Sensitivity and Asymptotic Analysis of Inter-Cell Interference Against Pricing for Multi-Antenna Base Stations

We thoroughly investigate the downlink beamforming problem of a two-tier network in a reversed time-division duplex system, where the interference leakage from a tier-2 base station (BS) toward nearby uplink tier-1 BSs is controlled through pricing. We show that soft interference control through the pricing mechanism does not undermine the ability to regulate interference leakage while giving flexibility to sharing the spectrum. Then, we analyze and demonstrate how the interference leakage is related to the variations of both the interference prices and the power budget. Moreover, we derive a closed-form expression for the interference leakage in an asymptotic case, where both the charging BSs and the charged BS are equipped with a large number of antennas, which provides further insights into the lowest possible interference leakage that can be achieved by the pricing mechanism

Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models

Medical Visual Question Answering (VQA) is an important challenge, as it would lead to faster and more accurate diagnoses and treatment decisions. Most existing methods approach it as a multi-class classification problem, which restricts the outcome to a predefined closed-set of curated answers. We focus on open-ended VQA and motivated by the recent advances in language models consider it as a generative task. Leveraging pre-trained language models, we introduce a novel method particularly suited for small, domain-specific, medical datasets. To properly communicate the medical images to the language model, we develop a network that maps the extracted visual features to a set of learnable tokens. Then, alongside the question, these learnable tokens directly prompt the language model. We explore recent parameter-efficient fine-tuning strategies for language models, which allow for resource- and data-efficient fine-tuning. We evaluate our approach on the prime medical VQA benchmarks, namely, Slake, OVQA and PathVQA. The results demonstrate that our approach outperforms existing methods across various training settings while also being computationally efficient.</p

Unlocking Spatial Comprehension in Text-to-Image Diffusion Models

We propose CompFuser, an image generation pipeline that enhances spatial comprehension and attribute assignment in text-to-image generative models. Our pipeline enables the interpretation of instructions defining spatial relationships between objects in a scene, such as `An image of a gray cat on the left of an orange dog', and generate corresponding images. This is especially important in order to provide more control to the user. CompFuser overcomes the limitation of existing text-to-image diffusion models by decoding the generation of multiple objects into iterative steps: first generating a single object and then editing the image by placing additional objects in their designated positions. To create training data for spatial comprehension and attribute assignment we introduce a synthetic data generation process, that leverages a frozen large language model and a frozen layout-based diffusion model for object placement. We compare our approach to strong baselines and show that our model outperforms state-of-the-art image generation models in spatial comprehension and attribute assignment, despite being 3x to 5x smaller in parameters

Any-Shift Prompting for Generalization over Distributions

Image-language models with prompt learning have shown remarkable advances in numerous downstream vision tasks. Nevertheless, conventional prompt learning methods overfit their training distribution and lose the generalization ability on test distributions. To improve generalization across various distribution shifts, we propose any-shift prompting: a general probabilistic inference framework that considers the relationship between training and test distributions during prompt learning. We explicitly connect training and test distributions in the latent space by constructing training and test prompts in a hierarchical architecture. Within this framework, the test prompt exploits the distribution relationships to guide the generalization of the CLIP image-language model from training to any test distribution. To effectively encode the distribution information and their relationships, we further introduce a transformer inference network with a pseudo-shift training mechanism. The network generates the tailored test prompt with both training and test information in a feedforward pass, avoiding extra training costs at test time. Extensive experiments on twenty-three datasets demonstrate the effectiveness of any-shift prompting on the generalization over various distribution shifts

Multi-disciplinary Collaborations in Measurement of Human Motion

Comparative Medicine - OneHealth and Comparative Medicine Poster SessionBioengineering is a broad and rapidly-growing discipline defined as the application of engineering principles to biological systems. Although bioengineering is diverse in nature, the study of human movement is common to many bioengineering subdisciplines such as biomechanics and biometrics. Biomechanics is the science that examines the forces acting upon and within a biological structure and effects produced by such forces [1]. Measurement of ground reaction forces, limb motion, and muscle activation are fundamental research components in musculoskeletal biomechanics. Researchers in this field have used these measurements to quantify human gait, balance, and posture in a multitude of applications including age-related fall risk [2-4], muscle fatigue [5-7], and balance-related pathologies such as Parkinson's disease [8-10], and stroke [11, 12]. Additionally, these measurements play a vital role in computational biomechanics models. For example, the inverse dynamics method incorporates measured ground reaction forces and body motions to calculate the net reaction forces and torques acting on body joints [13]. Biometrics is the science of confirming or discovering individuals' identities based on their specific biological or behavioral traits [14]. Gait is one such modality which can be used for biometric identification. It is based on the uniqueness of an individual's locomotion patterns [15]. In addition, we are interested in high-speed video analyses of micro-saccades and blink reflexes for spoof-proofing of biometric identification systems, biometric identification, and psychometry. We have shown that startle blink intensity can be derived from high- speed video [18], enabling video-based psychophysiological biometrics for detection of subject-specific affective-cognitive information [19]. The Human Motion Laboratory at the University of Missouri - Kansas City is dedicated to measuring the characteristics of human motion. The lab includes a VICON MX 6-camera motion capture system, 4 AMTI OR6-6 force platforms, and a Delsys Myomonitor IV 16-channel wireless EMG system. This equipment represents an experimental infrastructure mutually supporting the biomechanics and biometrics research efforts of four research labs. The scope of these research efforts includes aging, affective computing, psychophysiological biometrics, orthopedics, and human dynamics pathology. The lab capitalizes on a synergistic environment for characterization and measurement of human movement and the interrelated nature of the research activities. The four main research areas that the Human Motion Laboratory supports are: •Computational Biomechanics •Biometrics of Human Motion •Experimental Biomechanics •Body Area Sensor Network

Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models

Medical Visual Question Answering (VQA) is an important challenge, as it would lead to faster and more accurate diagnoses and treatment decisions. Most existing methods approach it as a multi-class classification problem, which restricts the outcome to a predefined closed-set of curated answers. We focus on open-ended VQA and motivated by the recent advances in language models consider it as a generative task. Leveraging pre-trained language models, we introduce a novel method particularly suited for small, domain-specific, medical datasets. To properly communicate the medical images to the language model, we develop a network that maps the extracted visual features to a set of learnable tokens. Then, alongside the question, these learnable tokens directly prompt the language model. We explore recent parameter-efficient fine-tuning strategies for language models, which allow for resource- and data-efficient fine-tuning. We evaluate our approach on the prime medical VQA benchmarks, namely, Slake, OVQA and PathVQA. The results demonstrate that our approach outperforms existing methods across various training settings while also being computationally efficient

LifeLonger: A Benchmark for Continual Disease Classification

Deep learning models have shown a great effectiveness in recognition of findings in medical images. However, they cannot handle the ever-changing clinical environment, bringing newly annotated medical data from different sources. To exploit the incoming streams of data, these models would benefit largely from sequentially learning from new samples, without forgetting the previously obtained knowledge. In this paper we introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection, by applying existing state-of-the-art continual learning methods. In particular, we consider three continual learning scenarios, namely, task and class incremental learning and the newly defined cross-domain incremental learning. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge. We perform a thorough analysis of the performance and examine how the well-known challenges of continual learning, such as the catastrophic forgetting exhibit themselves in this setting. The encouraging results demonstrate that continual learning has a major potential to advance disease classification and to produce a more robust and efficient learning framework for clinical settings. The code repository, data partitions and baseline results for the complete benchmark will be made publicly available

From Melanoma Development to RNA-Modified Dendritic Cell Vaccines: Highlighting the Lessons From the Past

Although melanoma remains the deadliest skin cancer, the current treatment has not resulted in the desired outcomes. Unlike chemotherapy, immunotherapy has provided more tolerable approaches and revolutionized cancer therapy. Although dendritic cell-based vaccines have minor side effects, the undesirable response rates of traditional approaches have posed questions about their clinical translation. The immunosuppressive tumor microenvironment can be the underlying reason for their low response rates. Immune checkpoints and indoleamine 2,3-dioxygenase have been implicated in the induction of immunosuppressive tumor microenvironment. Growing evidence indicates that the mitogen-activated protein kinase (MAPK) and phosphatidylinositol 3-kinase/Protein kinase B (PKB) (PI3K/AKT) pathways, as the main oncogenic pathways of melanoma, can upregulate the tumoral immune checkpoints, like programmed death-ligand 1. This study briefly represents the main oncogenic pathways of melanoma and highlights the cross-talk between these oncogenic pathways with indoleamine 2,3-dioxygenase, tumoral immune checkpoints, and myeloid-derived suppressor cells. Moreover, this study sheds light on a novel tumor antigen on melanoma, which has substantial roles in tumoral immune checkpoints expression, indoleamine 2,3-dioxygenase secretion, and stimulating the oncogenic pathways. Finally, this review collects the lessons from the previous unsuccessful trials and integrates their lessons with new approaches in RNA-modified dendritic cell vaccines. Unlike traditional approaches, the advances in single-cell RNA-sequencing techniques and RNA-modified dendritic cell vaccines along with combined therapy of the immune checkpoint inhibitors, indoleamine 2,3-dioxygenase inhibitor, and RNA-modified dendritic cell-based vaccine can overcome these auto-inductive loops and pave the way for developing robust dendritic cell-based vaccines with the most favorable response rate and the least side effects

Regulation of ctla-4 and pd-l1 expression in relapsing-remitting multiple sclerosis patients after treatment with fingolimod, ifnβ-1α, glatiramer acetate, and dimethyl fumarate drugs

Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system (CNS) that is characterized by inflammation which typically results in significant impairment in most patients. Immune checkpoints act as co-stimulatory and co-inhibitory molecules and play a fundamental role in keeping the equilibrium of the immune system. Cytotoxic T-lymphocyte antigen-4 (CTLA-4) and Programmed death-ligand 1 (PD-L1), as inhibitory immune checkpoints, participate in terminating the development of numerous autoimmune diseases, including MS. We assessed the CTLA-4 and PD-L1 gene expression in the different cell types of peripheral blood mononuclear cells of MS patients using single-cell RNA-seq data. Additionally, this study outlines how CTLA-4 and PD-L1 expression was altered in the PBMC samples of relapsing-remitting multiple sclerosis (RRMS) patients compared to the healthy group. Finally, it investigates the impact of various MS-related treatments in the CTLA-4 and PD-L1 expression to restrain autoreactive T cells and stop the development of MS autoimmunity

Bayesian Prompt Learning for Image-Language Model Generalization

Foundational image-language models have generated considerable interest due to their efficient adaptation to downstream tasks by prompt learning. Prompt learning treats part of the language model input as trainable while freezing the rest, and optimizes an Empirical Risk Minimization objective. However, Empirical Risk Minimization is known to suffer from distributional shifts which hurt generalizability to prompts unseen during training. By leveraging the regularization ability of Bayesian methods, we frame prompt learning from the Bayesian perspective and formulate it as a variational inference problem. Our approach regularizes the prompt space, reduces overfitting to the seen prompts and improves the prompt generalization on unseen prompts. Our framework is implemented by modeling the input prompt space in a probabilistic manner, as an a priori distribution which makes our proposal compatible with prompt learning approaches that are unconditional or conditional on the image. We demonstrate empirically on 15 benchmarks that Bayesian prompt learning provides an appropriate coverage of the prompt space, prevents learning spurious features, and exploits transferable invariant features. This results in better generalization of unseen prompts, even across different datasets and domains