Miracle-dz/UniFAR-FTUs huggingface dataset:
CDT-Dijkstra: Fast Planning of Globally Optimal Paths for All Points in 2D Continuous Space

The Dijkstra algorithm is a classic path planning method, which in a discrete graph space, can start from a specified source node and find the shortest path between the source node and all other nodes in the graph. However, to the best of our knowledge, there is no effective method that achieves a function similar to that of the Dijkstra's algorithm in a continuous space. In this study, an optimal path planning algorithm called convex dissection topology (CDT)-Dijkstra is developed, which can quickly compute the global optimal path from one point to all other points in a 2D continuous space. CDT-Dijkstra is mainly divided into two stages: SetInit and GetGoal. In SetInit, the algorithm can quickly obtain the optimal CDT encoding set of all the cut lines based on the initial point Xinit. In GetGoal, the algorithm can return the global optimal path of any goal point at an extremely high speed. In this study, we propose and prove the planning principle of considering only the points on the cutlines, thus reducing the state space of the distance optimal path planning task from 2D to 1D. In addition, we propose a fast method to find the optimal path in a homogeneous class and theoretically prove the correctness of the method. Finally, by testing in a series of environments, the experimental results demonstrate that CDT-Dijkstra not only plans the optimal path from all points at once, but also has a significant advantage over advanced algorithms considering certain complex tasks.
Boosting Chinese ASR Error Correction with Dynamic Error Scaling Mechanism

Chinese Automatic Speech Recognition (ASR) error correction presents significant challenges due to the Chinese language's unique features, including a large character set and borderless, morpheme-based structure. Current mainstream models often struggle with effectively utilizing word-level features and phonetic information. This paper introduces a novel approach that incorporates a dynamic error scaling mechanism to detect and correct phonetically erroneous text generated by ASR output. This mechanism operates by dynamically fusing word-level features and phonetic information, thereby enriching the model with additional semantic data. Furthermore, our method implements unique error reduction and amplification strategies to address the issues of matching wrong words caused by incorrect characters. Experimental results indicate substantial improvements in ASR error correction, demonstrating the effectiveness of our proposed method and yielding promising results on established datasets.
Ekonomi Negeri Kelantan dari Perspektif Analisis Shift-Share Wilayah

Using regional shift-share analysis the present study, covering the overall period of 1980-95 which is subdivided into two sub-periods, aims at analysing the performance of Kelantan's economy vis-a-vis national economy. The state GDP data according to its major sectors are collected from EPU and the state EPU which is expressed in its real value by using GDP deflator. The results show that to investors the state is unattractive locationally, good performance of national industrialisation stimulates that of the state, and its advantage in primary sector is erroded because of structural change at the national level. The study offers few policy recommendations.
Data Fusion for Multi-Task Learning of Building Extraction and Height Estimation

In accordance with the urban reconstruction problem proposed by the DFC23 Track 2 Contest, this paper attempts a multitask-learning method of building extraction and height estimation using both optical and radar satellite imagery. Contrary to the initial goal of multitask learning which could potentially give a superior solution by reusing features and forming implicit constraints between multiple tasks, this paper reports the individual implementation of the building extraction and height estimation under constraints. The baseline results for the building extraction and the height estimation significantly increased after designed experiments.
The influence of using fractal analysis in hybrid MLP model for short-term forecast of close prices on Warsaw Stock Exchange

The paper describes a new method of combining Artificial Neural Networks (ANN), technical analysis and fractal analysis for predicting share prices on the Warsaw Stock Exchange. The proposed hybrid model consists of two consecutive modules. In the first step share prices are preprocessed and calculated into moving averages and oscillators. Then, in the next step, they are given to the ANN inputs, which provides the closing values of the asset for the next day. ANN of Multi-Layer Perceptron (MLP) type, and fractal analysis are applied. The hybrid model combining ANN with technical and fractal analysis is compared with hybrid model combining ANN with technical analysis. The obtained results indicate that hybrid model combined with fractal analysis is more accurate and stable in the long run than the hybrid model.
The two-way nested global chemistry-transport zoom model TM5: algorithm and applications

This paper describes the global chemistry Transport Model, version 5 (TM5) which allows two-way nested zooming. The model is used for global studies which require high resolution regionally but can work on a coarser resolution globally. The zoom algorithm introduces refinement in both space and time in some predefined regions. Boundary conditions of the zoom region are provided by a coarser parent grid and the results of the zoom area are communicated back to the parent. A case study using 222Rn measurements that were taken during the MINOS campaign reveals the advantages of local zooming. As a next step, it is investigated to what extent simulated concentrations over Europe are influenced by using an additional zoom domain over North America. An artificial ozone-like tracer is introduced with a lifetime of twenty days and simplified non-linear chemistry. The concentration differences at Mace Head (Ireland) are generally smaller than 10%, much smaller than the effects of the resolution enhancement over Europe. Thus, coarsening of resolution at some distance of a sampling station seems allowed. However, it is also noted that the budgets of the tracers change considerably due to resolution dependencies of, for instance, vertical transport. Due to the two-way nested algorithm, TM5 offers a consistent tool to study the effects of grid refinement on global atmospheric chemistry issues like intercontinental transport of air pollution. Correspondence to: M. Krol (krol@phys.uu.nl)
Transactional Indexes on (RDMA or CXL-based) Disaggregated Memory with Repairable Transaction

The failure atomic and isolated execution of clients operations is a default requirement for a system that serve multiple loosely coupled clients at a server. However, disaggregated memory breaks this requirement in remote indexes because a client operation is disaggregated to multiple remote reads/writes. Current indexes focus on performance improvements and largely ignore tolerating client failures. We argue that a practical DM index should be transactional: each index operation should be failure atomic and isolated in addition to being concurrency isolated. We present repairable transaction (rTX), a lightweight primitive to execute DM index operations. Each rTX can detect other failed rTXes on-the-fly with the help of concurrency control. Upon detection, it will repair their non-atomic updates online with the help of logging, thus hiding their failures from healthy clients. By further removing unnecessary logging and delegating concurrency control to existing carefully-tuned index algorithms, we show that transactional indexes can be built at a low performance overhead on disaggregated memory. We have refactored two state-of-the-art DM indexes, RaceHashing and Sherman (B+Tree), with rTX. Evaluations show that rTX is 1.2 to 2X faster than other alternatives, e.g., distributed transaction. Meanwhile, its overhead is up to 42% compared to non-fault-tolerant indexes.
High-temporal resolution functional PET/MRI reveals coupling between human metabolic and hemodynamic brain response

Positron emission tomography (PET) provides precise molecular information on physiological processes, but its low temporal resolution is a major obstacle. Consequently, we characterized the metabolic response of the human brain to working memory performance using an optimized functional PET framework at a temporal resolution of 3 seconds. Consistent with simulated kinetic modeling, we observed a constant increase in the [18F]FDG signal during task execution, followed by a rapid return to baseline after stimulation ceased. The simultaneous acquisition of BOLD fMRI revealed that the temporal coupling between hemodynamic and metabolic signals in the primary motor cortex was related to individual behavioral performance during working memory. Furthermore, task-induced BOLD deactivations in the posteromedial default mode network were accompanied by distinct temporal patterns in glucose metabolism, which depended on the task-positive network metabolic demands. In sum, the proposed approach enables the advancement from parallel to truly synchronized investigation of metabolic and hemodynamic responses during cognitive processing.
Machine Learning for Infectious Disease Risk Prediction: A Survey

Infectious diseases, either emerging or long-lasting, place numerous people at risk and bring heavy public health burdens worldwide. In the process against infectious diseases, predicting the epidemic risk by modeling the disease transmission plays an essential role in assisting with preventing and controlling disease transmission in a more effective way. In this paper, we systematically describe how machine learning can play an essential role in quantitatively characterizing disease transmission patterns and accurately predicting infectious disease risks. First, we introduce the background and motivation of using machine learning for infectious disease risk prediction. Next, we describe the development and components of various machine learning models for infectious disease risk prediction. Specifically, existing models fall into three categories: Statistical prediction, data-driven machine learning, and epidemiology-inspired machine learning. Subsequently, we discuss challenges encountered when dealing with model inputs, designing task-oriented objectives, and conducting performance evaluation. Finally, we conclude with a discussion of open questions and future directions.
Cloning Grameen Bank : replicating a poverty reduction model in India, Nepal and Vietnam

CONTENTS Introduction, Helen Todd 1. Nirdhan, Nepal, David S. Gibbons Key Replicator in Nepal, The First Year: Rapid Growth Second Year: Problems Emerge Women's Own Use of Loans Group Formation Who Gets the Credit? Impact on the Poor Financial Viability Step by Step to Higher Incomes Expansion Potential of Nirdhan Nepal Preparing the Way For Scaling Up Teamwork and Problem Solving Barsati wins Love and Livelihood from her Grocery Shop 2. SHARE, Andhra Pradesh, David S. Gibbons First Hurdles Origins Region of Water Region of Stones Thieves, Thugs and Other NGOs Investing in Courage Impact on the Poor Raising the House with her own Hands Buffalo Loans Self-Employment The High Cost of SHARE Strength of Samson Finding a Legal Form for Growth Grabbing The Second Chance 3. Nirdhan, West Bengal, Md. RashidulIAlam and Abdus Salam Khan Innovations and Deviations In a Poor State Grameen Bankers Give Advice Final Evaluation Report Group Formation Group Training Another Chit Fund? Nirdhan Convinces Critics Group Recognition Test (GRT) Centre Discipline Credit Delivery Credit Discipline Chillies and Sweet Water Build a Centre House Group Fund Field Supervision Office Management Staff Training and Performance Measurement Accounting and Financial System Fund Management Operating Costs Monitoring System Socio-Economic Impact on the Borrowers Prospects for Viability Recommendations Can Nirdhan Expand? 4. Tau Yew Mai, Vietnam, David S. Gibbons and Helen Todd The Pilot Years Growth - and Enthusiasm - Level Off Weaknesses in Soc San 1 Too Poor for TYM? Staff in Transition Building the Future: Training and Planning Major Impact on Income Rapidly Rolling Credit Good Prospects for Financial Viability Expansion and Institutionalization Soc San 2 A Roof for the Rain Baby Beats the Loan Conclusion, Helen Todd and David S. Gibbons The Question of Impact Demand for Credit The Importance of Leadership Operating in the Field Building a Professional Operation Potential for Poverty-Reduction and Sustainability Institutional Financial Viability Master Plan for Institutional Financial Self-sufficiency Fund Mobilization Institutional Capacity-building Needs Select Bibliography Figures 1.1. Growth of Members and Loans 1.2. Growth of Branches and Staff 4.1. TYM Membership Growth: July 1993-Dec 1995 C.1. Preliminary Institutions Forward Analysis
Choir Transformer: Generating Polyphonic Music with Relative Attention on Transformer

Polyphonic music generation is still a challenge direction due to its correct between generating melody and harmony. Most of the previous studies used RNN-based models. However, the RNN-based models are hard to establish the relationship between long-distance notes. In this paper, we propose a polyphonic music generation neural network named Choir Transformer[ https://github.com/Zjy0401/choir-transformer], with relative positional attention to better model the structure of music. We also proposed a music representation suitable for polyphonic music generation. The performance of Choir Transformer surpasses the previous state-of-the-art accuracy of 4.06%. We also measures the harmony metrics of polyphonic music. Experiments show that the harmony metrics are close to the music of Bach. In practical application, the generated melody and rhythm can be adjusted according to the specified input, with different styles of music like folk music or pop music and so on.
Thermodynamic nature of irreversibility in active matter

Active matter describes systems whose constituents convert energy from their surroundings into directed motion, such as bacteria or catalytic colloids. We unravel a remarkable direct link between the dynamics and thermodynamics for suspensions of such active particles: The thermodynamic equivalent of irreversibility is the swim pressure. Like in the famous fluctuation theorems of stochastic thermodynamics, irreversibility is defined as the log-ratio of the probabilities to observe a given set of particle trajectories and their time reverses. Our discovery is thus similar in spirit to what is known for passive systems, for which those fluctuation theorems relate the irreversibility to the thermodynamic entropy. It is based on numerical simulations and on analytical arguments for the limits of low particle density and/or high activity in the paradigmatic model of active Ornstein-Uhlenbeck particles.
Targeting the dependence on PIK3C3-mTORC1 signaling in dormancy-prone breast cancer cells blunts metastasis initiation

Halting breast cancer metastatic relapses following primary tumor removal and the clinical dormant phase, remains challenging, due to a lack of specific vulnerabilities to target during dormancy. To address this, we conducted genome-wide CRISPR screens on two breast cancer cell lines with distinct dormancy properties: 4T1 (short-term dormancy) and 4T07 (prolonged dormancy). We discovered that loss of class-III PI3K, Pik3c3, revealed a unique vulnerability in 4T07 cells. Surprisingly, dormancy-prone 4T07 cells exhibited higher mTORC1 activity than 4T1 cells, due to lysosome-dependent signaling occurring at the cell periphery. Pharmacological inhibition of Pik3c3 counteracted this phenotype in 4T07 cells, and selectively reduced metastasis burden only in the 4T07 dormancy prone model. This mechanism was also detected in xenografts from human breast cancer patients, supporting that it may be relevant in humans. Our findings suggest dormant cancer cell-initiated metastasis may be prevented in patients carrying tumor cells that display PIK3C3-peripheral lysosomal signaling to mTORC1. Teaser PIK3C3-mTORC1 signaling is a novel druggable pathway in breast cancer metastasis.
Deciphering the Genetic Code of Neuronal Type Connectivity: A Bilinear Modeling Approach

Understanding how different neuronal types connect and communicate is critical to interpreting brain function and behavior. However, it has remained a formidable challenge to decipher the genetic underpinnings that dictate the specific connections formed between neuronal types. To address this, we propose a novel bilinear modeling approach that leverages the architecture similar to that of recommendation systems. Our model transforms the gene expressions of presynaptic and postsynaptic neuronal types, obtained from single-cell transcriptomics, into a crosscorrelation matrix. The objective is to construct this cross-correlation matrix that closely mirrors a connectivity matrix, derived from connectomic data, reflecting the known anatomical connections between these neuronal types. When tested on a dataset of Caenorhabditis elegans, our model achieved a performance comparable to, if slightly better than, the previously proposed spatial connectome model (SCM) in reconstructing electrical synaptic connectivity based on gene expressions. Through a comparative analysis, our model not only captured all genetic interactions identified by the SCM but also inferred additional ones. Applied to a mouse retinal neuronal dataset, the bilinear model successfully recapticulated recognized connectivity motifs between bipolar cells and retinal ganglion cells, and provided interpretable insights into genetic interactions shaping the connectivity. Specifically, it identified unique genetic signatures associated with different connectivity motifs, including genes important to cell-cell adhesion and synapse formation, highlighting their role in orchestrating specific synaptic connections between these neurons. Our work establishes an innovative computational strategy for decoding the genetic programming of neuronal type connectivity. It not only sets a new benchmark for single-cell transcriptomic analysis of synaptic connections but also paves the way for mechanistic studies of neural circuit assembly and genetic manipulation of circuit wiring.
Ecological Network Resilience & Extinction Proxies - Updating Projections of Ecological Networks

Forecasting biodiversity and functioning changes to ecosystem composition and functioning under climate change requires using multi-species approaches that explicitly consider ecological interactions. Here, we propose a framework with which to incorporate considerations of (1) localised extinction risk proxies, (2) resilience mechanisms of ecological networks, and (3) extinction cascade directionality as a driving force of ecological change. These three aspects are seldomly considered when establishing ecosystems responses to climate change and biodiversity loss. Using this framework, we demonstrate that current practices may severely underpredict ecological change measured as loss of biodiversity and change in connectedness. Our novel framework which explicitly explores two-dimensional resilience landscapes defined by network resilience mechanisms (i.e., link loss sensitivity and realisation of rewiring potential) represents the most complete toolbox for assessment of vulnerability of ecological networks to extinction cascades. Ultimately, we propose that using localised extinction proxies, explicitly quantifying ecological network resilience through link-loss sensitivity and realisation of rewiring potential, as well as simulation of bidirectional extinction cascades will lead to improved capabilities of estimating ecosystem trajectories throughout the Anthropocene.
Elemental Characters: The Living Vegetation of Element City

The Earth characters of Pixar’s Elemental feature a wide range of foliage adornments, ranging from bushes and small plants to trees that tower over their bodies. Creating appealing motion for each of the variations of these characters presented a unique challenge to the Simulation Team, and led to a significant expansion of the existing simulation pipeline. These characters needed to believably co-exist in a world with set-dressed foliage, while also interacting with the diverse and mechanically challenging environment of Element City. In order to meet the demands of a film featuring crowded mass transit, high levels of action, and trees that prune each other, a new series of tools were created to allow Pixar’s Simulation artists to apply the most powerful tools at our disposal to the most appropriate tasks.
Learning to Generate Training Datasets for Robust Semantic Segmentation

Semantic segmentation methods have advanced significantly. Still, their robustness to real-world perturbations and object types not seen during training remains a challenge, particularly in safety-critical applications. We propose a novel approach to improve the robustness of semantic segmentation techniques by leveraging the synergy between label-to-image generators and image-to-label segmentation models. Specifically, we design Robusta, a novel robust conditional generative adversarial network to generate realistic and plausible perturbed images that can be used to train reliable segmentation models. We conduct in-depth studies of the proposed generative model, assess the performance and robustness of the downstream segmentation network, and demonstrate that our approach can significantly enhance the robustness in the face of real-world perturbations, distribution shifts, and out-of-distribution samples. Our results suggest that this approach could be valuable in safety-critical applications, where the reliability of perception modules such as semantic segmentation is of utmost importance and comes with a limited computational budget in inference. We release our code at https://github.com/ENSTA-U2IS-AI/robusta.
Using Range-Revocable Pseudonyms to Provide Backward Unlinkability in the Edge

In this paper we propose a novel abstraction that we have named Range-Revocable Pseudonyms (RRPs). RRPs are a new class of pseudonyms whose validity can be revoked for any time-range within its original validity period. The key feature of RRPs is that the information provided to revoke a pseudonym for a given time-range cannot be linked with the information provided when using the pseudonym outside the revoked range. We provide an algorithm to implement RRPs using efficient cryptographic primitives where the space complexity of the pseudonym is constant, regardless of the granularity of the revocation range, and the space complexity of the revocation information only grows logarithmically with the granularity; this makes the use of RRPs far more efficient than the use of many short-lived pseudonyms. We have used RRPs to design EDGAR, an access control system for VANET scenarios that offers backward unlinkability. The experimental evaluation of EDGAR shows that, when using RRPs, the revocation can be performed efficiently (even when using time slots as small as 1 second) and that users can authenticate with low latency (0.5-3.5ms ).
Student-T and Beyond: Practical Tools for Multiple-Scattering BSDFs with General NDFs

We present a practical importance-sampling scheme for the Student-T distribution of visible normals by representing the Student-T NDF as a superposition of Beckmann NDFs. Additionally, we derive a new form of delta tracking to evaluate and sample exact BSDFs with general full-sphere NDFs. These tools permit efficient computation of benchmark BSDF values for the multiple scattering from general (including porous) rough surfaces.