OpenRaiser/Intern-Atlas huggingface dataset:
Artificial Intuition: Efficient Classification of Scientific Abstracts
Venue: arXiv cs.AI 2024
Year: 2024

It is desirable to coarsely classify short scientific texts, such as grant or publication abstracts, for strategic insight or research portfolio management. These texts efficiently transmit dense information to experts possessing a rich body of knowledge to aid interpretation. Yet this task is remarkably difficult to automate because of brevity and the absence of context. To address this gap, we have developed a novel approach to generate and appropriately assign coarse domain-specific labels. We show that a Large Language Model (LLM) can provide metadata essential to the task, in a process akin to the augmentation of supplemental knowledge representing human intuition, and propose a workflow. As a pilot study, we use a corpus of award abstracts from the National Aeronautics and Space Administration (NASA). We develop new assessment tools in concert with established performance metrics.
Filtering After Shading With Stochastic Texture Filtering
Venue: arXiv cs.CV 2024
Year: 2024

2D texture maps and 3D voxel arrays are widely used to add rich detail to the surfaces and volumes of rendered scenes, and filtered texture lookups are integral to producing high-quality imagery. We show that applying the texture filter after evaluating shading generally gives more accurate imagery than filtering textures before BSDF evaluation, as is current practice. These benefits are not merely theoretical, but are apparent in common cases. We demonstrate that practical and efficient filtering after shading is possible through the use of stochastic sampling of texture filters. Stochastic texture filtering offers additional benefits, including efficient implementation of high-quality texture filters and efficient filtering of textures stored in compressed and sparse data structures, including neural representations. We demonstrate applications in both real-time and offline rendering and show that the additional error from stochastic filtering is minimal. We find that this error is handled well by either spatiotemporal denoising or moderate pixel sampling rates.
Investigating User Perceptions of Collaborative Agenda Setting in Virtual Health Counseling Session
Venue: arXiv cs.HC 2024
Year: 2024

Virtual health counselors offer the potential to provide users with information and counseling in complex areas such as disease management and health education. However, ensuring user engagement is challenging, particularly when the volume of information and length of counseling sessions increase. Agenda setting a clinical counseling technique where a patient and clinician collaboratively decide on session topics is an effective approach to tailoring discussions for individual patient needs and sustaining engagement. We explore the effectiveness of agenda setting in a virtual counselor system designed to counsel women for breast cancer genetic testing. In a between subjects study, we assessed three versions of the system with varying levels of user control in the system's agenda setting approach. We found that participants' knowledge improved across all conditions. Although our results showed that any type of agenda setting was perceived as useful, regardless of user control, interviews revealed a preference for more collaboration and user involvement in the agenda setting process. Our study highlights the importance of using patient-centered approaches, such as tailored discussions, when using virtual counselors in healthcare.
Better Sampling, towards Better End-to-end Small Object Detection
Venue: arXiv cs.CV 2024
Year: 2024

While deep learning-based general object detection has made significant strides in recent years, the effectiveness and efficiency of small object detection remain unsatisfactory. This is primarily attributed not only to the limited characteristics of such small targets but also to the high density and mutual overlap among these targets. The existing transformer-based small object detectors do not leverage the gap between accuracy and inference speed. To address challenges, we propose methods enhancing sampling within an end-to-end framework. Sample Points Refinement (SPR) constrains localization and attention, preserving meaningful interactions in the region of interest and filtering out misleading information. Scale-aligned Target (ST) integrates scale information into target confidence, improving classification for small object detection. A task-decoupled Sample Reweighting (SR) mechanism guides attention toward challenging positive examples, utilizing a weight generator module to assess the difficulty and adjust classification loss based on decoder layer outcomes. Comprehensive experiments across various benchmarks reveal that our proposed detector excels in detecting small objects. Our model demonstrates a significant enhancement, achieving a 2.9\% increase in average precision (AP) over the state-of-the-art (SOTA) on the VisDrone dataset and a 1.7\% improvement on the SODA-D dataset.
Rényi Common Information for Doubly Symmetric Binary Sources
Venue: arXiv cs.IT 2024
Year: 2024

In this note, we provide analytic expressions for the Rényi common information of orders in $(1,\infty)$ for the doubly symmetric binary source (DSBS). Until now, analytic expressions for the Rényi common information of all orders in $[0,\infty]$ have been completely known for this source. We also consider the Rényi common information of all orders in $[-\infty,0)$ and evaluate it for the DSBS. We provide a sufficient condition under which the Rényi common information of such orders coincides with Wyner's common information for the DSBS. Based on numerical analysis, we conjecture that there is a certain phase transition as the crossover probability increasing for the Rényi common information of negative orders for the DSBS. Our proofs are based on a lemma on splitting of the entropy and the analytic expression of relaxed Wyner's common information.
Contour-weighted loss for class-imbalanced image segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Image segmentation is critically important in almost all medical image analysis for automatic interpretations and processing. However, it is often challenging to perform image segmentation due to data imbalance between intra- and inter-class, resulting in over- or under-segmentation. Consequently, we proposed a new methodology to address the above issue, with a compact yet effective contour-weighted loss function. Our new loss function incorporates a contour-weighted cross-entropy loss and separable dice loss. The former loss extracts the contour of target regions via morphological erosion and generates a weight map for the cross-entropy criterion, whereas the latter divides the target regions into contour and non-contour components through the extracted contour map, calculates dice loss separately, and combines them to update the network. We carried out abdominal organ segmentation and brain tumor segmentation on two public datasets to assess our approach. Experimental results demonstrated that our approach offered superior segmentation, as compared to several state-of-the-art methods, while in parallel improving the robustness of those popular state-of-the-art deep models through our new loss function. The code is available at https://github.com/huangzyong/Contour-weighted-Loss-Seg.
Chronological Analysis of Rigvedic Mandalas using Social Networks
Venue: arXiv cs.SI 2024
Year: 2024

Establishing the chronology of the Vedas has interested scholars for the last two centuries. The oldest among them is Rig-Veda which has ten Mandalas, each composed separately. In this paper, we look at deciphering plausible pointers to the internal chronology of the Mandalas, by focusing on Gods and Goddesses worshiped in different Mandalas. We apply text analysis to the Mandalas using Clustering Techniques based on Cosine Similarity. Then we represent the association of deities with Mandalas using a grid-based Social Network that is amenable to chronological analysis and demonstrates the benefits of using Social Network Analysis for the problem at hand. Further, we analyze references to rivers to arrive at additional correlations. The approach used can be deployed generically to analyze other kinds of references and mentions and arrive at more substantive inferences.
Guarded Successor: A Novel Temporal Logic
Venue: arXiv cs.LO 2024
Year: 2024

We present GS (Guarded Successor), a novel decidable temporal logic with several unique distinctive features. Among those, it allows infinitely many data values that come not only with equality but with a somehow rich theory too: the first-order theory of atomless Boolean algebras. The language also distinguishes between inputs and outputs, and has a decision procedure for determining whether for all inputs exist outputs, at each point of time. Moreover, and maybe most surprisingly, the data values can be nothing but sentences in GS itself. We also present a non-temporal fragment called NSO (Nullary Second Order) that enjoys merely this last property. These results are crucial necessary ingredients in any meaningful design of safe AI. Finally, all those results are obtained from a novel treatment of the first-order theory of atomless Boolean algebras.
The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: What can they learn from each other?
Venue: arXiv cs.CY 2024
Year: 2024

On the whole, the U.S. Algorithmic Accountability Act of 2022 (US AAA) is a pragmatic approach to balancing the benefits and risks of automated decision systems. Yet there is still room for improvement. This commentary highlights how the US AAA can both inform and learn from the European Artificial Intelligence Act (EU AIA).
Transfer Learning with Pseudo Multi-Label Birdcall Classification for DS@GT BirdCLEF 2024
Venue: arXiv cs.SD 2024
Year: 2024

We present working notes for the DS@GT team on transfer learning with pseudo multi-label birdcall classification for the BirdCLEF 2024 competition, focused on identifying Indian bird species in recorded soundscapes. Our approach utilizes production-grade models such as the Google Bird Vocalization Classifier, BirdNET, and EnCodec to address representation and labeling challenges in the competition. We explore the distributional shift between this year's edition of unlabeled soundscapes representative of the hidden test set and propose a pseudo multi-label classification strategy to leverage the unlabeled data. Our highest post-competition public leaderboard score is 0.63 using BirdNET embeddings with Bird Vocalization pseudo-labels. Our code is available at https://github.com/dsgt-kaggle-clef/birdclef-2024
Homogeneous Speaker Features for On-the-Fly Dysarthric and Elderly Speaker Adaptation
Venue: arXiv cs.SD 2024
Year: 2024

The application of data-intensive automatic speech recognition (ASR) technologies to dysarthric and elderly adult speech is confronted by their mismatch against healthy and nonaged voices, data scarcity and large speaker-level variability. To this end, this paper proposes two novel data-efficient methods to learn homogeneous dysarthric and elderly speaker-level features for rapid, on-the-fly test-time adaptation of DNN/TDNN and Conformer ASR models. These include: 1) speaker-level variance-regularized spectral basis embedding (VR-SBE) features that exploit a special regularization term to enforce homogeneity of speaker features in adaptation; and 2) feature-based learning hidden unit contributions (f-LHUC) transforms that are conditioned on VR-SBE features. Experiments are conducted on four tasks across two languages: the English UASpeech and TORGO dysarthric speech datasets, the English DementiaBank Pitt and Cantonese JCCOCC MoCA elderly speech corpora. The proposed on-the-fly speaker adaptation techniques consistently outperform baseline iVector and xVector adaptation by statistically significant word or character error rate reductions up to 5.32% absolute (18.57% relative) and batch-mode LHUC speaker adaptation by 2.24% absolute (9.20% relative), while operating with real-time factors speeding up to 33.6 times against xVectors during adaptation. The efficacy of the proposed adaptation techniques is demonstrated in a comparison against current ASR technologies including SSL pre-trained systems on UASpeech, where our best system produces a state-of-the-art WER of 23.33%. Analyses show VR-SBE features and f-LHUC transforms are insensitive to speaker-level data quantity in testtime adaptation. T-SNE visualization reveals they have stronger speaker-level homogeneity than baseline iVectors, xVectors and batch-mode LHUC transforms.
Leveraging image captions for selective whole slide image annotation
Venue: arXiv cs.CV 2024
Year: 2024

Acquiring annotations for whole slide images (WSIs)-based deep learning tasks, such as creating tissue segmentation masks or detecting mitotic figures, is a laborious process due to the extensive image size and the significant manual work involved in the annotation. This paper focuses on identifying and annotating specific image regions that optimize model training, given a limited annotation budget. While random sampling helps capture data variance by collecting annotation regions throughout the WSIs, insufficient data curation may result in an inadequate representation of minority classes. Recent studies proposed diversity sampling to select a set of regions that maximally represent unique characteristics of the WSIs. This is done by pretraining on unlabeled data through self-supervised learning and then clustering all regions in the latent space. However, establishing the optimal number of clusters can be difficult and not all clusters are task-relevant. This paper presents prototype sampling, a new method for annotation region selection. It discovers regions exhibiting typical characteristics of each task-specific class. The process entails recognizing class prototypes from extensive histopathology image-caption databases and detecting unlabeled image regions that resemble these prototypes. Our results show that prototype sampling is more effective than random and diversity sampling in identifying annotation regions with valuable training information, resulting in improved model performance in semantic segmentation and mitotic figure detection tasks. Code is available at https://github.com/DeepMicroscopy/Prototype-sampling.
Stochastic Traveling Salesperson Problem with Neighborhoods for Object Detection
Venue: arXiv cs.DS 2024
Year: 2024

We introduce a new route-finding problem which considers perception and travel costs simultaneously. Specifically, we consider the problem of finding the shortest tour such that all objects of interest can be detected successfully. To represent a viable detection region for each object, we propose to use an entropy-based viewing score that generates a diameter-bounded region as a viewing neighborhood. We formulate the detection-based trajectory planning problem as a stochastic traveling salesperson problem with neighborhoods and propose a center-visit method that obtains an approximation ratio of O(DmaxDmin) for disjoint regions. For non-disjoint regions, our method -provides a novel finite detour in 3D, which utilizes the region's minimum curvature property. Finally, we show that our method can generate efficient trajectories compared to a baseline method in a photo-realistic simulation environment.
The Path-Label Reconciliation (PLR) Dissimilarity Measure for Gene Trees
Venue: arXiv cs.DM 2024
Year: 2024

In this study, we investigate the problem of comparing gene trees reconciled with the same species tree using a novel semi-metric, called the Path-Label Reconciliation (PLR) dissimilarity measure. This approach not only quantifies differences in the topology of reconciled gene trees, but also considers discrepancies in predicted ancestral gene-species maps and speciation/duplication events, offering a refinement of existing metrics such as Robinson-Foulds (RF) and their labeled extensions LRF and ELRF. A tunable parameter α also allows users to adjust the balance between its species map and event labeling components. We show that PLR can be computed in linear time and that it is a semi-metric. We also discuss the diameters of reconciled gene tree measures, which are important in practice for normalization, and provide initial bounds on PLR, LRF, and ELRF. To validate PLR, we simulate reconciliations and perform comparisons with LRF and ELRF. The results show that PLR provides a more evenly distributed range of distances, making it less susceptible to overestimating differences in the presence of small topological changes, while at the same time being computationally efficient. Our findings suggest that the theoretical diameter is rarely reached in practice. The PLR measure advances phylogenetic reconciliation by combining theoretical rigor with practical applicability. Future research will refine its mathematical properties, explore its performance on different tree types, and integrate it with existing bioinformatics tools for large-scale evolutionary analyses. The open source code is available at: https://pypi.org/project/parle/.
RRM: Relightable assets using Radiance guided Material extraction
Venue: arXiv cs.CV 2024
Year: 2024

Synthesizing NeRFs under arbitrary lighting has become a seminal problem in the last few years. Recent efforts tackle the problem via the extraction of physically-based parameters that can then be rendered under arbitrary lighting, but they are limited in the range of scenes they can handle, usually mishandling glossy scenes. We propose RRM, a method that can extract the materials, geometry, and environment lighting of a scene even in the presence of highly reflective objects. Our method consists of a physically-aware radiance field representation that informs physically-based parameters, and an expressive environment light structure based on a Laplacian Pyramid. We demonstrate that our contributions outperform the state-of-the-art on parameter retrieval tasks, leading to high-fidelity relighting and novel view synthesis on surfacic scenes.
The k-Facility Location Problem Via Optimal Transport: A Bayesian Study of the Percentile Mechanisms
Venue: arXiv cs.GT 2024
Year: 2024

In this paper, we investigate the $k$-Facility Location Problem ($k$-FLP) within the Bayesian Mechanism Design framework, in which agents' preferences are samples of a probability distributed on a line. Our primary contribution is characterising the asymptotic behavior of percentile mechanisms, which varies according to the distribution governing the agents' types. To achieve this, we connect the $k$-FLP and projection problems in the Wasserstein space. Owing to this relation, we show that the ratio between the expected cost of a percentile mechanism and the expected optimal cost is asymptotically bounded. Furthermore, we characterize the limit of this ratio and analyze its convergence speed. Our asymptotic study is complemented by deriving an upper bound on the Bayesian approximation ratio, applicable when the number of agents $n$ exceeds the number of facilities $k$. We also characterize the optimal percentile mechanism for a given agent's distribution through a system of $k$ equations. Finally, we estimate the optimality loss incurred when the optimal percentile mechanism is derived using an approximation of the agents' distribution rather than the actual distribution.
Knowledge Management in the Companion Cognitive Architecture
Venue: arXiv cs.AI 2024
Year: 2024

One of the fundamental aspects of cognitive architectures is their ability to encode and manipulate knowledge. Without a consistent, well-designed, and scalable knowledge management scheme, an architecture will be unable to move past toy problems and tackle the broader problems of cognition. In this paper, we document some of the challenges we have faced in developing the knowledge stack for the Companion cognitive architecture and discuss the tools, representations, and practices we have developed to overcome them. We also lay out a series of potential next steps that will allow Companion agents to play a greater role in managing their own knowledge. It is our hope that these observations will prove useful to other cognitive architecture developers facing similar challenges.
Simplifying Integration of Custom Controllers in Exergames
Venue: arXiv cs.HC 2024
Year: 2024

Despite of the established evidence in favor of exergames for physical rehabilitation their use is limited in Pakistan. In our user study with game developers (N=62), majority (67.7%) of the participants believed that exergames' popularity will increase if cheap alternatives of body tracking devices are available. Perhaps, custom controllers can be used as an affordable alternate input source in exergames but the lack of hardware programming knowledge and shortage of experience in the embedded programming attribute to the little involvement of game developers (11.3% of the participants) in the area of exergames. This paper presents a library for the integration of Arduino based (open-source and low-cost) tailored controllers to be used as input source in Unity3D (most preferred game development engine by 88.7% participants) based exergames. The interface to the library proposes a flexible and easy structure for programming and serve as a template application for a range of exergames.
SOLO: A Single Transformer for Scalable Vision-Language Modeling
Venue: arXiv cs.CV 2024
Year: 2024

We present SOLO, a single transformer for Scalable visiOn-Language mOdeling. Current large vision-language models (LVLMs) such as LLaVA mostly employ heterogeneous architectures that connect pre-trained visual encoders with large language models (LLMs) to facilitate visual recognition and complex reasoning. Although achieving remarkable performance with relatively lightweight training, we identify four primary scalability limitations: (1) The visual capacity is constrained by pre-trained visual encoders, which are typically an order of magnitude smaller than LLMs. (2) The heterogeneous architecture complicates the use of established hardware and software infrastructure. (3) Study of scaling laws on such architecture must consider three separate components - visual encoder, connector, and LLMs, which complicates the analysis. (4) The use of existing visual encoders typically requires following a pre-defined specification of image inputs pre-processing, for example, by reshaping inputs to fixed-resolution square images, which presents difficulties in processing and training on high-resolution images or those with unusual aspect ratio. A unified single Transformer architecture, like SOLO, effectively addresses these scalability concerns in LVLMs; however, its limited adoption in the modern context likely stems from the absence of reliable training recipes that balance both modalities and ensure stable training for billion-scale models. In this paper, we introduce the first open-source training recipe for developing SOLO, an open-source 7B LVLM using moderate academic resources. The training recipe involves initializing from LLMs, sequential pre-training on ImageNet and web-scale data, and instruction fine-tuning on our curated high-quality datasets. On extensive evaluation, SOLO demonstrates performance comparable to LLaVA-v1.5-7B, particularly excelling in visual mathematical reasoning.