Reviewerly/exHarmony huggingface dataset:
The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

Language models (LMs) with less than 100B parameters are known to perform poorly on chain-of-thought (CoT) reasoning in contrast to large LMs when solving unseen tasks. In this work, we aim to equip smaller LMs with the step-by-step reasoning capability by instruction tuning with CoT rationales. In order to achieve this goal, we first introduce a new instruction-tuning dataset called the CoT Collection, which augments the existing Flan Collection (including only 9 CoT tasks) with additional 1.84 million rationales across 1,060 tasks. We show that CoT fine-tuning Flan-T5 (3B & 11B) with CoT Collection enables smaller LMs to have better CoT capabilities on unseen tasks. On the BIG-Bench-Hard (BBH) benchmark, we report an average improvement of +4.34% (Flan-T5 3B) and +2.60% (Flan-T5 11B), in terms of zero-shot task accuracy. Furthermore, we show that instruction tuning with CoT Collection allows LMs to possess stronger few-shot learning capabilities on 4 domain-specific tasks, resulting in an improvement of +2.24% (Flan-T5 3B) and +2.37% (Flan-T5 11B), even outperforming ChatGPT utilizing demonstrations until the max length by a +13.98% margin. Our code, the CoT Collection data, and model checkpoints are publicly available.
CoMMA Protocol: Towards Complete Mitigation of Maximal Extractable Value (MEV) Attacks

MEV attacks have been an omnipresent evil in the blockchain world, an implicit tax that uninformed users pay for using the service. The problem arises from the miners' ability to reorder and insert arbitrary transactions in the blocks they mine. This paper introduces a 2-phased transaction protocol designed to mitigate MEV attacks in blockchain systems. The proposed protocol involves users obtaining interaction tokens from on-chain counter-parties, which act as preemptive measures against transaction reordering. The effectiveness of the CoMMA protocol in preventing MEV attacks is demonstrated and validated.
Algorithmic Bias in De-Identification Tools

We present a series of experiments designed to quantify the algorithmic bias inherent in six off-the-shelf de-identification systems. We used false negative rate (FNR) and true positive rate (TPR) parity measures in addition to F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score to evaluate the systems across two environments. In the first condition, we inferred gender and race/ethnicity labels for a pre-existing de-identification corpus and analyzed performance on disaggregated subgroups. In the second condition, we controlled the dominant race/ethnicity bias for names used as realistic surrogates for resynthesizing a different pre-existing de-identification corpus. In both conditions, we found cases of strong bias and near-zero bias.
WaterNeRF: Neural Radiance Fields for Underwater Scenes

Underwater imaging is a critical task performed by marine robots for a wide range of applications including aquaculture, marine infrastructure inspection, and environmental monitoring. However, water column effects, such as attenuation and backscattering, drastically change the color and quality of imagery captured underwater. Due to varying water conditions and range-dependency of these effects, restoring underwater imagery is a challenging problem. This impacts downstream perception tasks including depth estimation and 3D reconstruction. In this paper, we leverage state-of-the-art neural radiance fields (NeRFs) to enable physics-informed novel view synthesis with image restoration and dense depth estimation for underwater scenes. Our proposed method, WaterNeRF, estimates parameters of a physics-based model for underwater image formation and uses these parameters for novel view synthesis. After learning the scene structure and radiance field, we can produce novel views of degraded as well as corrected underwater images. We evaluate the proposed method qualitatively and quantitatively on a real underwater dataset.
Assessment of the Need for Staff of Public Libraries of the Subjects of the Russian Federation

The monitoring of the staffing needs of public libraries of the constituent entities of the Russian Federation, conducted by the Russian State Library, shows the dynamics of the regional library policy and allows us to determine the vectors of development of the human resources potential of libraries in the near future. The staffing needs of public libraries of the constituent entities of the Russian Federation — participants of the monitoring, their size and professional structure are different and are mainly conditioned by the size of the regional library network, the level of staffing in the region and the professional composition of library staff. These factors directly determine the indicators of staffing needs of libraries in the near future. The needs for library specialists with higher profile education prevail in relation to other professional groups. However, the absolute indicator of these needs does not exceed half of the regions’ staffing needs and accounts for 43.07 percent of their total number. At the same time, the average value of the relative indicator of the regions’ needs in library specialists with higher profile education is only 29.7 staff units. The need for library specialists with secondary non-core and core additional education remains high. Based on the requests of libraries, their number should be increased by 5.76 percent of the available number. The regional comparative-correlation analysis of the need for library specialists revealed that there is a great need for specialists with higher profile education in a number of constituent entities of the Russian Federation, which have specialised universities that train bachelors and masters in “Library and Information Activities” (Republic of Tatarstan — 77 specialists, Altai Krai — 87, Krasnodar Krai — 174, Samara Oblast — 127). Perhaps, the high demand is due to the low availability of specialists with higher profile education in libraries in these regions (on average — 33.21%). The monitoring data indicate an increased need of public libraries for IT specialists: it amounts to 11.65% of the total staffing needs of the constituent entities of the Russian Federation. Based on the stated needs, the number of IT-specialists should be increased by 30.42%. At the same time, the need for IT specialists with higher profile education significantly exceeds the need for specialists with secondary profile education: 71.45% to 19.35% respectively. The needs of libraries in specialists of other professions are relatively small and occupy 8.43 percent of their total staffing needs. Of these, 71.65 percent are specialists with higher professional education.
Addressing the Gaps of IoU Loss in 3D Object Detection with IIoU

Three-dimensional object detection involves estimating the dimensions, orientations, and locations of 3D bounding boxes. Intersection of Union (IoU) loss measures the overlap between predicted 3D box and ground truth 3D bounding boxes. The localization task uses smooth-L1 loss with IoU to estimate the object’s location, and the classification task identifies the object/class category inside each 3D bounding box. Localization suffers a performance gap in cases where the predicted and ground truth boxes overlap significantly less or do not overlap, indicating the boxes are far away, and in scenarios where the boxes are inclusive. Existing axis-aligned IoU losses suffer performance drop in cases of rotated 3D bounding boxes. This research addresses the shortcomings in bounding box regression problems of 3D object detection by introducing an Improved Intersection Over Union (IIoU) loss. The proposed loss function’s performance is experimented on LiDAR-based and Camera-LiDAR-based fusion methods using the KITTI dataset.
Information-theoretic hardness of out-of-time-order correlators

We establish that there are properties of quantum many-body dynamics which are efficiently learnable if we are given access to out-of-time-order correlators (OTOCs), but which require exponentially many operations in the system size if we can only measure time-ordered correlators. This implies that any experimental protocol which reconstructs OTOCs solely from time-ordered correlators must be, in certain cases, exponentially inefficient. Our proofs leverage and generalize recent techniques in quantum learning theory. Along the way, we elucidate a general definition of time-ordered versus out-of-time-order experimental measurement protocols, which can be considered as classes of adaptive quantum learning algorithms. Moreover, our results provide a theoretical foundation for novel applications of OTOCs in quantum simulations.
A Novel Approach for Data Feature Weighting Using Correlation Coefficients and Min–Max Normalization

In the realm of data analysis and machine learning, achieving an optimal balance of feature importance, known as feature weighting, plays a pivotal role, especially when considering the nuanced interplay between the symmetry of data distribution and the need to assign differential weights to individual features. Also, avoiding the dominance of large-scale traits is essential in data preparation. This step makes choosing an effective normalization approach one of the most challenging aspects of machine learning. In addition to normalization, feature weighting is another strategy to deal with the importance of the different features. One of the strategies to measure the dependency of features is the correlation coefficient. The correlation between features shows the relationship strength between the features. The integration of the normalization method with feature weighting in data transformation for classification has not been extensively studied. The goal is to improve the accuracy of classification methods by striking a balance between the normalization step and assigning greater importance to features with a strong relation to the class feature. To achieve this, we combine Min–Max normalization and weight the features by increasing their values based on their correlation coefficients with the class feature. This paper presents a proposed Correlation Coefficient with Min–Max Weighted (CCMMW) approach. The data being normalized depends on their correlation with the class feature. Logistic regression, support vector machine, k-nearest neighbor, neural network, and naive Bayesian classifiers were used to evaluate the proposed method. Twenty UCI Machine Learning Repository and Kaggle datasets with numerical values were also used in this study. The empirical results showed that the proposed CCMMW significantly improves the classification performance through support vector machine, logistic regression, and neural network classifiers in most datasets.
From Outcrop to Spectrum—An Automated Approach to Modal Mineralogy of Silt‐Sized Sediment Applied to Central European Loess

Abstract Provenance information from recent and ancient sedimentary archives is obscured by several factors and for disentangling these intermingled signals, analysis by multiple methods is paramount. In sedimentary provenance analysis (SPA), single‐grain methods determining mineralogy, chemical composition, or radiometric ages are of key importance but are mostly applied to sand‐sized sediments or sedimentary rocks. Finer grained sediments or sedimentary rocks are usually analyzed by whole‐rock geochemical means and seldom by single‐grain methods. Considering the abundance of fine‐grained sedimentary archives, a strong need for single‐grain, multi‐method analyses of silt‐sized sediments is obvious. Thus, we propose a workflow that is optimized for sample throughput and correlative analysis of fine‐grained sediments based on machine learning methods. The feasibility of the workflow is demonstrated by differentiating three Central European loess‐paleosol‐sequences. The increased sample throughput enables access to sedimentary archives at high spatial and/or temporal resolution, which will open up new research pathways in SPA of silt‐sized sediments.
Swarm unmanned surface vehicle path planning for visiting multiple targets

In this study we present a hybrid approach of ACO with fuzzy logic and clustering methods to solve multi-objective path planning problems in case of swarm USVs. This study aims to enhance the performance of ACO algorithm by integrating fuzzy logic in order to cope with the multiple contradicting objectives and generate quality solutions by in parallel identifying the mission areas of each USV to reach the desired targets. The objectives that are taken into account are the minimization of traveled distance and energy consumption, and the maximization of path smoothness. A comparative evaluation is conducted among ACO and fuzzy inference systems, Mamdani (ACO-M) and Takagi–Sugeno–Kang (ACO-TSK). The results showed that depending on the needs of the application, each methodology can contribute respectively. ACO-M generates better paths but ACO-TSK presents higher computation efficiency.
ProxMaP: Proximal Occupancy Map Prediction for Efficient Indoor Robot Navigation

Planning a path for a mobile robot typically requires building a map (e.g., an occupancy grid) of the environment as the robot moves around. While navigating in an unknown environment, the map built by the robot online may have many as-yet-unknown regions. A conservative planner may avoid such regions taking a longer time to navigate to the goal. Instead, if a robot is able to correctly predict the occupancy in the occluded regions, the robot may navigate efficiently. We present a self-supervised occupancy prediction technique, ProxMaP, to predict the occupancy within the proximity of the robot to enable faster navigation. We show that ProxMaP generalizes well across realistic and real domains, and improves the robot navigation efficiency in simulation by 12.40% against a traditional navigation method. We share our findings and code at https://raaslab.org/projects/ProxMaP.
Task and Motion Planning with Large Language Models for Object Rearrangement

Multi-object rearrangement is a crucial skill for service robots, and commonsense reasoning is frequently needed in this process. However, achieving commonsense arrangements requires knowledge about objects, which is hard to transfer to robots. Large language models (LLMs) are one potential source of this knowledge, but they do not naively capture information about plausible physical arrangements of the world. We propose LLM-GROP, which uses prompting to extract commonsense knowledge about semantically valid object configurations from an LLM and instantiates them with a task and motion planner in order to generalize to varying scene geometry. LLM-GROP allows us to go from natural-language commands to human-aligned object rearrangement in varied environments. Based on human evaluations, our approach achieves the highest rating while outperforming competitive baselines in terms of success rate while maintaining comparable cumulative action costs. Finally, we demonstrate a practical implementation of LLM-GROP on a mobile manipulator in real-world scenarios. Supplementary materials are available at: https://sites.google.com/view/llm-grop
NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Rendering

Autonomous robotic tasks require actively perceiving the environment to achieve application-specific goals. In this paper, we address the problem of positioning an RGB camera to collect the most informative images to represent an unknown scene, given a limited measurement budget. We propose a novel mapless planning framework to iteratively plan the next best camera view based on collected image measurements. A key aspect of our approach is a new technique for uncertainty estimation in image-based neural rendering, which guides measurement acquisition at the most uncertain view among view candidates, thus maximising the information value during data collection. By incrementally adding new measurements into our image collection, our approach efficiently explores an unknown scene in a mapless manner. We show that our uncertainty estimation is generalisable and valuable for view planning in unknown scenes. Our planning experiments using synthetic and real-world data verify that our uncertainty-guided approach finds informative images leading to more accurate scene representations when compared against baselines.
TransCAR: Transformer-Based Camera-and-Radar Fusion for 3D Object Detection

Despite radar's popularity in the automotive industry, for fusion-based 3D object detection, most existing works focus on LiDAR and camera fusion. In this paper, we propose TransCAR, a Transformer-based Camera-And-Radar fusion solution for 3D object detection. Our TransCAR consists of two modules. The first module learns 2D features from surround-view camera images and then uses a sparse set of 3D object queries to index into these 2D features. The vision-updated queries then interact with each other via transformer self-attention layer. The second module learns radar features from multiple radar scans and then applies transformer decoder to learn the interactions between radar features and vision-updated queries. The cross-attention layer within the transformer decoder can adaptively learn the soft-association between the radar features and vision-updated queries instead of hard-association based on sensor calibration only. Finally, our model estimates a bounding box per query using set-to-set Hungarian loss, which enables the method to avoid non-maximum suppression. TransCAR improves the velocity estimation using the radar scans without temporal information. The superior experimental results of our TransCAR on the challenging nuScenes datasets illustrate that our TransCAR outperforms state-of-the-art Camera-Radar fusion-based 3D object detection approaches.
Generating Executable Action Plans with Environmentally-Aware Language Models

Large Language Models (LLMs) trained using massive text datasets have recently shown promise in generating action plans for robotic agents from high-level text queries. However, these models typically do not consider the robot's environment, resulting in generated plans that may not actually be executable, due to ambiguities in the planned actions or environmental constraints. In this paper, we propose an approach to generate environmentally-aware action plans that agents are better able to execute. Our approach involves integrating environmental objects and object relations as additional inputs into LLM action plan generation to provide the system with an awareness of its surroundings, resulting in plans where each generated action is mapped to objects present in the scene. We also design a novel scoring function that, along with generating the action steps and associating them with objects, helps the system disambiguate among object instances and take into account their states. We evaluated our approach using the VirtualHome simulator and the ActivityPrograms knowledge base and found that action plans generated from our system had a 310% improvement in executability and a 147% improvement in correctness over prior work. The complete code and a demo of our method is publicly available at https://github.com/hri-ironlab/scene_aware_language_planner.
Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet

Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performance have made it popular among researchers. With the increase in data and model complexity, optimization and fine-tuning models play a vital and more challenging role than before. This paper presents a comparative study evaluating the effect of image preprocessing and different optimization techniques and the importance of fine-tuning different UNet segmentation models for breast ultrasound images. Optimization and fine-tuning techniques have been applied to enhance the performance of UNet, Sharp UNet, and Attention UNet. Building upon this progress, we designed a novel approach by combining Sharp UNet and Attention UNet, known as Sharp Attention UNet. Our analysis yielded the following quantitative evaluation metrics for the Sharp Attention UNet: the Dice coefficient, specificity, sensitivity, and F1 score values obtained were 0.93, 0.99, 0.94, and 0.94, respectively. In addition, McNemar's statistical test was applied to assess significant differences between the approaches. Across a number of measures, our proposed model outperformed all other models, resulting in improved breast lesion segmentation.
FortunChain: EC-VRF-Based Scalable Blockchain System for Realizing State Sharding

The low throughput of traditional blockchain can hardly support the demand of high-frequency and high-concurrent application scenarios, which severely restricts the practical application of blockchain and becomes a bottleneck for blockchain development. Sharding is a potential solution to this issue. Existing sharding schemes mostly use complicated PBFT or its variants consensus protocols, with diminishing consensus performance as the number of nodes grows, and global consistency of transaction data is a challenge due to parallel block generation. Aiming at the above problems, we propose an efficient and scalable sharding blockchain system named FortunChain that comprises a verification chain and multiple sharding chains. In the FortunChain, a novel Tyche consensus protocol is designed to select the set of sharding verification nodes and accounting nodes probabilistically leveraging the newly constructed elliptic curve-based verifiable random function (EC-VRF), which ensures the efficiency, unpredictability and security of node selection. Multi-period, fast and dynamic sharding accounting is implemented in the sharding chain to improve the performance of transaction processing. After that, the verification chain is responsible for the secondary verification by applying the batch verification algorithm to the blocks submitted by the shards and then generating the verification block. Each shard stores the sharding state by synchronizing the verification chain to realize the state sharding and ensure the global consistency of the transaction data. Simulation results show that the throughput of FortunChain can reach approximately 6540tx/s and the estimated latency is 8.59s, which demonstrates its efficiency and feasibility.