Miracle-dz/UniFAR-FTUs huggingface dataset:
Hypersusceptibility to non-nucleoside reverse transcriptase inhibitors in HIV-1: clinical, phenotypic and genotypic correlates

Objective: The routine use of phenotypic drug resistance testing in patient management has revealed that many HIV-1 strains possess significantly increased drug sensitivity, or ‘hypersusceptibility’ compared with wild-type viruses. This study describes hypersusceptibility to non-nucleoside reverse transcriptase inhibitors (NNRTI) and was designed to determine the prevalence of and viral characteristics associated with NNRTI hypersusceptibility in patient-derived viruses. Methods: Retrospective analyses were performed on a large clinical laboratory dataset containing phenotypic drug susceptibility and genotypic sequence results from HIV-1 patient isolates. Genetically engineered viruses were used to confirm the role of certain nucleoside reverse transcriptase inhibitor (NRTI)-resistance mutations in NNRTI hypersusceptibility. Results: Hypersusceptibility to delavirdine, efavirenz and nevirapine was detected in 10.7, 10.8 and 8.0% of more than 17 000 consecutive plasma samples submitted for phenotypic susceptibility testing. In analyses limited to a subset of viruses derived from patients with known treatment histories, NNRTI hypersusceptibility was observed significantly more frequently among viruses from NRTI experienced/NNRTI-naive patients compared with viruses from NRTI/NNRTI-naive patients. Significant inverse correlations between NRTI and NNRTI susceptibility exist among the viruses from NRTI-experienced patients. Analyses of viruses classified according to their NNRTI susceptibility identified 18 positions in reverse transcriptase where substitutions were significantly associated with NNRTI hypersusceptibility. Conclusions: NNRTI hypersusceptibility is common among patient HIV-1 isolates, especially in NRTI-resistant viruses. Genotypic correlates of hypersusceptibility are complex and not easily defined by a simple analysis of NRTI-associated resistance mutations. NNRTI hypersusceptibility may provide an explanation for the superior virologic response to NNRTI-containing salvage regimens observed in NRTI-experienced patients in several clinical trials.
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity

Retrieval-Augmented Large Language Models (LLMs), which incorporate the non-parametric knowledge from external knowledge bases into LLMs, have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA). However, even though there are various approaches dealing with queries of different complexities, they either handle simple queries with unnecessary computational overhead or fail to adequately address complex multi-step queries; yet, not all user requests fall into only one of the simple or complex categories. In this work, we propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs from the simplest to the most sophisticated ones based on the query complexity. Also, this selection process is operationalized with a classifier, which is a smaller LM trained to predict the complexity level of incoming queries with automatically collected labels, obtained from actual predicted outcomes of models and inherent inductive biases in datasets. This approach offers a balanced strategy, seamlessly adapting between the iterative and single-step retrieval-augmented LLMs, as well as the no-retrieval methods, in response to a range of query complexities. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems, compared to relevant baselines including the adaptive retrieval approaches. Code is available at: https://github.com/starsuzi/Adaptive-RAG.
An architecture model for supporting application integration decisions

For a successful information systems architecture management, reference models and decision rules must be provided. In this paper, an architecture model and design rules are proposed that are intended to support information systems architects in integration decisions. The goal is to improve application architecture by balancing intra-application integration costs and inter-application integration costs. Based on a general discussion of architecture models and integration costs, related work is reviewed, and a model is proposed that combines two existing models. Using that model, existing application design is analyzed, and an ideal generic application design is presented. In order to minimize inter-application integration costs, three approaches to optimal application interfacing are discussed. The work is based on experience from several integration projects at large Swiss and German companies, mainly from the service sector.
Estimating Free and Added Sugar Intakes in New Zealand

The reduction of free or added sugar intake (sugars added to food and drinks as a sweetener) is almost universally recommended to reduce the risk of obesity-related diseases and dental caries. The World Health Organisation recommends intakes of free sugars of less than 10% of energy intake. However, estimating and monitoring intakes at the population level is challenging because free sugars cannot be analytically distinguished from naturally occurring sugars and most national food composition databases do not include data on free or added sugars. We developed free and added sugar estimates for the New Zealand (NZ) food composition database (FOODfiles 2010) by adapting a method developed for Australia. We reanalyzed the 24 h recall dietary data collected for 4721 adults aged 15 years and over participating in the nationally representative 2008/09 New Zealand Adult Nutrition Survey to estimate free and added sugar intakes. The median estimated intake of free and added sugars was 57 and 49 g/day respectively and 42% of adults consumed less than 10% of their energy intake from free sugars. This approach provides more direct estimates of the free and added sugar contents of New Zealand foods than previously available and will enable monitoring of adherence to free sugar intake guidelines in future.
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central Server

In federated learning, data heterogeneity significantly impacts performance. A typical solution involves segregating these parameters into shared and personalized components, a concept also relevant in multi-task learning. Addressing this, we propose"Loop Improvement"(LI), a novel method enhancing this separation and feature extraction without necessitating a central server or data interchange among participants. Our experiments reveal LI's superiority in several aspects: In personalized federated learning environments, LI consistently outperforms the advanced FedALA algorithm in accuracy across diverse scenarios. Additionally, LI's feature extractor closely matches the performance achieved when aggregating data from all clients. In global model contexts, employing LI with stacked personalized layers and an additional network also yields comparable results to combined client data scenarios. Furthermore, LI's adaptability extends to multi-task learning, streamlining the extraction of common features across tasks and obviating the need for simultaneous training. This approach not only enhances individual task performance but also achieves accuracy levels on par with classic multi-task learning methods where all tasks are trained simultaneously. LI integrates a loop topology with layer-wise and end-to-end training, compatible with various neural network models. This paper also delves into the theoretical underpinnings of LI's effectiveness, offering insights into its potential applications. The code is on https://github.com/axedge1983/LI
Cohen–Macaulayness of Vertex Splittable Monomial Ideals

In this paper, we give a new criterion for the Cohen–Macaulayness of vertex splittable ideals, a family of monomial ideals recently introduced by Moradi and Khosh-Ahang. Our result relies on a Betti splitting of the ideal and provides an inductive way of checking the Cohen–Macaulay property. As a result, we obtain characterizations for Gorenstein, level and pseudo-Gorenstein vertex splittable ideals. Furthermore, we provide new and simpler combinatorial proofs of known Cohen–Macaulay criteria for several families of monomial ideals, such as (vector-spread) strongly stable ideals and (componentwise) polymatroidals. Finally, we characterize the family of bi-Cohen–Macaulay graphs by the novel criterion for the Cohen–Macaulayness of vertex splittable ideals.
AI and Memory Wall

The availability of unprecedented unsupervised training data, along with neural scaling laws, has resulted in an unprecedented surge in model size and compute requirements for serving/training LLMs. However, the main performance bottleneck is increasingly shifting to memory bandwidth. Over the past 20 years, peak server hardware FLOPS has been scaling at 3.0x/2yrs, outpacing the growth of DRAM and interconnect bandwidth, which have only scaled at 1.6 and 1.4 times every 2 years, respectively. This disparity has made memory, rather than compute, the primary bottleneck in AI applications, particularly in serving. Here, we analyze encoder and decoder Transformer models and show how memory bandwidth can become the dominant bottleneck for decoder models. We argue for a redesign in model architecture, training, and deployment strategies to overcome this memory limitation.
Scene-Graph ViT: End-to-End Open-Vocabulary Visual Relationship Detection

Visual relationship detection aims to identify objects and their relationships in images. Prior methods approach this task by adding separate relationship modules or decoders to existing object detection architectures. This separation increases complexity and hinders end-to-end training, which limits performance. We propose a simple and highly efficient decoder-free architecture for open-vocabulary visual relationship detection. Our model consists of a Transformer-based image encoder that represents objects as tokens and models their relationships implicitly. To extract relationship information, we introduce an attention mechanism that selects object pairs likely to form a relationship. We provide a single-stage recipe to train this model on a mixture of object and relationship detection data. Our approach achieves state-of-the-art relationship detection performance on Visual Genome and on the large-vocabulary GQA benchmark at real-time inference speeds. We provide analyses of zero-shot performance, ablations, and real-world qualitative examples.
Protein phosphatase 2A dysfunction in Alzheimer’s disease

Protein phosphatase 2A (PP2A) is a large family of enzymes that account for the majority of brain Ser/Thr phosphatase activity. While PP2A enzymes collectively modulate most cellular processes, sophisticated regulatory mechanisms are ultimately responsible for ensuring isoform-specific substrate specificity. Of particular interest to the Alzheimer’s disease (AD) field, alterations in PP2A regulators and PP2A catalytic activity, subunit expression, methylation and/or phosphorylation, have been reported in AD-affected brain regions. “PP2A” dysfunction has been linked to tau hyperphosphorylation, amyloidogenesis and synaptic deficits that are pathological hallmarks of this neurodegenerative disorder. Deregulation of PP2A enzymes also affects the activity of many Ser/Thr protein kinases implicated in AD. This review will more specifically discuss the role of the PP2A/Bα holoenzyme and PP2A methylation in AD pathogenesis. The PP2A/Bα isoform binds to tau and is the primary tau phosphatase. Its deregulation correlates with increased tau phosphorylation in vivo and in AD. Disruption of PP2A/Bα-tau protein interactions likely contribute to tau deregulation in AD. Significantly, alterations in one-carbon metabolism that impair PP2A methylation are associated with increased risk for sporadic AD, and enhanced AD-like pathology in animal models. Experimental studies have linked deregulation of PP2A methylation with down-regulation of PP2A/Bα, enhanced phosphorylation of tau and amyloid precursor protein, tau mislocalization, microtubule destabilization and neuritic defects. While it remains unclear what are the primary events that underlie “PP2A” dysfunction in AD, deregulation of PP2A enzymes definitely affects key players in the pathogenic process. As such, there is growing interest in developing PP2A-centric therapies for AD, but this may be a daunting task without a better understanding of the regulation and function of specific PP2A enzymes.
Policy Mirror Descent with Lookahead

Policy Mirror Descent (PMD) stands as a versatile algorithmic framework encompassing several seminal policy gradient algorithms such as natural policy gradient, with connections with state-of-the-art reinforcement learning (RL) algorithms such as TRPO and PPO. PMD can be seen as a soft Policy Iteration algorithm implementing regularized 1-step greedy policy improvement. However, 1-step greedy policies might not be the best choice and recent remarkable empirical successes in RL such as AlphaGo and AlphaZero have demonstrated that greedy approaches with respect to multiple steps outperform their 1-step counterpart. In this work, we propose a new class of PMD algorithms called $h$-PMD which incorporates multi-step greedy policy improvement with lookahead depth $h$ to the PMD update rule. To solve discounted infinite horizon Markov Decision Processes with discount factor $\gamma$, we show that $h$-PMD which generalizes the standard PMD enjoys a faster dimension-free $\gamma^h$-linear convergence rate, contingent on the computation of multi-step greedy policies. We propose an inexact version of $h$-PMD where lookahead action values are estimated. Under a generative model, we establish a sample complexity for $h$-PMD which improves over prior work. Finally, we extend our result to linear function approximation to scale to large state spaces. Under suitable assumptions, our sample complexity only involves dependence on the dimension of the feature map space instead of the state space size.
MyVLM: Personalizing VLMs for User-Specific Queries

Recent large-scale vision-language models (VLMs) have demonstrated remarkable capabilities in understanding and generating textual descriptions for visual content. However, these models lack an understanding of user-specific concepts. In this work, we take a first step toward the personalization of VLMs, enabling them to learn and reason over user-provided concepts. For example, we explore whether these models can learn to recognize you in an image and communicate what you are doing, tailoring the model to reflect your personal experiences and relationships. To effectively recognize a variety of user-specific concepts, we augment the VLM with external concept heads that function as toggles for the model, enabling the VLM to identify the presence of specific target concepts in a given image. Having recognized the concept, we learn a new concept embedding in the intermediate feature space of the VLM. This embedding is tasked with guiding the language model to naturally integrate the target concept in its generated response. We apply our technique to BLIP-2 and LLaVA for personalized image captioning and further show its applicability for personalized visual question-answering. Our experiments demonstrate our ability to generalize to unseen images of learned concepts while preserving the model behavior on unrelated inputs.
Analysing heavy-tail properties of Stochastic Gradient Descent by means of Stochastic Recurrence Equations

In recent works on the theory of machine learning, it has been observed that heavy tail properties of Stochastic Gradient Descent (SGD) can be studied in the probabilistic framework of stochastic recursions. In particular, G\"{u}rb\"{u}zbalaban et al. (arXiv:2006.04740) considered a setup corresponding to linear regression for which iterations of SGD can be modelled by a multivariate affine stochastic recursion $X_k=A_k X_{k-1}+B_k$, for independent and identically distributed pairs $(A_k, B_k)$, where $A_k$ is a random symmetric matrix and $B_k$ is a random vector. In this work, we will answer several open questions of the quoted paper and extend their results by applying the theory of irreducible-proximal (i-p) matrices.
Powerful Lossy Compression for Noisy Images

Image compression and denoising represent fundamental challenges in image processing with many real-world applications. To address practical demands, current solutions can be categorized into two main strategies: 1) sequential method; and 2) joint method. However, sequential methods have the disadvantage of error accumulation as there is information loss between multiple individual models. Recently, the academic community began to make some attempts to tackle this problem through end-to-end joint methods. Most of them ignore that different regions of noisy images have different characteristics. To solve these problems, in this paper, our proposed signal-to-noise ratio~(SNR) aware joint solution exploits local and non-local features for image compression and denoising simultaneously. We design an end-to-end trainable network, which includes the main encoder branch, the guidance branch, and the signal-to-noise ratio~(SNR) aware branch. We conducted extensive experiments on both synthetic and real-world datasets, demonstrating that our joint solution outperforms existing state-of-the-art methods.
Self-perceived competence and the relation between life events and depressive symptoms in adolescence: mediator or moderator?

In a 2-wave longitudinal study, 9th-grade students (N = 468) completed self-report measures of stressful life events, depressive symptoms, and 5 domains of self-perceived competence (i.e., academic competence, social acceptance, physical appearance, behavioral conduct, and athletic competence). Teachers and peers also reported about the participants' depressive symptoms. In longitudinal analyses, negative (but not positive) events related to depressive symptoms. Results suggested that self-perceived competence served as a mediator (but not a moderator) of this relation. Negative events predicted changes in self-perceived competence. Self-perceived competence predicted changes in depressive symptoms. Also, the direct effect of negative events on depressive symptoms diminished after controlling for self-perceived competence.
"It's Not a Replacement:"Enabling Parent-Robot Collaboration to Support In-Home Learning Experiences of Young Children

Learning companion robots for young children are increasingly adopted in informal learning environments. Although parents play a pivotal role in their children's learning, very little is known about how parents prefer to incorporate robots into their children's learning activities. We developed prototype capabilities for a learning companion robot to deliver educational prompts and responses to parent-child pairs during reading sessions and conducted in-home user studies involving 10 families with children aged 3-5. Our data indicates that parents want to work with robots as collaborators to augment parental activities to foster children's learning, introducing the notion of parent-robot collaboration. Our findings offer an empirical understanding of the needs and challenges of parent-child interaction in informal learning scenarios and design opportunities for integrating a companion robot into these interactions. We offer insights into how robots might be designed to facilitate parent-robot collaboration, including parenting policies, collaboration patterns, and interaction paradigms.
Effects of Zaprinast and Rolipram on Olfactory and Visual Memory in the Social Transmission of Food Preference and Novel Object Recognition Tests in Mice

The role of phosphodiesterase (PDE) inhibitors in central nervous system has been investigated and shown to stimulate neuronal functions and increase neurogenesis in Alzheimer patients. The aim of this study is to investigate effect of PDE5 inhibitor zaprinast and PDE4 inhibitor rolipram on visual memory in novel object recognition (NOR) test, on olfactory memory in social transmission of food preference (STFP) test, and also on locomotion and anxiety in open field test in naive mice. Male Balb-c mice were treated intraperitoneally (i.p.) with zaprinast (3 and 10 mg/kg), rolipram (0.05 and 0.1 mg/kg), or physiological saline. Zaprinast (10 mg/kg) significantly increased cued/non-cued food eaten compared to control group, while rolipram had a partial effect on retention trial of STFP test. Zaprinast (10 mg/kg) and rolipram (0.05 and 0.1 mg/kg) significantly increased ratio index (RI) compared to control group in retention trial of NOR test. There was no significant effect of zaprinast and rolipram on total distance moved, speed, and center zone duration in open field test. Results of this study revealed that both zaprinast and rolipram enhanced visual memory in NOR test, however zaprinast exerted a significant memory-enhancing effect compared to rolipram in STFP test in mice.
Infectious but nonpathogenic isolate of Borrelia burgdorferi

We document for the first time an infectious but nonarthritogenic variant of Borrelia burgdorferi. Strain 25015, previously isolated from an Ixodes dammini larva collected in upstate New York, was infectious but failed to produce arthritis or carditis in laboratory rats and mice. By contrast, pathogenic strain N40 invariably caused arthritis. This nonarthritogenic variant, with proteins with molecular weights different from those of the standard B31 strain, was frequently isolated from normal joint tissues of experimentally infected rats. Outer surface proteins A and B of strain 25015 have molecular weights of about 32,500 and 35,500, respectively, in contrast to molecular weights of approximately 31,000 and 34,000, respectively, for outer surface proteins A and B of strains B31 and N40. A prominent low-molecular-weight protein of about 23,500 also characterizes strain 25015. Test animals infected for 30 to 60 days had relatively high antibody titers (greater than or equal to 1:1,280). The nonarthritogenic variant will be useful, along with pathogenic strains, in providing comparative insight into the pathogenesis of Lyme borreliosis. Homologous immunoblotting of sera from rats and mice inoculated with both the arthritogenic and nonarthritogenic strains revealed antibody reactivities to proteins of B. burgdorferi different from those revealed in the heterologous tests.