Miracle-dz/UniFAR-FTUs huggingface dataset:
Adjustable Molecular Representation for Unified Pre-training Strategy

We propose a new large-scale molecular model, named AdaMR, which stands for Adjustable Molecular Representation for Unified Pre-training Strategy. Unlike recent large-scale molecular models that use a single molecular encoding, AdaMR employs a granularity-adjustable molecular encoder, learning molecular representations at both the atomic and substructure levels. For the pre-training process, we designed a task for molecular canonicalization, which involves transforming ltiple generic molecular representations into canonical representations. By adjusting the granularity of molecular encoding, the trained model can improve the effects on multiple downstream tasks, such as model attribute prediction and molecule generation. Substructure-level molecular representation retains information of specific atom groups or arrangements that determine chemical properties and have similar functions, which is beneficial for tasks like property prediction. Meanwhile, atomic-level representation, combined with generative molecular canonicalization pre-training tasks, enhances the validity, novelty, and uniqueness in generative tasks. These features of AdaMR demonstrate its strong performance in numerous downstream tasks. We use different molecular properties prediction tasks on six different datasets on MoleculeNet and two generative tasks on ZINC250K dataset to evaluate our proposed molecular encoding and pre-training methods, and obtain state-of-the-art (SOTA) results on five of these tasks.
Iterated power intersections of ideals in rings of iterated differential polynomials II

This paper is a continuation of a previous work by the author and G. Puninski where iterated intersections of powers of ideals were studied in rings of iterated differential polynomials. We present a method which can be used to show that for every proper ideal $I$ of a suitable ring of iterated differential polynomials almost all iterated intersections of powers of $I$ have to be zero. The setback of the method is that it works only if the derivations used in the construction of the ring of iterated differential polynomials satisfy additional assumptions. On the other hand, it can be applied in cases which were not covered by the aforementioned work, for example for a universal enveloping algebra of a completely solvable Lie algebra over a field of positive characteristic.
Design and Nonlinear Modeling of a Modular Cable Driven Soft Robotic Arm

We propose a novel multi-section cable-driven soft robotic arm inspired by octopus tentacles along with a new modeling approach. Each section of the modular manipulator is made of a soft tubing backbone, a soft silicon arm body, and two rigid endcaps, which connect adjacent sections and decouple the actuation cables of different sections. The soft robotic arm is made with casting after the rigid endcaps are 3D-printed, achieving low-cost and convenient fabrication. To capture the nonlinear effect of cables pushing into the soft silicon arm body, which results from the absence of intermediate rigid cable guides for higher compliance, an analytical static model is developed to capture the relationship between the bending curvature and the cable lengths. The proposed model shows superior prediction performance in experiments over that of a baseline model, especially under large bending conditions. Based on the nonlinear static model, a kinematic model of a multi-section arm is further developed and used to derive a motion planning algorithm. Experiments show that the proposed soft arm has high flexibility and a large workspace, and the tracking errors under the algorithm based on the proposed modeling approach are up to 52$\%$ smaller than those with the algorithm derived from the baseline model. The presented modeling approach is expected to be applicable to a broad range of soft cable-driven actuators and manipulators.
Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization

While modern biotechnologies allow synthesizing new proteins and function measurements at scale, efficiently exploring a protein sequence space and engineering it remains a daunting task due to the vast sequence space of any given protein. Protein engineering is typically conducted through an iterative process of adding mutations to the wild-type or lead sequences, recombination of mutations, and running new rounds of screening. To enhance the efficiency of such a process, we propose a tree search-based bandit learning method, which expands a tree starting from the initial sequence with the guidance of a bandit machine learning model. Under simplified assumptions and a Gaussian Process prior, we provide theoretical analysis and a Bayesian regret bound, demonstrating that the method can efficiently discover a near-optimal design. The full algorithm is compatible with a suite of randomized tree search heuristics, machine learning models, pre-trained embeddings, and bandit techniques. We test various instances of the algorithm across benchmark protein datasets using simulated screens. Experiment results demonstrate that the algorithm is both sample-efficient, diversity-promoting, and able to find top designs using reasonably small mutation counts.
PCB-Vision: A Multiscene RGB-Hyperspectral Benchmark Dataset of Printed Circuit Boards

Addressing the critical theme of recycling electronic waste (E-waste), this contribution is dedicated to developing advanced automated data processing pipelines as a basis for decision-making and process control. Aligning with the broader goals of the circular economy and the United Nations (UN) Sustainable Development Goals (SDG), our work leverages non-invasive analysis methods utilizing RGB and hyperspectral imaging data to provide both quantitative and qualitative insights into the E-waste stream composition for optimizing recycling efficiency. In this paper, we introduce 'PCB-Vision'; a pioneering RGB-hyperspectral printed circuit board (PCB) benchmark dataset, comprising 53 RGB images of high spatial resolution paired with their corresponding high spectral resolution hyperspectral data cubes in the visible and near-infrared (VNIR) range. Grounded in open science principles, our dataset provides a comprehensive resource for researchers through high-quality ground truths, focusing on three primary PCB components: integrated circuits (IC), capacitors, and connectors. We provide extensive statistical investigations on the proposed dataset together with the performance of several state-of-the-art (SOTA) models, including U-Net, Attention U-Net, Residual U-Net, LinkNet, and DeepLabv3+. By openly sharing this multi-scene benchmark dataset along with the baseline codes, we hope to foster transparent, traceable, and comparable developments of advanced data processing across various scientific communities, including, but not limited to, computer vision and remote sensing. Emphasizing our commitment to supporting a collaborative and inclusive scientific community, all materials, including code, data, ground truth, and masks, will be accessible at https://github.com/hifexplo/PCBVision.
Automated Test Case Repair Using Language Models

Ensuring the quality of software systems through testing is essential, yet maintaining test cases poses significant challenges and costs. The need for frequent updates to align with the evolving system under test often entails high complexity and cost for maintaining these test cases. Further, unrepaired broken test cases can degrade test suite quality and disrupt the software development process, wasting developers' time. To address this challenge, we present TaRGet (Test Repair GEneraTor), a novel approach leveraging pre-trained code language models for automated test case repair. TaRGet treats test repair as a language translation task, employing a two-step process to fine-tune a language model based on essential context data characterizing the test breakage. To evaluate our approach, we introduce TaRBench, a comprehensive benchmark we developed covering 45,373 broken test repairs across 59 open-source projects. Our results demonstrate TaRGet's effectiveness, achieving a 66.1% exact match accuracy. Furthermore, our study examines the effectiveness of TaRGet across different test repair scenarios. We provide a practical guide to predict situations where the generated test repairs might be less reliable. We also explore whether project-specific data is always necessary for fine-tuning and if our approach can be effective on new projects.
LMI-based robust model predictive control for a quarter car with series active variable geometry suspension

This paper proposes a robust model predictive control-based solution for the recently introduced series active variable geometry suspension (SAVGS) to improve the ride comfort and road holding of a quarter car. In order to close the gap between the nonlinear multi-body SAVGS model and its linear equivalent, a new uncertain system characterization is proposed that captures unmodeled dynamics, parameter variation, and external disturbances. Based on the newly proposed linear uncertain model for the quarter car SAVGS system, a constrained optimal control problem (OCP) is presented in the form of a linear matrix inequality (LMI) optimization. More specifically, utilizing semidefinite relaxation techniques a state-feedback robust model predictive control (RMPC) scheme is presented and integrated with the nonlinear multi-body SAVGS model, where state-feedback gain and control perturbation are computed online to optimise performance, while physical and design constraints are preserved. Numerical simulation results with different ISO-defined road events demonstrate the robustness and significant performance improvement in terms of ride comfort and road holding of the proposed approach, as compared to the conventional passive suspension, as well as, to actively controlled SAVGS by a previously developed conventional H-infinity control scheme.
Capturing short-range order in high-entropy alloys with machine learning potentials

Chemical short-range order (SRO) affects the distribution of elements throughout the solid-solution phase of metallic alloys, thereby modifying the background against which microstructural evolution occurs. Investigating such chemistry-microstructure relationships requires atomistic models that act at the appropriate length scales while capturing the intricacies of chemical bonds leading to SRO. Here we consider various approaches for the construction of training data sets for machine learning potentials (MLPs) for CrCoNi and evaluate their performance in capturing SRO and its effects on materials quantities of relevance for mechanical properties, such as stacking-fault energy and phase stability. It is demonstrated that energy accuracy on test sets often does not correlate with accuracy in capturing material properties, which is fundamental in enabling large-scale atomistic simulations of metallic alloys with high physical fidelity. Based on this analysis we systematically derive design principles for the rational construction of MLPs that capture SRO in the crystal and liquid phases of alloys.
Contrastive Learning With Audio Discrimination For Customizable Keyword Spotting In Continuous Speech

Customizable keyword spotting (KWS) in continuous speech has attracted increasing attention due to its real-world application potential. While contrastive learning (CL) has been widely used to extract keyword representations, previous CL approaches all operate on pre-segmented isolated words and employ only audio-text representations matching strategy. However, for KWS in continuous speech, co-articulation and streaming word segmentation can easily yield similar audio patterns for different texts, which may consequently trigger false alarms. To address this issue, we propose a novel CL with Audio Discrimination (CLAD) approach to learning keyword representation with both audio-text matching and audio-audio discrimination ability. Here, an InfoNCE loss considering both audio-audio and audio-text CL data pairs is employed for each sliding window during training. Evaluations on the open-source LibriPhrase dataset show that the use of sliding-window level InfoNCE loss yields comparable performance compared to previous CL approaches. Furthermore, experiments on the continuous speech dataset LibriSpeech demonstrate that, by incorporating audio discrimination, CLAD achieves significant performance gain over CL without audio discrimination. Meanwhile, compared to two-stage KWS approaches, the end-to-end KWS with CLAD achieves not only better performance, but also significant speed-up.
A General Benchmark Framework is Dynamic Graph Neural Network Need

Dynamic graph learning is crucial for modeling real-world systems with evolving relationships and temporal dynamics. However, the lack of a unified benchmark framework in current research has led to inaccurate evaluations of dynamic graph models. This paper highlights the significance of dynamic graph learning and its applications in various domains. It emphasizes the need for a standardized benchmark framework that captures temporal dynamics, evolving graph structures, and downstream task requirements. Establishing a unified benchmark will help researchers understand the strengths and limitations of existing models, foster innovation, and advance dynamic graph learning. In conclusion, this paper identifies the lack of a standardized benchmark framework as a current limitation in dynamic graph learning research . Such a framework will facilitate accurate model evaluation, drive advancements in dynamic graph learning techniques, and enable the development of more effective models for real-world applications.
TraffNet: Learning Causality of Traffic Generation for What-if Prediction

Real-time what-if traffic prediction is crucial for decision making in intelligent traffic management and control. Although current deep learning methods demonstrate significant advantages in traffic prediction, they are powerless in what-if traffic prediction due to their nature of correlation-based. Here, we present a simple deep learning framework called TraffNet that learns the mechanisms of traffic generation for what-if prediction from vehicle trajectory data. First, we use a heterogeneous graph to represent the road network, allowing the model to incorporate causal features of traffic flows, such as Origin-Destination (OD) demands and routes. Next, we propose a method for learning segment representations, which involves modeling the process of assigning OD demands onto the road network. The learned segment representations effectively encapsulate the intricate causes of traffic generation, facilitating downstream what-if traffic prediction. Finally, we conduct experiments on synthetic datasets to evaluate the effectiveness of TraffNet. The code and datasets of TraffNet is available at https://github.com/mayunyi-1999/TraffNet_code. git.
In Support of Pair Programming in the Introductory Computer Science Course

A formal pair programming experiment was run at North Carolina to empirically assess the educational efficacy of the technique in a CS1 course. Results indicate that students who practice pair programming perform better on programming projects and are more likely to succeed by completing the class with a C or better. Student pairs are more self-sufficient which reduces their reliance on the teaching staff. Qualitatively, paired students demonstrate higher order thinking skills than students who work alone. These results are supportive of pair programming as a collaborative learning technique.
Power Factor Correction of Three Phase Rectifier with Interleaved Boost Converter

This paper proposes a method of improving the power factor of a three phase rectifier by using a interleaved boost Converter with Power Factor Correction Controller. When compared to Power Factor Correction and THD minimizations of three phase rectifier with single phase rectifier , Power Factor Correction of three phase rectifier with interleaved boost converter Method is good because here THD is very low . Average Current Control technique has been discussed in this paper. Modelling of the PFC Controller using MATLAB/ Simulink is carried out and the results are verified.
Process to improve expected value of mining operations

This paper presents an improved appraisal process that correctly recognises the increased expected value of mining operations attributable to intelligent management response to changing operating conditions and market prices. It recognises the option value of flexibility in the exploitation of deposits, and thus leads to better selection of properties for finance and development. This evaluation method is based on a computationally efficient procedure for examining the opportunities provided by possible price changes that may occur over the project lifetime. It implicitly looks at all possible future price scenarios using a limited range of typical price profiles, therefore covering the range of possibilities without exploring it exhaustively. It contrasts with conventional analyses that assume that prices do not fluctuate through the lifetime of the project. The procedure was developed in collaboration with experienced mining professionals and actual cases in mining and other extractive industries.
Discerning the contribution of balance and mobility to ambulatory activity in community-dwelling octogenarians: A preliminary report

Adults are encouraged to maintain levels of physical activity throughout their life span. This study describes gait performance and ambulatory activity (as a key component of physical activity) in 15 community-dwelling octogenarians and examines the association between activity measured continuously for 5 days with a tri-axial accelerometer and clinical measures of balance and functional mobility. Outcomes represented macro features of ambulatory activity and included volume, pattern and variability of activity. Micro gait outcomes were derived from each walking bout and represented 14 discrete spatio-temporal characteristics of gait. Participants walked a median of 9,294 steps/day (range 5,121-18,231). For macro outcomes, the strongest associations were established for Timed up and Go (TUG) single and dual task times and mean bout length (rs = -.66, p = 0.006, and -.68, p = 0.005 respectively; Spearman’s rho), and TUG dual task and accumulation of walking bouts (alpha) (α) (rs = -.67, p = 0.006). For micro outcomes, there was a positive correlation between step velocity and the Activities Balance and Confidence Scale (rs = .67 p = 0.006), and a negative correlation between step velocity and TUG single task (rs = .71, p = 0.003). TUG dual task showed a positive association with step time asymmetry (rs = .65 p = 0.008) and swing time asymmetry (rs = .66, p = 0.004). For this group of active octogenarians, associations between ambulatory activity and functional mobility were stronger than for balance. Lord, S. , Isbey, O. , Del-Din, S. , Rochester, L. , Taylor, L. (2018). Discerning the contribution of balance and mobility to ambulatory activity in community-dwelling octogenarians: A preliminary report. New Zealand Journal of Physiotherapy 46(3): 133-138. doi:10.15619/NZJP/46.3. 05
Sour Cream Analogue Supplemented with Red Grape Pomace and Tomato Pomace Extracts: Chemical Composition, Oxidative Stability, and Organoleptic Characteristics

This study was performed to assess the impact of supplementation of red grape pomace and tomato pomace extracts (200, 400, and 600 ppm) on the chemical composition, sensory properties, and oxidative stability of sour cream substitutes kept at 5-8 °C for 4 weeks. Red grape pomace and tomato pomace extracts showed high total phenolic contents (990.60 and 470.20 mg/100g), total flavonoids contains (120.60 and 76.20 mg/100g) and radical scavenging activity (92.40 and 85.30 %), respectively. The supplementation of sour cream with extracts at different concentrations did not appreciably change the levels of fat and total solids, but increased total volatile fatty acids, and slightly decreased the acidity. Treatments with 400 and 600 ppm of grape pomace extract had decreased peroxide, acid, and TBA levels and higher oxidative stability index (Rancimat) at the time of storage at 5°C than other treatments. The study demonstrated that grape pomace and tomato pomace extracts at concentrations of 400 or 600 ppm could be used as natural antioxidants for sour cream analogue. Further studies are recommended to investigate the application of these extracts in other dairy products.
Too simple, too complex, or just right? Advantages, challenges and resolutions for indicators of genetic diversity

Summary: describe criticisms of the genetic diversity indicators from Parties and scientists, and address them clearly; acknowledge things to work on. Only summarize indicators briefly before focusing the bulk of the article on critiques and resolutions . Outcome… policy people can be more conversant on genetic diversity indicators at upcoming meetings. Especially AHTEG, Parties can be prepared to talk about the indicators
JOURNAL ARTICLE PRE-PROOF (as accepted)

Objectives: To evaluate relative expression of genes with the potential to translate environmental stimuli into long-term alterations in the brain - namely Early Growth Response (EGR)1, EGR3, and Cryptochrome Circadian Regulator 2 (CRY2) - in peripheral blood from patients with Bipolar Disorder (BD), Schizophrenia (SZ), Major Depressive Disorder (MDD) and healthy controls (HC). Methods: Thirty individuals ranging from 18 to 60 years were recruited for each group (BD, SZ, MDD or HC) from a Brazilian public hospital. Therefore, individuals’ peripheral blood was collected and EGR1, EGR3 and CRY2 gene expression analyzed by PCR Real Time. Results: EGR1 mRNA levels are significantly lower in psychiatric patients when compared to HC, but there is no difference for EGR3 and CRY2. Exploring the findings for each diagnosis, there is a significant difference between each diagnosis group only for EGR1, which was lower in BD, MDD and SZ as compared to HC. No significant correlations were found between gene expression and clinical features. Conclusions: EGR1 is downregulated in psychiatric patients, regardless of the diagnosis and may be a potential common target in major psychiatric disorders. EGR1, as a transcription factor, modulates many other genes and participates in crucial neuronal and synaptic processes, such as plasticity, neurotransmitters metabolism, vesicular transport and signaling pathways. The study of EGR1 and its upstream regulators in psychiatry might lead to potential new therapeutic targets.
TMotion: Embedded 3D Mobile Input using Magnetic Sensing Technique

We present TMotion, a self-contained 3D input that enables spatial interactions around mobile using a magnetic sensing technique. Using a single magnetometer from the mobile device, we can track the 3D position of the permanent magnet embedded in the prototype along with an inertial measurement unit. By numerically solving non-linear magnetic field equations with known orientation from inertial measurement unit (IMU), we attain a tracking rate greater than 30Hz based solely on the mobile device computation. We describe the working principle of TMotion and example applications illustrating its capability.