Miracle-dz/UniFAR-FTUs huggingface dataset:
BoW3D: Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM

Loop closing is a fundamental part of simultaneous localization and mapping (SLAM) for autonomous mobile systems. In the field of visual SLAM, bag of words (BoW) has achieved great success in loop closure. The BoW features for loop searching can also be used in the subsequent 6-DoF loop correction. However, for 3D LiDAR SLAM, the state-of-the-art methods may fail to effectively recognize the loop in real time, and usually cannot correct the full 6-DoF loop pose. To address this limitation, we present a novel <underline>B</underline>ag <underline>o</underline>f <underline>W</underline>ords for real-time loop closing in <underline>3D</underline> LiDAR SLAM, called BoW3D. Our method not only efficiently recognizes the revisited loop places, but also corrects the full 6-DoF loop pose in real time. BoW3D builds the bag of words based on the 3D LiDAR feature LinK3D, which is efficient, pose-invariant and can be used for accurate point-to-point matching. We furthermore embed our proposed method into 3D LiDAR odometry system to evaluate loop closing performance. We test our method on public dataset, and compare it against other state-of-the-art algorithms. BoW3D shows better performance in terms of <inline-formula><tex-math notation="LaTeX">$F_{1}$</tex-math></inline-formula> max and extended precision scores on most scenes. It is noticeable that BoW3D takes an average of 48 ms to recognize and correct the loops on KITTI 00 (includes 4K+ 64-ray LiDAR scans), when executed on a notebook with an Intel Core i7 @2.2 GHz processor.
A Library for Representing Python Programs as Graphs for Machine Learning

Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static analysis to construct graph representations of Python programs suitable for training machine learning models. Our library admits the construction of control-flow graphs, data-flow graphs, and composite ``program graphs'' that combine control-flow, data-flow, syntactic, and lexical information about a program. We present the capabilities and limitations of the library, perform a case study applying the library to millions of competitive programming submissions, and showcase the library's utility for machine learning research.
Diagnosis of malignant melanoma by neural network ensemble-based system utilising hand-crafted skin lesion features

Malignant melanomas are the most deadly type of skin cancer but detected early have high chances for successful treatment. In the last twenty years, the interest of automated melanoma recognition detection and classification dynamically increased partially because of public datasets appearing with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task due to uneven datasets sizes, the huge intra-class variation with small interclass variation, and the existence of many artifacts in the image. One of the most recognized methods of melanoma diagnosis is the ABCD method. In the paper, we propose an extended version of this method and an intelligent decision support system based on neural networks that uses its results in a form of hand-crafted features. Automatic determination of the skin features used by the ABCD method is difficult due to the large diversity of images of various quality, the existence of hair, different markers and other obstacles. Therefore, it was necessary to apply advanced methods of preprocessing the images. The system is an ensemble of ten neural networks, working in parallel and one network using their results to generate a final decision. This system structure allowed us to increase the efficiency of the operation by several percentage points compared to a single neural network. The proposed system is trained on over 5000 and tested afterward on 200 skin moles. The presented system can be used as a decision support system for primary care physicians, as a system capable of self-examination of the skin with a dermatoscope and also as an important tool to improve biopsy decision making.
Making Reinforcement Learning Work on Swimmer

The SWIMMER environment is a standard benchmark in reinforcement learning (RL). In particular, it is often used in papers comparing or combining RL methods with direct policy search methods such as genetic algorithms or evolution strategies. A lot of these papers report poor performance on SWIMMER from RL methods and much better performance from direct policy search methods. In this technical report we show that the low performance of RL methods on SWIMMER simply comes from the inadequate tuning of an important hyper-parameter, the discount factor. Furthermore we show that, by setting this hyper-parameter to a correct value, the issue can be easily fixed. Finally, for a set of often used RL algorithms, we provide a set of successful hyper-parameters obtained with the Stable Baselines3 library and its RL Zoo.
Strain effect on the ground-state structure of Sr2SnO4 Ruddlesden-Popper oxides

Ruddlesden-Popper (RP) oxides (A$_{n+1}$B$_n$O$_{3n+1}$) comprised of perovskite (ABO$_3$)$_n$ slabs can host a wider variety of structural distortions than their perovskite counterparts. This makes accurate structural determination of RP oxides more challenging. In this study, we investigate the structural phase diagram of $n=1$ RP Sr$_2$SnO$_4$, one of alkaline earth stannates that are promising for opto-electronic applications by using group theory-based symmetry analysis and first-principles calculations. We explore the symmetry breaking effects of different dynamical instabilities, predict the energies of phases they lead to, and take into account different (biaxial strain and hydrostatic pressure) boundary conditions. We also address the effect of structural changes on the electronic structure and find that compressive biaxial strain drives Sr$_2$SnO$_4$ into a regime with wider bandgap and lower electron effective mass.
Effect of area specific mineral mixture, by-pass protein and hormonal treatment on the performance of crossbred cows in Kakatpur block

Three hundred and thirty four numbers of crossbred cows and heifers were surveyed for assessment of reproductive disorders in Kakatpur block of Puri district in Odisha. Out of those 41.61% were found to have reproductive problems. From those problematic animals 100 animals (60 cows and 40 heifers) were selected for the present study. Animals were divided into five groups having 20 animals each (8 anoestrus and 12 repeat breeding) and treatment was done for 60days. Group I was kept as control (farmer practice), Group II; Group I + mineral mixture (50g/day/animal) comprising of Ca, P, Zn, Cu and Mn, Group III; Group II + Bypass protein (100g/day/animal), Group IV; Group II+ hormone (Double sync), Group V; Group II+ hormone (Estra-double sync). Body weight change was recorded during the experiment and ADG (average daily gain) was calculated. Blood collection was done at the start and end of the experiment for assessment of blood biochemical (Glucose, cholesterol, total protein, albumin, globulin, urea, triglyceride, ALT, AST and ALP), hematological parameters (Hb, TEC, PCV, MCV and MCHC), mineral status (Ca, P, Zn, Cu and Mn) of the animals. Level of progesterone and estrogen was evaluated at the start of the experiment for assessment of reproductive status and they were found to be in normal range. By per-rectal examination, ovarian characters were recorded at the start and end of the experiment. Nutrient requirement and availability of the animals were evaluated. Nutrient availability was found to be less as compared to that of requirement. After 60 days there was no significant difference in body weight gain among the treatments. The blood biochemical parameters like glucose, total protein, albumin level increased and urea level decreased significantly (P<0.05) among the groups. Hb% level was found to be increased significantly (P<0.05) after treatment. Level of Ca and micro minerals concentration increased significantly in treatment groups than control animals. Among anestrus and repeat breeding animals percentage of non-functional ovaries and sub-active ovaries were reduced and percentage of functional ovaries increased. In total 62% of animals were found to be pregnant after treatments. Highest pregnancy percentage was observed in group V followed by IV, III and II. So it may be concluded that mineral mixture, bypass protein and hormone supplementation improved blood biochemicals like glucose, total protein and albumin, decreased serum urea concentration; increased Hb%; and improved mineral concentration and reproductive performances of cross bred cows. CHAPTER-
Reinforcement Learning to Rank with Coarse-grained Labels

Ranking lies at the core of many Information Retrieval (IR) tasks. While existing research on Learning to Rank (LTR) using Deep Neural Network (DNN) has achieved great success, it is somewhat limited because of its dependence on fine-grained labels. In practice, fine-grained labels are often expensive to acquire, i.e. explicit relevance judgements, or suffer from biases, i.e. click logs. Compared to fine-grained labels, coarse-grained labels are easier and cheaper to collect. Some recent works propose utilizing only coarse-grained labels for LTR tasks. A most representative line of work introduces Reinforcement Learning (RL) algorithms. RL can help train the LTR model with little reliance on fine-grained labels compared to Supervised Learning. To study the effectiveness of the RL-based LTR algorithm on coarse-grained labels, in this paper, we implement four different RL paradigms and conduct extensive experiments on two well-established LTR datasets. The results on simulated coarse-grained labeled dataset show that while using coarse-grained labels to train an RL model for LTR tasks still can not outperform traditional approaches using fine-grained labels, it still achieve somewhat promising results and is potentially helpful for future research in LTR. Our code implementations will be released after this work is accepted.
Fine-Grained Complexity Lower Bounds for Families of Dynamic Graphs

A dynamic graph algorithm is a data structure that answers queries about a property of the current graph while supporting graph modifications such as edge insertions and deletions. Prior work has shown strong conditional lower bounds for general dynamic graphs, yet graph families that arise in practice often exhibit structural properties that the existing lower bound constructions do not possess. We study three specific graph families that are ubiquitous, namely constant-degree graphs, power-law graphs, and expander graphs, and give the first conditional lower bounds for them. Our results show that even when restricting our attention to one of these graph classes, any algorithm for fundamental graph problems such as distance computation or approximation or maximum matching, cannot simultaneously achieve a sub-polynomial update time and query time. For example, we show that the same lower bounds as for general graphs hold for maximum matching and ( s, t )-distance in constant-degree graphs, power-law graphs or expanders. Namely, in an m -edge graph, there exists no dynamic algorithms with both O ( m 1 / 2 − ϵ ) update time and O ( m 1 − ϵ ) query time, for any small ϵ > 0. Note that for ( s, t )-distance the trivial dynamic algorithm achieves an almost matching upper bound of constant update time and O ( m ) query time. We prove similar bounds for the other graph families and for other fundamental problems such as densest subgraph detection and perfect matching. “The Design of Modern Fully Dynamic Data Structures (MoDynStruct)” and from the Austrian Science Fund (FWF) project “Fast Algorithms for a Reactive Network Layer (ReactNet)”, P 33775-N, with additional funding from the netidee SCIENCE Stiftung , 2020–2024
GNNear: Accelerating Full-Batch Training of Graph Neural Networks with near-Memory Processing

Recently, Graph Neural Networks (GNNs) have become state-of-the-art algorithms for analyzing non-euclidean graph data. However, to realize efficient GNN training is challenging, especially on large graphs. The reasons are many-folded: 1) GNN training incurs a substantial memory footprint. Full-batch training on large graphs even requires hundreds to thousands of gigabytes of memory. 2) GNN training involves both memory-intensive and computation-intensive operations, challenging current CPU/GPU platforms. 3) The irregularity of graphs can result in severe resource under-utilization and load-imbalance problems. This paper presents a GNNear accelerator to tackle these challenges. GNNear adopts a DIMM-based memory system to provide sufficient memory capacity. To match the heterogeneous nature of GNN training, we offload the memory-intensive Reduce operations to in-DIMM Near-Memory-Engines (NMEs), making full use of the high aggregated local bandwidth. We adopt a Centralized-Acceleration-Engine (CAE) to process the computation-intensive Update operations. We further propose several optimization strategies to deal with the irregularity of input graphs and improve GNNear's performance. Comprehensive evaluations on 16 GNN training tasks demonstrate that GNNear achieves 30.8× / 2.5× geomean speedup and 79.6× / 7.3× (geomean) higher energy efficiency compared to Xeon E5-2698-v4 CPU and NVIDIA V100 GPU.
Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care

While dental disease is largely preventable, professional advice on optimal oral hygiene practices is often forgotten or abandoned by patients. Therefore patients may benefit from timely and personalized encouragement to engage in oral self-care behaviors. In this paper, we develop an online reinforcement learning (RL) algorithm for use in optimizing the delivery of mobile-based prompts to encourage oral hygiene behaviors. One of the main challenges in developing such an algorithm is ensuring that the algorithm considers the impact of current actions on the effectiveness of future actions (i.e., delayed effects), especially when the algorithm has been designed to run stably and autonomously in a constrained, real-world setting characterized by highly noisy, sparse data. We address this challenge by designing a quality reward that maximizes the desired health outcome (i.e., high-quality brushing) while minimizing user burden. We also highlight a procedure for optimizing the hyperparameters of the reward by building a simulation environment test bed and evaluating candidates using the test bed. The RL algorithm discussed in this paper will be deployed in Oralytics. To the best of our knowledge, Oralytics is the first mobile health study utilizing an RL algorithm designed to prevent dental disease by optimizing the delivery of motivational messages supporting oral self-care behaviors.
Generalized Bondage Number: The $k$-synchronous bondage number of a graph

We investigate a generalization of the bondage number of a graph called the \textit{$k\,$-synchronous bondage number}. The $k\,$-synchronous bondage number of a graph is the smallest number of edges that, when removed, increases the dominating number by $k$. In this paper, we discuss the 2-synchronous bondage number and then generalize to $k\,$-synchronous bondage number. We present $k\,$-synchronous bondage number for several graph classes and give bounds for general graphs. We propose this characteristic as a metric of the connectivity of a simple graph with possible uses in the field of network design and optimization.
Higher-order accurate two-sample network inference and network hashing

Two-sample hypothesis testing for network comparison presents many significant challenges, including: leveraging repeated network observations and known node registration, but without requiring them to operate; relaxing strong structural assumptions; achieving finite-sample higher-order accuracy; handling different network sizes and sparsity levels; fast computation and memory parsimony; controlling false discovery rate (FDR) in multiple testing; and theoretical understandings, particularly regarding finite-sample accuracy and minimax optimality. In this paper, we develop a comprehensive toolbox, featuring a novel main method and its variants, all accompanied by strong theoretical guarantees, to address these challenges. Our method outperforms existing tools in speed and accuracy, and it is proved power-optimal. Our algorithms are user-friendly and versatile in handling various data structures (single or repeated network observations; known or unknown node registration). We also develop an innovative framework for offline hashing and fast querying as a very useful tool for large network databases. We showcase the effectiveness of our method through comprehensive simulations and applications to two real-world datasets, which revealed intriguing new structures.
Latent trajectory models for spatio‐temporal dynamics in Alaskan ecosystems

The Alaskan landscape has undergone substantial changes in recent decades, most notably the expansion of shrubs and trees across the Arctic. We developed a Bayesian hierarchical model to quantify the impact of climate change on the structural transformation of ecosystems using remotely sensed imagery. We used latent trajectory processes to model dynamic state probabilities that evolve annually, from which we derived transition probabilities between ecotypes. Our latent trajectory model accommodates temporal irregularity in survey intervals and uses spatio‐temporally heterogeneous climate drivers to infer rates of land cover transitions. We characterized multi‐scale spatial correlation induced by plot and subplot arrangements in our study system. We also developed a Pólya–Gamma sampling strategy to improve computation. Our model facilitates inference on the response of ecosystems to shifts in the climate and can be used to predict future land cover transitions under various climate scenarios.
enhances directed cardiac differentiation of mouse and human embryonic stem cells.

Apelin is a peptide ligand for an orphan G-protein coupled receptor (APJ receptor) and serves as a critical gradient for migration of mesodermal cells fated to contribute to the myocardial lineage. The present study was designed to establish a robust cardiac differentiation protocol, specifically, to evaluate the effect of apelin on directed differentiation of mouse and human embryonic stem cells (mESCs and hESCs) into cardiac lineage. Different concentrations of apelin (50, 100, 500 nM) were evaluated to determine its differentiation potential. The optimized dose of apelin was then combined with mesodermal differentiation factors, including BMP-4, activin-A, and bFGF, in a developmentally specific temporal sequence to examine the synergistic effects on cardiac differentiation. Cellular, molecular, and physiologic characteristics of the apelin-induced contractile embryoid bodies (EBs) were analyzed. It was found that 100 nM apelin resulted in highest percentage of contractile EB for mESCs while 500 nM had the highest effects on hESCs. Functionally, the contractile frequency of mESCs-derived EBs (mEBs) responded appropriately to increasing concentration of isoprenaline and diltiazem. Positive phenotype of cardiac specific markers was confirmed in the apelin-treated groups. The protocol, consisting of apelin and mesodermal differentiation factors, induced contractility in significantly higher percentage of hESC-derived EBs (hEBs), up-regulated cardiac-specific genes and cell surface markers, and increased the contractile force. In conclusion, we have demonstrated that the treatment of apelin enhanced cardiac differentiation of mouse and human ESCs and exhibited synergistic effects with mesodermal differentiation factors.
Discussion Papers in Economics The Political Economy of Foreign Aid Effectiveness

When resource allocation in a recipient country is characterized by an economic contest between the rent-seeking elite and economically active masses, foreign aid is more eﬀective when there is lower economic inequality. This ﬁnding is supported by empirical evidence: increasing the aid/GDP ratio by one standard deviation is estimated to boost recipient growth by 0.25 points in the most equal aid recipients but reduce growth by 2.30 points in the least equal recipients; similarly, it is estimated to decrease the Gini coeﬃcient by 0.35 points in the most equal recipients but increase it by 1.45 points in the least equal recipients.
Transfer of orbital angular momentum on a macroscopic object in the UHF frequency band

We report on the exchange of orbital angular momentum between an electromagnetic microwave and a macroscopic object. Using a quadrupole wire antenna at frequency f = 870 MHz, we induce a torque on a suspended copper strip. The induced torque on the strip is of the order of 10 8 Nm. A linear dependence of the acceleration of the strip on the radiated power, up to 8.10-4 °/s², is observed and rotations of a few degrees are measured.
Singular Spectrum Transformation on Motion/Emotion Matrix to Measure Collaboration

— In this study, we propose the method estimating collaborative state by the similarity of motion characteristics and emotion characteristics changes. The group learning based on collaboration among students are introduced in many university classes. However, it may not function as a group and teachers evaluate only result of group learning. Our method focusses on similarity of motion and emotion characteristics change. The change points are extracted from these time series data by applying singular spectrum transformation (SST). In addition, the similarity points of change extracted by comparing the change points among students. The collaborative states are estimated in a certain period from the number of similarity points of change using machine learning. The experimental result indicates this method can identify between non-collaborative state and collaborative state. The method facilitates teachers to evaluate and guide the groups.