The contribution of geophysical and spectral imaging techniques in the archaeological investigations of Minoan Koumasa

A manifold geophysical strategy has been carried out for mapping the Minoan cemetery and settlement of Koumasa, S. Crete. Multispectral imaging was used as a complementary method to investigate associations with the geophysical results. The GNVI proved the most effective in this task. Archaeological excavations have verified a number of features suggested by the remote sensing methods

Optimising time series forecasts through linear programming

This study explores the usage of linear programming (LP) as a tool to optimise the parameters of time series forecasting models. LP is the most well-known tool in the field of operational research and it has been used for a wide range of optimisation problems. Nonetheless, there are very few applications in forecasting and all of them are limited to causal modelling. The rationale behind this study is that time series forecasting problems can be treated as optimisation problems, where the objective is to minimise the forecasting error. The research topic is very interesting from a theoretical and mathematical prospective. LP is a very strong tool but simple to use; hence, an LP-based approach will give to forecasters the opportunity to do accurate forecasts quickly and easily. In addition, the flexibility of LP can help analysts to deal with situations that other methods cannot deal with. The study consists of five parts where the parameters of forecasting models are estimated by using LP to minimise one or more accuracy (error) indices (sum of absolute deviations – SAD, sum of absolute percentage errors – SAPE, maximum absolute deviation – MaxAD, absolute differences between deviations – ADBD and absolute differences between percentage deviations – ADBPD). In order to test the accuracy of the approaches two samples of series from the M3 competition are used and the results are compared with traditional techniques that are found in the literature. In the first part simple LP is used to estimate the parameters of autoregressive based forecasting models by minimising one error index and they are compared with the method of the ordinary least squares (OLS minimises the sum of squared errors, SSE). The experiments show that the decision maker has to choose the best optimisation objective according to the characteristic of the series. In the second part, goal programming (GP) formulations are applied to similar models by minimising a combination of two accuracy indices. The experiments show that goal programming improves the performance of the single objective approaches. In the third part, several constraints to the initial simple LP and GP formulations are added to improve their performance on series with high randomness and their accuracy is compared with techniques that perform well on these series. The additional constraints improve the results and outperform all the other techniques. In the fourth part, simple LP and GP are used to combine forecasts. Eight simple individual techniques are combined and LP is compared with five traditional combination methods. The LP combinations outperform the other methods according to several performance indices. Finally, LP is used to estimate the parameters of autoregressive based models with optimisation objectives to minimise forecasting cost and it is compared them with the OLS. The experiments show that LP approaches perform better in terms of cost. The research shows that LP is a very useful tool that can be used to make accurate time series forecasts, which can outperform the traditional approaches that are found in forecasting literature and in practise

VISION DIFFMASK: Faithful Interpretation of Vision Transformers with Differentiable Patch Masking

The lack of interpretability of the Vision Transformer may hinder its use in critical real-world applications despite its effectiveness. To overcome this issue, we propose a post-hoc interpretability method called VISION DIFFMASK, which uses the activations of the model's hidden layers to predict the relevant parts of the input that contribute to its final predictions. Our approach uses a gating mechanism to identify the minimal subset of the original input that preserves the predicted distribution over classes. We demonstrate the faithfulness of our method, by introducing a faithfulness task, and comparing it to other state-of-the-art attribution methods on CIFAR-10 and ImageNet-1K, achieving compelling results. To aid reproducibility and further extension of our work, we open source our implementation: https://github.com/AngelosNal/Vision-DiffMaskComment: Accepted in the XAI4CV Workshop at CVPR 202

Probing LLMs for Joint Encoding of Linguistic Categories

Large Language Models (LLMs) exhibit impressive performance on a range of NLP tasks, due to the general-purpose linguistic knowledge acquired during pretraining. Existing model interpretability research (Tenney et al., 2019) suggests that a linguistic hierarchy emerges in the LLM layers, with lower layers better suited to solving syntactic tasks and higher layers employed for semantic processing. Yet, little is known about how encodings of different linguistic phenomena interact within the models and to what extent processing of linguistically-related categories relies on the same, shared model representations. In this paper, we propose a framework for testing the joint encoding of linguistic categories in LLMs. Focusing on syntax, we find evidence of joint encoding both at the same (related part-of-speech (POS) classes) and different (POS classes and related syntactic dependency relations) levels of linguistic hierarchy. Our cross-lingual experiments show that the same patterns hold across languages in multilingual LLMs.Comment: Accepted in EMNLP Findings 202

On-chip integrated graphene aptasensor with portable readout for fast and label-free COVID-19 detection in virus transport medium

Graphene field-effect transistor (GFET) biosensors exhibit high sensitivity due to a large surface-to-volume ratio and the high sensitivity of the Fermi level to the presence of charged biomolecules near the surface. For most reported GFET biosensors, bulky external reference electrodes are used which prevent their full-scale chip integration and contribute to higher costs per test. In this study, GFET arrays with on-chip integrated liquid electrodes were employed for COVID-19 detection and functionalized with either antibody or aptamer to selectively bind the spike proteins of SARS-CoV-2. In the case of the aptamer-functionalized GFET (aptasensor, Apt-GFET), the limit-of-detection (LOD) achieved was about 103 particles per mL for virus-like particles (VLPs) in clinical transport medium, outperforming the Ab-GFET biosensor counterpart. In addition, the aptasensor achieved a LOD of 160 aM for COVID-19 neutralizing antibodies in serum. The sensors were found to be highly selective, fast (sample-to-result within minutes), and stable (low device-to-device signal variation; relative standard deviations below 0.5%). A home-built portable readout electronic unit was employed for simultaneous real-time measurements of 12 GFETs per chip. Our successful demonstration of a portable GFET biosensing platform has high potential for infectious disease detection and other health-care applications

Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution

Immune evasion is a hallmark of cancer. Losing the ability to present neoantigens through human leukocyte antigen (HLA) loss may facilitate immune evasion. However, the polymorphic nature of the locus has precluded accurate HLA copy-number analysis. Here, we present loss of heterozygosity in human leukocyte antigen (LOHHLA), a computational tool to determine HLA allele-specific copy number from sequencing data. Using LOHHLA, we find that HLA LOH occurs in 40% of non-small-cell lung cancers (NSCLCs) and is associated with a high subclonal neoantigen burden, APOBEC-mediated mutagenesis, upregulation of cytolytic activity, and PD-L1 positivity. The focal nature of HLA LOH alterations, their subclonal frequencies, enrichment in metastatic sites, and occurrence as parallel events suggests that HLA LOH is an immune escape mechanism that is subject to strong microenvironmental selection pressures later in tumor evolution. Characterizing HLA LOH with LOHHLA refines neoantigen prediction and may have implications for our understanding of resistance mechanisms and immunotherapeutic approaches targeting neoantigens. Video Abstract [Figure presented] Development of the bioinformatics tool LOHHLA allows precise measurement of allele-specific HLA copy number, improves the accuracy in neoantigen prediction, and uncovers insights into how immune escape contributes to tumor evolution in non-small-cell lung cancer

Fc-Optimized Anti-CD25 Depletes Tumor-Infiltrating Regulatory T Cells and Synergizes with PD-1 Blockade to Eradicate Established Tumors

CD25 is expressed at high levels on regulatory T (Treg) cells and was initially proposed as a target for cancer immunotherapy. However, anti-CD25 antibodies have displayed limited activity against established tumors. We demonstrated that CD25 expression is largely restricted to tumor-infiltrating Treg cells in mice and humans. While existing anti-CD25 antibodies were observed to deplete Treg cells in the periphery, upregulation of the inhibitory Fc gamma receptor (FcγR) IIb at the tumor site prevented intra-tumoral Treg cell depletion, which may underlie the lack of anti-tumor activity previously observed in pre-clinical models. Use of an anti-CD25 antibody with enhanced binding to activating FcγRs led to effective depletion of tumor-infiltrating Treg cells, increased effector to Treg cell ratios, and improved control of established tumors. Combination with anti-programmed cell death protein-1 antibodies promoted complete tumor rejection, demonstrating the relevance of CD25 as a therapeutic target and promising substrate for future combination approaches in immune-oncology

Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution.

The early detection of relapse following primary surgery for non-small-cell lung cancer and the characterization of emerging subclones, which seed metastatic sites, might offer new therapeutic approaches for limiting tumour recurrence. The ability to track the evolutionary dynamics of early-stage lung cancer non-invasively in circulating tumour DNA (ctDNA) has not yet been demonstrated. Here we use a tumour-specific phylogenetic approach to profile the ctDNA of the first 100 TRACERx (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy (Rx)) study participants, including one patient who was also recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release and analyse the tumour-volume detection limit. Through blinded profiling of postoperative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients who are very likely to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastasis, providing a new approach for ctDNA-driven therapeutic studies