low PAPR pilot structure. The proposed technique utilizes the recently proposed practically feasible pilot structure with a cyclic prefix (PCP). We exploit the periodic properties of PCP in both delay and time domains to find the starting point of each OTFS block. Furthermore, we propose a two-stage CFO estimation technique with over an order of magnitude higher estimation accuracy than the existing estimator using the impulse pilot. In the first stage, a coarse CFO estimate is obtained which is refined in the second stage, through our proposed maximum likelihood (ML) based approach. The proposed ML-based approach deploys the generalized complex exponential basis expansion model (GCE-BEM) to capture the time variations of the channel, absorb them into the pilot and provide an accurate CFO estimate. Since our proposed synchronization technique utilizes the same pilot deployed for channel estimation, it does not require any additional overhead. Finally, we evaluate the performance of our proposed synchronization technique through simulations. We also compare and show the superior performance of our proposed technique to the only other existing joint TO and CFO estimation method in OTFS literature.
Intrinsic Motivation in Model-based Reinforcement Learning: A Brief Review

The reinforcement learning research area contains a wide range of methods for solving the problems of intelligent agent control. Despite the progress that has been made, the task of creating a highly autonomous agent is still a significant challenge. One potential solution to this problem is intrinsic motivation, a concept derived from developmental psychology. This review considers the existing methods for determining intrinsic motivation based on the world model obtained by the agent. We propose a systematic approach to current research in this field, which consists of three categories of methods, distinguished by the way they utilize a world model in the agent's components: complementary intrinsic reward, exploration policy, and intrinsically motivated goals. The proposed unified framework describes the architecture of agents using a world model and intrinsic motivation to improve learning. The potential for developing new techniques in this area of research is also examined.
SN 2020uem: a Possible Thermonuclear Explosion within a Dense Circumstellar Medium. I. The Nature of Type IIn/Ia-CSM SNe from Photometry and Spectroscopy

We have performed intensive follow-up observations of a Type IIn/Ia-CSM supernova (SN IIn/Ia-CSM), 2020uem, with photometry, spectroscopy, and polarimetry. In this paper, we report on the results of our observations focusing on optical/near-infrared (NIR) photometry and spectroscopy. The maximum V-band magnitude of SN 2020uem is less than −19.5 mag. The light curves decline slowly with a rate of ∼0.75 mag/100 days. In the late phase (≳300 days), the light curves show accelerated decay (∼1.2 mag/100 days). The optical spectra show prominent hydrogen emission lines and broad features possibly associated with Fe-peak elements. In addition, the Hα profile exhibits a narrow P-Cygni profile with an absorption minimum of ∼100 km s−1. SN 2020uem shows a higher Hα/Hβ ratio (∼7) than those of SNe IIn, which suggests a denser circumstellar medium (CSM). The NIR spectrum shows the Paschen and Brackett series with a continuum excess in the H and Ks bands. We conclude that the NIR excess emission originates from newly formed carbon dust. The dust mass (M d) and temperature (T d) are derived to be (M d, T d) ∼ (4−7 × 10−5 M ⊙, 1500–1600 K). We discuss the differences and similarities between the observational properties of SNe IIn/Ia-CSM and those of other SNe Ia and interacting SNe. In particular, spectral features around ∼4650 Å and ∼5900 Å of SNe IIn/Ia-CSM are more suppressed than those of SNe Ia; these lines are possibly contributed, at least partly, by Mg i] and Na i, and may be suppressed by high ionization behind the reverse shock caused by the massive CSM.
Multi-view Kernel PCA for Time series Forecasting

In this paper, we propose a kernel principal component analysis model for multi-variate time series forecasting, where the training and prediction schemes are derived from the multi-view formulation of Restricted Kernel Machines. The training problem is simply an eigenvalue decomposition of the summation of two kernel matrices corresponding to the views of the input and output data. When a linear kernel is used for the output view, it is shown that the forecasting equation takes the form of kernel ridge regression. When that kernel is non-linear, a pre-image problem has to be solved to forecast a point in the input space. We evaluate the model on several standard time series datasets, perform ablation studies, benchmark with closely related models and discuss its results.
Solving Quantum-Inspired Perfect Matching Problems via Tutte's Theorem-Based Hybrid Boolean Constraints

Determining the satisfiability of Boolean constraint-satisfaction problems with different types of constraints, that is hybrid constraints, is a well-studied problem with important applications. We study a new application of hybrid Boolean constraints, which arises in quantum computing. The problem relates to constrained perfect matching in edge-colored graphs. While general-purpose hybrid constraint solvers can be powerful, we show that direct encodings of the constrained-matching problem as hybrid constraints scale poorly and special techniques are still needed. We propose a novel encoding based on Tutte's Theorem in graph theory as well as optimization techniques. Empirical results demonstrate that our encoding, in suitable languages with advanced SAT solvers, scales significantly better than a number of competing approaches on constrained-matching benchmarks. Our study identifies the necessity of designing problem-specific encodings when applying powerful general-purpose constraint solvers.
Proactive and reactive engagement of artificial intelligence methods for education: a review

The education sector has benefited enormously through integrating digital technology driven tools and platforms. In recent years, artificial intelligence based methods are being considered as the next generation of technology that can enhance the experience of education for students, teachers, and administrative staff alike. The concurrent boom of necessary infrastructure, digitized data and general social awareness has propelled these efforts further. In this review article, we investigate how artificial intelligence, machine learning, and deep learning methods are being utilized to support the education process. We do this through the lens of a novel categorization approach. We consider the involvement of AI-driven methods in the education process in its entirety—from students admissions, course scheduling, and content generation in the proactive planning phase to knowledge delivery, performance assessment, and outcome prediction in the reactive execution phase. We outline and analyze the major research directions under proactive and reactive engagement of AI in education using a representative group of 195 original research articles published in the past two decades, i.e., 2003–2022. We discuss the paradigm shifts in the solution approaches proposed, particularly with respect to the choice of data and algorithms used over this time. We further discuss how the COVID-19 pandemic influenced this field of active development and the existing infrastructural challenges and ethical concerns pertaining to global adoption of artificial intelligence for education.