Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobile device use and the cognitive function and depressive symptoms of older adults living in residential care homes

ion 0.304 0.104 0.004 0.100, 0.508 Delayed Recall 1.475 0.224 <0.001 1.034, 1.951 Orientation 0.603 0.177 0.001 0.254, 0.953 Total MOCA Score 4.454 0.685 <0.001 3.104, 5.804 *MoCA: Montreal Cognitive Assessment. Table 4. Logistic Regression Analysis of Mobile Device Use and Depressive Symptoms Covariates b SE Waldc2 P OR 95%CI
Dietary Inflammatory Index (DII) and risk of colorectal cancer in Iowa Women’s Health Study

: Background: Colorectal cancer (CRC), the third most common cancer in the United States, has a natural history that usually encompasses several decades. Dietary components have been implicated in the etiology of CRC, perhaps through their effect on inflammation. Methods: We examined the ability of the dietary inflammatory index (DII) to predict CRC in the Iowa Women’s Health Study. The DII was computed based on dietary intake assessed by a 121-item food frequency questionnaire in this cohort of 34,703 women, aged 55–69 years, free of any self-reported prior malignancy at enrollment in 1986. Incident CRC cases were identified through linkage with the State Health Registry of Iowa (a Surveillance, Epidemiology and End Results program member). Cox proportional hazards regression was used to estimate hazard ratios (HR). Through the end of 2010, 1636 incident CRCs were identified, including 1329 colon and 325 rectal cancers. Results: Multivariable analysis, adjusting for BMI, smoking status, pack-years of smoking, hormone replacement therapy, education, diabetes and total energy intake, revealed positive associations between higher DII and CRC risk (HR for DII continuous : 1.07 per unit increase in DII (corresponding to 0.5 standard deviation unit increase); 95%CI 1.01- 1.13; HR for DII quintiles : Q5vsQ1=1.20; 95%CI 1.01- 1.43). HRs for DII were similar for colon cancer and rectal cancer, though not statistically significant for rectal cancer. Conclusions: These results indicate that a pro-inflammatory diet, as indicated by higher DII scores, was associated with higher risk of developing CRC. Impact : Pro-inflammatory diets are associated with increased risk of CRC.
Addenbrooke's Cognitive Examination-III.

The abolition of fixed retirement means that many people will work into their late 60s or even their early 70s. Older health care workers tend to suffer from the four Ds—drink, drugs, depression and dementia [1]. Studies show negative correlations between performance on cognitive testing and job performance problems [2] or with age [3]. One study found cognitive impairment in doctors was responsible for 63% of all adverse medical events with most being preventable [4]. Detection of cognitive impairment is essential in determining any risk to patient safety and to safeguard patients by designing and implementing effective remediation programmes. Dementia UK [5] estimates that 1.3% of people aged 65–69 years have dementia rising to 2.9% for those aged 70–74 years. Lesser degrees of cognitive impairment can be more common in people at these ages, increasing demand on occupational physicians to assess older workers for possible cognitive impairment(s). Mild cognitive impairment converts to dementia at a rate of ~10% per year [6], a clinical challenge due to the variety and often dynamic nature of symptoms. There is need to determine whether cognitive impairment is present but also to establish which cognitive domains are affected in relation to skills and knowledge required by the person’s occupation [7]. Screening must be effective, cost beneficial with suitable methods and therapeutic evidence. Patients may perform poorly on formal cognitive tests for other reasons, including acute illness, pain, lethargy, sleep deprivation, medication, depression, anxiety, not wishing to engage with testing, language barriers, cultural issues and learning disability. Hence these tests form part of the overall clinical assessment and clarity is needed as to the level of detail required for screening, differential diagnosis or detailed neuropsychological analysis.
Cell‐type‐specific visualisation and biochemical isolation of endogenous synaptic proteins in mice

In recent years, the remarkable molecular complexity of synapses has been revealed, with over 1,000 proteins identified in the synapse proteome. Although it is known that different receptors and other synaptic proteins are present in different types of neurons, the extent of synapse diversity across the brain is largely unknown. This is mainly due to the limitations of current techniques. Here, we report an efficient method for the purification of synaptic protein complexes, fusing a high‐affinity tag to endogenous PSD95 in specific cell types. We also developed a strategy, which enables the visualisation of endogenous PSD95 with fluorescent‐protein tag in Cre‐recombinase‐expressing cells. We demonstrate the feasibility of proteomic analysis of synaptic protein complexes and visualisation of these in specific cell types. We find that the composition of PSD95 complexes purified from specific cell types differs from those extracted from tissues with diverse cellular composition. The results suggest that there might be differential interactions in the PSD95 complexes in different brain regions. We have detected differentially interacting proteins by comparing data sets from the whole hippocampus and the CA3 subfield of the hippocampus. Therefore, these novel conditional PSD95 tagging lines will not only serve as powerful tools for precisely dissecting synapse diversity in specific brain regions and subsets of neuronal cells, but also provide an opportunity to better understand brain region‐ and cell‐type‐specific alterations associated with various psychiatric/neurological diseases. These newly developed conditional gene tagging methods can be applied to many different synaptic proteins and will facilitate research on the molecular complexity of synapses.
Supplementary Information for The Evolution of Homophily

1 Department of Health Care Policy, Harvard Medical School, Boston, MA 02115, USA 2 Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138, USA 3 Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA 4 Department of Mathematics, Harvard University, Cambridge, MA 02138, USA 5 Department of Medicine, Harvard Medical School, Boston, MA 02115, USA 6 Department of Sociology, Faculty of Arts and Sciences, Harvard University, Cambridge, MA 02138,
Assessing Cyclostationary Malware Detection via Feature Selection and Classification

Cyclostationarity involves periodic statistical variations in signals and processes, commonly used in signal analysis and network security. In the context of attacks, cyclostationarity helps detect malicious behaviors within network traffic, such as traffic patterns in Distributed Denial of Service (DDoS) attacks or hidden communication channels in malware. This approach enhances security by identifying abnormal patterns and informing Network Intrusion Detection Systems (NIDSs) to recognize potential attacks, enhancing protection against both known and novel threats. This research focuses on identifying cyclostationary malware behavior and its detection. The main goal is to pinpoint essential cyclostationary features used in NIDSs. These features are extracted using algorithms such as Boruta and Principal Component Analysis (PCA), and then categorized to find the most significant cyclostationary patterns. The aim of this article is to reveal periodically changing malware behaviors through cyclostationarity. The study highlights the importance of spotting cyclostationary malware in NIDSs by using established datasets like KDD99, NSL-KDD, and the UGRansome dataset. The UGRansome dataset is designed for anomaly detection research and includes both normal and abnormal network threat categories of zero-day attacks. A comparison is made using the Random Forest (RF) and Support Vector Machine (SVM) algorithms, while also evaluating the effectiveness of Boruta and PCA. The findings show that PCA is more promising than using Boruta alone for extracting cyclostationary network feature patterns. Additionally, the analysis identifies the internet protocol as the most noticeable cyclostationary feature pattern used by malware. Notably, the UGRansome dataset outperforms the KDD99 and NSL-KDD, achieving 99% accuracy in signature malware detection using the RF algorithm and 98% with the SVM.
Policy composition in reinforcement learning via multi-objective policy optimization

We enable reinforcement learning agents to learn successful behavior policies by utilizing relevant pre-existing teacher policies. The teacher policies are introduced as objectives, in addition to the task objective, in a multi-objective policy optimization setting. Using the Multi-Objective Maximum a Posteriori Policy Optimization algorithm (Abdolmaleki et al. 2020), we show that teacher policies can help speed up learning, particularly in the absence of shaping rewards. In two domains with continuous observation and action spaces, our agents successfully compose teacher policies in sequence and in parallel, and are also able to further extend the policies of the teachers in order to solve the task. Depending on the specified combination of task and teacher(s), teacher(s) may naturally act to limit the final performance of an agent. The extent to which agents are required to adhere to teacher policies are determined by hyperparameters which determine both the effect of teachers on learning speed and the eventual performance of the agent on the task. In the humanoid domain (Tassa et al. 2018), we also equip agents with the ability to control the selection of teachers. With this ability, agents are able to meaningfully compose from the teacher policies to achieve a superior task reward on the walk task than in cases without access to the teacher policies. We show the resemblance of composed task policies with the corresponding teacher policies through videos.
Benchmarking the Generation of Fact Checking Explanations

Abstract Fighting misinformation is a challenging, yet crucial, task. Despite the growing number of experts being involved in manual fact-checking, this activity is time-consuming and cannot keep up with the ever-increasing amount of fake news produced daily. Hence, automating this process is necessary to help curb misinformation. Thus far, researchers have mainly focused on claim veracity classification. In this paper, instead, we address the generation of justifications (textual explanation of why a claim is classified as either true or false) and benchmark it with novel datasets and advanced baselines. In particular, we focus on summarization approaches over unstructured knowledge (i.e., news articles) and we experiment with several extractive and abstractive strategies. We employed two datasets with different styles and structures, in order to assess the generalizability of our findings. Results show that in justification production summarization benefits from the claim information, and, in particular, that a claim-driven extractive step improves abstractive summarization performances. Finally, we show that although cross-dataset experiments suffer from performance degradation, a unique model trained on a combination of the two datasets is able to retain style information in an efficient manner.
Best Memory Architecture Exploration under Parameters Variations accelerated with Machine Learning

The design of effective memory architecture is of utmost importance in modern computing systems. However, the design of memory subsystems is even more difficult today because process variation and modern design techniques like dynamic voltage scaling make performance metrics for memory assessment be treated as random variables instead of scalars at design time. Most of the previous works have studied the design of memory design from the yield analysis perspective leaving the question of the best memory organization on average open. Because examining all possible combinations of design parameter values of a memory chip would require prohibitively much time, in this work, we propose Best Arm Identification (BAI) algorithms to accelerate the exploration for the best memory architecture on average under parameter variations. Our experimental results demonstrate that we can arrive at the best memory organization 99% of the time in x5 faster than an exhaustive search of all possible conditions.
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM

This paper explores the challenges of implementing Federated Learning (FL) in practical scenarios featuring isolated nodes with data heterogeneity, which can only be connected to the server through wireless links in an infrastructure-less environment. To overcome these challenges, we propose a novel mobilizing personalized FL approach, which aims to facilitate mobility and resilience. Specifically, we develop a novel optimization algorithm called Random Walk Stochastic Alternating Direction Method of Multipliers (RWSADMM). RWSADMM capitalizes on the server's random movement toward clients and formulates local proximity among their adjacent clients based on hard inequality constraints rather than requiring consensus updates or introducing bias via regularization methods. To mitigate the computational burden on the clients, an efficient stochastic solver of the approximated optimization problem is designed in RWSADMM, which provably converges to the stationary point almost surely in expectation. Our theoretical and empirical results demonstrate the provable fast convergence and substantial accuracy improvements achieved by RWSADMM compared to baseline methods, along with its benefits of reduced communication costs and enhanced scalability.
Spatial Chromatin Architecture Alteration by Structural Variations in Human Genomes at Population Scale

This genome-wide study is focused on the impact of structural variants identified in individuals from 26 human populations onto three-dimensional structures of their genomes. We assess the tendency of structural variants to accumulate in spatially interacting genomic segments and design a high-resolution computational algorithm to model the 3D conformational changes resulted by structural variations. We show that differential gene transcription is closely linked to variation in chromatin interaction networks mediated by RNA polymerase II. We also demonstrate that CTCF-mediated interactions are well conserved across population, but enriched with disease-associated SNPs. Altogether, this study assesses the critical impact of structural variants on the higher order organization of chromatin folding and provides unique insight into the mechanisms regulating gene transcription at the population scale, among which the local arrangement of chromatin loops seems to be the leading one. It is the first insight into the variability of the human 3D genome at the population scale.
Contact-Rich SE(3)-Equivariant Robot Manipulation Task Learning via Geometric Impedance Control

This letter presents a differential geometric control approach that leverages SE(3) group invariance and equivariance to increase transferability in learning robot manipulation tasks that involve interaction with the environment. Specifically, we employ a control law and a learning representation framework that remain invariant under arbitrary SE(3) transformations of the manipulation task definition. Furthermore, the control law and learning representation framework are shown to be SE(3) equivariant when represented relative to the spatial frame. The proposed approach is based on utilizing a recently presented geometric impedance control (GIC) combined with a learning variable impedance control framework, where the gain scheduling policy is trained in a supervised learning fashion from expert demonstrations. A geometrically consistent error vector (GCEV) is fed to a neural network to achieve a gain scheduling policy that remains invariant to arbitrary translation and rotations. A comparison of our proposed control and learning framework with a well-known Cartesian space learning impedance control, equipped with a Cartesian error vector-based gain scheduling policy, confirms the significantly superior learning transferability of our proposed approach. A hardware implementation on a peg-in-hole task is conducted to validate the learning transferability and feasibility of the proposed approach.
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition

Deep Neural Networks (DNNs) have been a large driver and enabler for AI breakthroughs in recent years. These models have been getting larger in their attempt to become more accurate and tackle new upcoming use-cases, including AR/VR and intelligent assistants. However, the training process of such large models is a costly and time-consuming process, which typically yields a single model to fit all targets. To mitigate this, various techniques have been proposed in the literature, including pruning, sparsification or quantization of the model weights and updates. While able to achieve high compression rates, they often incur computational overheads or accuracy penalties. Alternatively, factorization methods have been leveraged to incorporate low-rank compression in the training process. Similarly, such techniques (e.g.,~SVD) frequently rely on the computationally expensive decomposition of layers and are potentially sub-optimal for non-linear models, such as DNNs. In this work, we take a further step in designing efficient low-rank models and propose Maestro, a framework for trainable low-rank layers. Instead of regularly applying a priori decompositions such as SVD, the low-rank structure is built into the training process through a generalized variant of Ordered Dropout. This method imposes an importance ordering via sampling on the decomposed DNN structure. Our theoretical analysis demonstrates that our method recovers the SVD decomposition of linear mapping on uniformly distributed data and PCA for linear autoencoders. We further apply our technique on DNNs and empirically illustrate that Maestro enables the extraction of lower footprint models that preserve model performance while allowing for graceful accuracy-latency tradeoff for the deployment to devices of different capabilities.
Adjusting inverse regression for predictors with clustered distribution

A major family of sufficient dimension reduction (SDR) methods, called inverse regression, commonly require the distribution of the predictor $X$ to have a linear $E(X|\beta^\mathsf{T}X)$ and a degenerate $\mathrm{var}(X|\beta^\mathsf{T}X)$ for the desired reduced predictor $\beta^\mathsf{T}X$. In this paper, we adjust the first and second-order inverse regression methods by modeling $E(X|\beta^\mathsf{T}X)$ and $\mathrm{var}(X|\beta^\mathsf{T}X)$ under the mixture model assumption on $X$, which allows these terms to convey more complex patterns and is most suitable when $X$ has a clustered sample distribution. The proposed SDR methods build a natural path between inverse regression and the localized SDR methods, and in particular inherit the advantages of both; that is, they are $\sqrt{n}$-consistent, efficiently implementable, directly adjustable under the high-dimensional settings, and fully recovering the desired reduced predictor. These findings are illustrated by simulation studies and a real data example at the end, which also suggest the effectiveness of the proposed methods for nonclustered data.
Scaling Limits of Stochastic Transport Equations on Manifolds

In this work, we generalize some results on scaling limits of stochastic transport equations on torus, developed recently by Flandoli, Galeati and Luo, to manifolds. We consider the stochastic transport equations driven by colored space-time noise(smooth in space, white in time) on a compact Riemannian manifold without boundary. By tuning the noises properly, we obtain different scaling limits depending on the initial data. With space white noise as initial data, the solutions converge in distribution to the solution of a stochastic heat equation with additive noise. With regular initial data, the solutions of transport equation converge to the solution of the deterministic heat equation, accompanied by qualitative estimates on the convergence rate. We can get the same rate as on torus in the estimates, but at the cost of either imposing stronger bounds on the initial data or sacrificing some regularity.
XVir: A Transformer-Based Architecture for Identifying Viral Reads from Cancer Samples

It is estimated that approximately 15% of cancers world-wide can be linked to viral infections. The viruses that can cause or increase the risk of cancer include human papillomavirus, hepatitis B and C viruses, Epstein-Barr virus, and human immunodeficiency virus, to name a few. The computational analysis of the massive amounts of tumor DNA data, whose collection is enabled by the recent advancements in sequencing technologies, have allowed studies of the potential association between cancers and viral pathogens. However, the high diversity of oncoviral families makes reliable detection of viral DNA difficult and thus, renders such analysis challenging. In this paper, we introduce XVir, a data pipeline that relies on a transformer-based deep learning architecture to reliably identify viral DNA present in human tumors. In particular, XVir is trained on genomic sequencing reads from viral and human genomes and may be used with tumor sequence information to find evidence of viral DNA in human cancers. Results on semi-experimental data demonstrate that XVir is capable of achieving high detection accuracy, generally outperforming state-of-the-art competing methods while being more compact and less computationally demanding. CCS CONCEPTS • Computer systems organization → Embedded systems; Redundancy; Robotics; • Networks → Network reliability. ACM Reference Format Shorya Consul, John Robertson, and Haris Vikalo. 2023. XVir: A Transformer-Based Architecture for Identifying Viral Reads from Cancer Samples. In Proceedings of The Eighth International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC ’23). ACM, New York, NY, USA, 8 pages.
Best performance and reliability for your time: budget-aware search-based optimization of software model refactoring

Context: Software model optimization is a process that automatically generates design alternatives, typically to enhance quantifiable non-functional properties of software systems, such as performance and reliability. Multi-objective evolutionary algorithms have shown to be effective in this context for assisting the designer in identifying trade-offs between the desired non-functional properties. Objective: In this work, we investigate the effects of imposing a time budget to limit the search for design alternatives, which inevitably affects the quality of the resulting alternatives. Method: The effects of time budgets are analyzed by investigating both the quality of the generated design alternatives and their structural features when varying the budget and the genetic algorithm (NSGA-II, PESA2, SPEA2). This is achieved by employing multi-objective quality indicators and a tree-based representation of the search space. Results: The study reveals that the time budget significantly affects the quality of Pareto fronts, especially for performance and reliability. NSGA-II is the fastest algorithm, while PESA2 generates the highest-quality solutions. The imposition of a time budget results in structurally distinct models compared to those obtained without a budget, indicating that the search process is influenced by both the budget and algorithm selection. Conclusions: In software model optimization, imposing a time budget can be effective in saving optimization time, but designers should carefully consider the trade-off between time and solution quality in the Pareto front, along with the structural characteristics of the generated models. By making informed choices about the specific genetic algorithm, designers can achieve different trade-offs.