Miracle-dz/UniFAR-FTUs huggingface dataset:
Making the InterPlanetary File System ( IPFS ) more reliable

One of the main benefits that distributed file systems have over traditional file systems that are stored on a single device or server is data redundancy. To achieve data redundancy, distributed file systems usually use replication. However, it is debatable whether replication is the best option in distributed settings. Researchers have proposed schemas that have some advantages over replication in certain aspects, for example, lowering the storage overhead, requiring less maintenance effort, and improving recovery capability. One of these schemas is Alpha Entanglement (AE) codes. In this project, we try to integrate IPFS, one of the most widely used distributed file systems nowadays, with AE codes. Users could only use replication in file reliability solutions provided by IPFS, which could be a large storage overhead and resource contention. The intention of this project is to offer one more solution on IPFS for file reliability. With AE codes integrated into IPFS, users could achieve a similar reliability level as replication with less storage and a slight compromise in the bandwidth overhead. Our solution builds on top of both IPFS and IPFS Cluster. Users are able to define and control their file reliability. The evaluation results suggest a 2.7× storage saving, with a maximum 70% more bandwidth usage for file recovery, compared to replication.
Object-oriented modeling of sparse matrix operation in power system software

This paper presents design of a generic sparse matrix model for a class of large sparse linear system over real and complex numbers. Such matrices arise in various power system analysis applications like load flow analysis and short circuit analysis. Template facility of C++ is used to write a generic program on float, double and complex data types. Design of specific algorithm optimization for different power system application modules guarantees numerical stability and efficient sparsity implementation. Graph theory and class are introduced to facilitate and simplify the factorization of sparse matrix. The proposed sparse matrix model reflects an important development toward a truly object oriented power system analysis software.
PREreview of bioRxiv article “Subfamily-specific functionalization of diversified immune receptors in wild barley”

This is a review of Maekawa et al. bioRxiv 293050; doi: https://doi.org/10.1101/352278 posted on June 20, 2018. In this paper, the authors mined the transcriptomes of 50 different accession of wild barley, generating a rich library of natural variants of the MLA immune receptor—a classical nucleotide-binding domain and leucine-rich repeat-containing (NLR) protein. They grouped the MLA variants in two subfamilies with all receptors known to be effective against the powdery mildew fungus grouping in one subfamily.
An efficient multi parametric kernel function for large and small-update methods interior point algorithm for P*(κ)-horizontal linear complementarity problem

In this paper, we propose the first efficient multi parametric kernel function with logarithmic barrier term. A class of polynomial interior-point algorithms for P*(κ)-horizontal linear complementarity problem based on this kernel function, with parameters pi > 0 for all i ∈ 1, 2, , m, are presented. Then by using some simple analysis tools, we present a primal-dual interior point method (IPM) for P*(κ)-horizontal linear complementarity problems based on this kernel function. At the same time, we derive the complexity bounds small and large-update methods, respectively. In particular, if we take many different values of the parameters, we obtain the best known iteration bounds for the algorithms with large- and small-update methods are derived, namely, O((1 + 2κ)√n(log n)log n/ϵ) and O((1 + 2κ)√n log n/ϵ) respectively. We illustrate the performance of the proposed kernel function by some numerical results that are derived by applying our algorithm.
SAMPEX observations of the South Atlantic anomaly secular drift during solar cycles 22–24

It is observed that charged particle intensities are very high near the South Atlantic anomaly (SAA) and are a potential hazard to spacecraft passing through the region. In this study, we examine the secular drift of the SAA location at ∼400–600 km altitude over nearly two solar cycles, using particle count rates to trace the geomagnetic field lines in the region near the SAA. We use data from the Low‐Energy Ion Composition Analyzer sensor on board the SAMPEX (Solar, Anomalous, and Magnetospheric Particle Explorer) spacecraft to measure both the longitudinal and latitudinal drifts of the SAA. We find that the longitudinal drift rate is 0.20 ± 0.04° west per year and that the latitudinal drift rate is 0.11 ± 0.01° south per year. These measurements are compared with the IGRF12 (International Geomagnetic Reference Field) model calculations based on an analysis of magnetic field minima in the region of the SAA. Our results, which are in good agreement with model results and prior measurements when declining spacecraft altitude is taken into account, have important space weather implications.
The Levels of DAHP Synthase, the First Enzyme of the Shikimate Pathway, Are Related to Free Aromatic Amino Acids and Glutamine Content in Nicotiana plumbaginifolia Cell Cultures

Aromatic amino acid homeostasis was investigated in cell suspension cultures of Nicotiana plumbaginifolia and was related to the activity of the first enzyme in aromatic biosynthesis, 3-deoxy-D-arabino-heptulosonate-7-phosphate (DAHP) synthase. An inverse relationship was found between the intracellular content of free phenylalanine, tyrosine and tryptophan and enzyme specific activity levels, suggesting the occurrence of end-product control mechanisms. Two DAHP synthase isogenes are present in wild tobacco that showed a different expression pattern during the culture growth cycle. Intracellular levels of aromatic amino acids were increased or decreased by adding the culture medium with phenylalanine, tyrosine and tryptophan, or with sublethal doses of the shikimate pathway inhibitor glyphosate, respectively. As a consequence, enzyme levels varied in the opposite direction. The concomitant exogenous supply of glutamine further reduced enzyme activity in mid-log cells, suggesting induction by both aromatic amino acid depletion and nitrogen starvation.
Audiobook synthesis with long-form neural text-to-speech

Despite recent advances in text-to-speech (TTS) technology, auto-narration of long-form content such as books remains a challenge. The goal of this work is to enhance neural TTS to be suitable for long-form content such as audiobooks. In addition to high quality, we aim to provide a compelling and engaging listening experience with expressivity that spans beyond a single sentence to a paragraph level so that the user can not only follow the story but also enjoy listening to it. Towards that goal, we made four enhancements to our baseline TTS system: incorporation of BERT embeddings, explicit prosody prediction from text, long-context modeling over multiple sentences, and pre-training on long-form data. We propose an evaluation framework tailored to long-form content that evaluates the synthesis on segments spanning multiple paragraphs and focuses on elements such as comprehension, ease of listening, ability to keep attention, and enjoyment. The evaluation results show that the proposed approach outperforms the baseline on all evaluated metrics, with an absolute 0.47 MOS gain in overall quality. Ablation studies further confirm the effectiveness of the proposed enhancements.
The boundedly rational user equilibrium : a parametric optimization approach

This study proposes a static traffic assignment model in which travelers are boundedly rational in route choice: users only switch routes when a new path significantly improves travel time. We introduce a bilevel optimization problem which finds the best-performing boundedly rational user equilibrium flow with respect to travel time. We use a parametric optimization approach to study continuity of the feasible set, optimal value function and optimal link flow solution with respect to perturbations in the indifference band. We propose two algorithms that approach the best-performing flow distribution and compare our results with approaches in literature.
Effect of Dietary Level of DL-Methionine on Growth Performance and Some Blood Parameters in Japanese Growing Quails

One-day-old Japanese quail birds totaling 900 were divided into five groups, with six replicates in each group. Five doses of DL-methionine supplementation were used in a one-way arrangement for the experimental treatments. At concentrations of 0.0, 0.9, 1.65, 2.15, and 2.65 g/kg feed, DL-methionine was included in a basal diet. Dietary methionine values ranged from 0.41 (inadequate), to 0.50 (sufficient; 100% NRC), to 0.58, 0.63, and 0.68 (excess), compared to the current NRC standards. Dietary methionine levels did not significantly affect performance at 32 days of life. DL-methionine has no significant effect on total protein, albumin, globulin, creatinine, uric acid, total antioxidant capacity (TAC), or malondialdehyde (MDA). In contrast, serum high-density lipoprotein (HDL-c) levels significantly increased (P ≤ 0.05) in response to DL-methionine supplementation, and serum immunoglobulin G (IgG) levels significantly varied between groups (P ≤ 0.05) in response to DL-methionine supplementation, with the highest level occurring at 0.68% (exceeding) the NRC (1994) recommended concentration. Our findings imply that production performance, immunity, or antioxidant status can be improved by dietary supplementation with DL-methionine levels at 0.68% (excess) of the NRC (1994) recommended levels. However, a high methionine intake has a beneficial impact on enhancing serum HDL levels.
Diffusion Transformer for Adaptive Text-to-Speech

Given the success of diffusion in synthesizing realistic speech, we investigate how diffusion can be included in adaptive text-to-speech systems. Inspired by the adaptable layer norm modules for Transformer, we adapt a new backbone of diffusion models, Diffusion Transformer, for acoustic modeling. Specifically, the adaptive layer norm in the architecture is used to condition the diffusion network on text representations, which further enables parameter-efficient adaptation. We show the new architecture to be a faster alternative to its convolutional counterpart for general text-to-speech, while demonstrating a clear advantage on naturalness and similarity over the Transformer for few-shot and few-parameter adaptation. In the zero-shot scenario, while the new backbone is a decent alternative, the main benefit of such an architecture is to enable high-quality parameter-efficient adaptation when finetuning is performed.
Deep Learning Regression of Cardiac Phase on Real-Time MRI

Cine steady-state free-precession (SSFP) is the backbone of cardiac MRI, providing visualization of cardiac structure and function over the cardiac cycle, but requires concurrent ECG-gating to combine k-space data over multiple heart beats. However, cine SSFP is limited by a number of factors including arrhythmia, where beat-to-beat variability causes image artifacts. Real-time (RT) SSFP and recent innovations in image reconstruction provides a new potential alternative, capable of acquiring images without averaging over multiple heart beats. However, analysis of cardiac function from this image data can be complex, requiring retrospective analysis of function over multiple cardiac cycles and slices. We propose a deep learning regression method to facilitate cardiac phase detection, leveraging synthetic training approach from historical cine SSFP image data, and evaluate the effectiveness of this approach for detecting cardiac phase on RT SSFP images, manually labeled by expert readers. This combined approach using RT SSFP may have multiple potential advantages over traditional cine SSFP for evaluating cardiac function in patients with arrhythmia or difficulty tolerating long breath holds.
CLIP-Based Features Achieve Competitive Zero-Shot Visual Localization

—We evaluate the effectiveness of the Contrastive Language-Image Pre-training (CLIP) model for place recognition in indoor environments. A key advantage of CLIP is its ability to incorporate natural language understanding of the scene, and its zero-shot nature, enabling recognition of office and home environments without explicit training. Our results show that CLIP performs comparably to models trained specifically for place recognition such as NetVLAD, achieving above 90% recall on several datasets. This work further discusses the potential of CLIP to extend to semantic-based localization and mapping, enhancing scene comprehension and enabling autonomous systems to respond to higher-level commands.
Prompt, Condition, and Generate: Classification of Unsupported Claims with In-Context Learning

Unsupported and unfalsifiable claims we encounter in our daily lives can influence our view of the world. Characterizing, summarizing, and -- more generally -- making sense of such claims, however, can be challenging. In this work, we focus on fine-grained debate topics and formulate a new task of distilling, from such claims, a countable set of narratives. We present a crowdsourced dataset of 12 controversial topics, comprising more than 120k arguments, claims, and comments from heterogeneous sources, each annotated with a narrative label. We further investigate how large language models (LLMs) can be used to synthesise claims using In-Context Learning. We find that generated claims with supported evidence can be used to improve the performance of narrative classification models and, additionally, that the same model can infer the stance and aspect using a few training examples. Such a model can be useful in applications which rely on narratives , e.g. fact-checking.
Response of Plasma Flow Field to Nuclear Electromagnetic Pulse in Near Space

With the development of national missile defense system, we must rely on new ways to penetrate the existing strategic balance to maintain. Near space vehicle is one of the most promising means of a highly e-cient of penetration. Near space vehicle can efiectively circumvent the Kinetic Energy Interceptor, but low-yield nuclear detonation may be faced with a typical combat scenario. Nuclear explosions in near space on the impact of near space vehicle are particularly important. In this paper, flnite-difierence time-domain (FDTD) method has been discussed for complete simulation of nuclear electromagnetic pulse (NEMP) originating from an atmospheric nuclear detonation in near space. The Compton current formula and the simplifled model of solving the Maxwell's equations are described. In addition, the typical results that the plasma ∞ow fleld of near space vehicle is generated by the ionization between high-speed ∞ight and the thin air in near space are presented. The coupling between nuclear electromagnetic pulse (NEMP) in nuclear exploding source region and the plasma ∞ow fleld of near space vehicle is studied. To achieve this purpose, the radiated fleld in nuclear exploding source region is calculated using a late-model iterative equation including the electron plasma extra conductivity efiect. Because of the free electron plasma extra conductivity, the corresponding conduction current is ofiset by the Compton current, resulting in signiflcant decline in the peak transverse electric fleld. The presented results may be used in electromagnetic compatibility analysis of near space vehicle.
Learning Without Cleaning Over Content-Heterogeneous Databases

Given a relational database and training examples for a target relation, relational learning algorithms learn a Datalog program that defines the target relation in terms of the existing relations in the database. The information in a domain is usually spread across several databases, which often represent the same entities under different names. Therefore, learning over multiple databases may result in inaccurate definitions. This type of heterogeneities is also common within a single database due to low data quality. Currently, users have to spend a great deal of time and effort to resolve these heterogeneities and create a unified and clean database instance to be used for learning. As it is not usually clear which values represent the same real-world entity or data item, e.g., whether J. Smith represents John Smith or Jeremy Smith, users may end up with numerous database instances after cleaning and resolving the data values. We propose HDLearn, a relational learning system that learns directly over heterogeneous databases without any pre-processing step. The user specifies the attributes across relations that contain values that may refer to the same real-world entity or data item through a set of declarative constraints called matching dependencies. HDLearn leverages these dependencies to learn accurate definitions over the heterogeneous data. Our empirical study on real-world datasets indicate that HDLearn learns accurate definitions over large databases efficiently. PVLDB Reference Format: Jose Picado and Arash Termehchy. Learning Over Heterogeneous Relational Databases. PVLDB, 12(xxx): xxxx-yyyy, 2019. DOI: https://doi.org/10.14778/xxxxxxx.xxxxxxx
Generating Packed Rectilinear Display Text Layouts with Weighted Word Emphasis

A common text layout style is a “packed rectilinear layout”, in which non-overlapping word bounding boxes are packed so that their union forms a rectangle with no holes other than word and line spacing. Designing variations of these layouts while preserving word emphasis is a difficult and time-consuming process. We present a display text layout algorithm in which designers specify parameters that control the visual emphasis of words in these layouts. The number of possible layouts for a phrase follows the sequence of Big Schr¨oder numbers as our algorithm involves the recursive subdivision of a rectangular bounding box. We conducted semi-structured interviews with graphic design experts to better understand their design decisions in creative typesetting. They rated the best-fitting layouts generated by our system to be very similar to designs that they would have created themselves.
Work Package/Activity: D-FED 3.1.7 - Understanding the multiple dimensions of prediction of concepts in social and biomedical science questionnaires

The content of the surveys in these longitudinal studies are in the form of PDF questionnaires with different semantically distinct elements (question text, conditions, instructions etc.) that are captured within the DDI-Lifecycle standard schema. The application of common ontologies to a data collection is a significant investment to aid discovery and uptake of data for secondary data analysis. As data scales and ontologies develop, this is a major burden on providing ease of entry to using data investments. Existing efforts within CLOSER Discovery have involved manual and semi-automated tagging of question items to the CLOSER ontology, which have been utilised by the RCNIC-funded project ‘Automated classification of social science questions’ as training data to explore machine learning (ML) algorithms for the automated tagging of question items to existing thesauri.
On the V2G capacity of shared electric vehicles and its forecasting through MAML‐CNN‐LSTM‐Attention algorithm

When acting as the shared energy storage and participating in electricity market services, the schedulable capacity that shared electric vehicle (shared EV) will provide to the grid needs in future time needs to be predicted accurately. This research ﬁrst proposes a method to construct a schedulable capacity dataset for shared EVs based on publicly available shared vehicle rental service data. Secondly, a schedulable capacity evaluation model based on model-agnostic meta-learning, convolutional neural network, long short-term neural network and attention mechanism (MAML-CNN-LSTM-Attention) is proposed. Through the model, the aggregated schedulable capacity of shared EVs in diﬀerent functional communities for the coming 60 minutes is predicted. Model uses MAML to ﬁne-tune the meta-prediction network through multi-task training to quickly adapt to feature changes caused by diﬀerent travel habits of diﬀerent functional communities; CNN-LSTM is used to learn spatial features of schedulable
How do humans and machine learning models track multiple objects through occlusion?

Interacting with a complex environment often requires us to track multiple task-1 relevant objects not all of which are continually visible. The cognitive literature 2 has focused on tracking a subset of visible identical abstract objects (e.g. circles), 3 isolating the tracking component from its context in real-world experience. In 4 the real world, object tracking is harder in that objects may not be continually 5 visible and easier in that objects differ in appearance and so their recognition can 6 rely on both remembered position and current appearance. Here we introduce 7 a generalized task that combines tracking and recognition of valued objects that 8 move in complex trajectories and frequently disappear behind occluders. Humans 9 and models (from the computer-vision literature on object tracking) performed 10 tasks varying widely in terms of the number of objects to be tracked, the number 11 of distractors, the presence of an occluder, and the appearance similarity between 12 targets and distractors. We replicated results from the human literature, including 13 a deterioration of tracking performance with the number and similarity of targets 14 and distractors. In addition, we find that increasing levels of occlusion reduce 15 performance. All models tested here behaved in qualitatively different ways from 16 human observers, showing superhuman performance for large numbers of targets, 17 and subhuman performance under conditions of occlusion. Our framework will 18 enable future studies to connect the human behavioral and engineering literatures, 19 so as to test image-computable multiple-object-tracking models as models of human 20 performance and to investigate how tracking and recognition interact under natural 21 conditions of dynamic motion and occlusion. 22
Unsupervised Object Interaction Learning with Counterfactual Dynamics Models

We present COIL (Counterfactual Object Interaction Learning), a novel way of learning skills of object interactions on entity-centric environments. The goal is to learn primitive behaviors that can induce interactions without external reward or any supervision. Existing skill discovery methods are limited to locomotion, simple navigation tasks, or single-object manipulation tasks, mostly not inducing interaction between objects. Unlike a monolithic representation usually used in prior skill learning methods, we propose to use a structured goal representation that can query and scope which objects to interact with, which can serve as a basis for solving more complex downstream tasks. We design a novel counterfactual intrinsic reward through the use of either a forward model or successor features that can learn an interaction skill between a pair of objects given as a goal. Through experiments on continuous control environments such as Magnetic Block and 2.5-D Stacking Box, we demonstrate that an agent can learn object interaction behaviors (e.g., attaching or stacking one block to another) without any external rewards or domain-specific knowledge.