Convex learning of multiple tasks and their structure

Reducing the amount of human supervision is a key problem in machine learning and a natural approach is that of exploiting the relations (structure) among different tasks. This is the idea at the core of multi-task learning. In this context a fundamental question is how to incorporate the tasks structure in the learning problem. We tackle this question by studying a general computational framework that allows to encode a-priori knowledge of the tasks structure in the form of a convex penalty; in this setting a variety of previously proposed methods can be recovered as special cases, including linear and non-linear approaches. Within this framework, we show that tasks and their structure can be efficiently learned considering a convex optimization problem that can be approached by means of block coordinate methods such as alternating minimization and for which we prove convergence to the global minimum

Learning with group invariant features: A Kernel perspective

We analyze in this paper a random feature map based on a theory of invariance (I-theory) introduced in [1]. More specifically, a group invariant signal signature is obtained through cumulative distributions of group-transformed random projections. Our analysis bridges invariant feature learning with kernel methods, as we show that this feature map defines an expected Haar-integration kernel that is invariant to the specified group action. We show how this non-linear random feature map approximates this group invariant kernel uniformly on a set of N points. Moreover, we show that it defines a function space that is dense in the equivalent Invariant Reproducing Kernel Hilbert Space. Finally, we quantify error rates of the convergence of the empirical risk minimization, as well as the reduction in the sample complexity of a learning algorithm using such an invariant representation for signal classification, in a classical supervised learning setting

Non-curative treatment of patients with oral tongue squamous-cell carcinoma

PurposeLate-stage OTSCC is associated with poor overall survival (OS). Non-curative treatment approach aims to improve quality of life and prolong survival of patients deemed incurable. The purpose of this study was to investigate the used non-curative treatment modalities for OTSSC and patient survival.MethodsAll patients diagnosed with OTSCC and treated with non-curative intent at the HUS Helsinki University Hospital (Helsinki, Finland) during the 12-year period of 2005-2016 were included. Survival analysis after the non-curative treatment decision was conducted using the Kaplan-Meier method in this population-based study.ResultsEighty-two patients were identified. A non-curative treatment decision was made at presentation without any previous treatment in 26 patients (7% of all patients diagnosed with OTSCC during the study period). Palliative radiotherapy was administered to 24% of all patients. The average survival time after the non-curative treatment decision was 3.7months (median 2 and range 0-26).ConclusionsDue to the short mean survival time after decision for treatment with non-curative intent, and the notable symptom burden in this patient population, a prompt initiation of all non-curative measures is warranted.Peer reviewe

Evaluation of the budding and depth of invasion (BD) model in oral tongue cancer biopsies

It is of great clinical importance to identify simple prognostic markers from preoperative biopsies that could guide treatment planning. Here, we compared tumor budding (B), depth of invasion (D), and the combined scores (i.e., budding and depth of invasion (BD) histopathologic model) in preoperative biopsies and the corresponding postoperative specimens of oral tongue squamous cell carcinoma (OTSCC). Tumor budding and depth of invasion were evaluated in the pre- and postoperative samples from 100 patients treated for OTSCC. Sensitivity and specificity statistics were used. Our results showed statistically significant (P <0.001) relationship between pre- and postoperative BD scores. There was an agreement between the pre- and postoperative BD model scores in 83 cases (83%) with 57.1% sensitivity (95% CI 39.4 to 73.7%) and 96.9% specificity (95% CI 89.3 to 99.6%). Our findings suggest that the BD model, analyzed from representative biopsies, could be used for the treatment planning of OTSCC.Peer reviewe

Auditing and Generating Synthetic Data with Controllable Trust Trade-offs

Data collected from the real world tends to be biased, unbalanced, and at risk of exposing sensitive and private information. This reality has given rise to the idea of creating synthetic datasets to alleviate risk, bias, harm, and privacy concerns inherent in the real data. This concept relies on Generative AI models to produce unbiased, privacy-preserving synthetic data while being true to the real data. In this new paradigm, how can we tell if this approach delivers on its promises? We present an auditing framework that offers a holistic assessment of synthetic datasets and AI models trained on them, centered around bias and discrimination prevention, fidelity to the real data, utility, robustness, and privacy preservation. We showcase our framework by auditing multiple generative models on diverse use cases, including education, healthcare, banking, human resources, and across different modalities, from tabular, to time-series, to natural language. Our use cases demonstrate the importance of a holistic assessment in order to ensure compliance with socio-technical safeguards that regulators and policymakers are increasingly enforcing. For this purpose, we introduce the trust index that ranks multiple synthetic datasets based on their prescribed safeguards and their desired trade-offs. Moreover, we devise a trust-index-driven model selection and cross-validation procedure via auditing in the training loop that we showcase on a class of transformer models that we dub TrustFormers, across different modalities. This trust-driven model selection allows for controllable trust trade-offs in the resulting synthetic data. We instrument our auditing framework with workflows that connect different stakeholders from model development to audit and certification via a synthetic data auditing report.Comment: 49 pages; submitte

Non-curative treatment of patients with oral tongue squamous-cell carcinoma

PurposeLate-stage OTSCC is associated with poor overall survival (OS). Non-curative treatment approach aims to improve quality of life and prolong survival of patients deemed incurable. The purpose of this study was to investigate the used non-curative treatment modalities for OTSSC and patient survival.MethodsAll patients diagnosed with OTSCC and treated with non-curative intent at the HUS Helsinki University Hospital (Helsinki, Finland) during the 12-year period of 2005–2016 were included. Survival analysis after the non-curative treatment decision was conducted using the Kaplan–Meier method in this population-based study.ResultsEighty-two patients were identified. A non-curative treatment decision was made at presentation without any previous treatment in 26 patients (7% of all patients diagnosed with OTSCC during the study period). Palliative radiotherapy was administered to 24% of all patients. The average survival time after the non-curative treatment decision was 3.7 months (median 2 and range 0–26).ConclusionsDue to the short mean survival time after decision for treatment with non-curative intent, and the notable symptom burden in this patient population, a prompt initiation of all non-curative measures is warranted.</div

Unmasking the interplay between mTOR and Nox4: novel insights into the mechanism connecting diabetes and cancer

Cancer was recently annexed to diabetic complications. Furthermore, recent studies suggest that cancer can increase the risk of diabetes. Consequently, diabetes and cancer share many risk factors, but the cellular and molecular pathways correlating diabetes and colon and rectal cancer (CRC) remain far from understood. In this study, we assess the effect of hyperglycemia on cancer cell aggressiveness in human colon epithelial adenocarcinoma cells in vitro and in an experimental animal model of CRC. Our results show that Nox (NADPH oxidase enzyme) 4-induced reactive oxygen species (ROS) production is deregulated in both diabetes and CRC. This is paralleled by inactivation of the AMPK and activation of the mammalian target of rapamycin (mTOR) C1 signaling pathways, resulting in 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) accumulation, induction of DNA damage, and exacerbation of cancer cell aggressiveness, thus contributing to the genomic instability and predisposition to increased tumorigenesis in the diabetic milieu. Pharmacologic activation of AMPK, inhibition of mTORC1, or blockade of Nox4 reduce ROS production, restore the homeostatic signaling of 8-oxoguanine DNA glycosylase/8-oxodG, and lessen the progression of CRC malignancy in a diabetic milieu. Taken together, our results identify the AMPK/mTORC1/Nox4 signaling axis as a molecular switch correlating diabetes and CRC. Modulating this pathway may be a strategic target of therapeutic potential aimed at reversing or slowing the progression of CRC in patients with or without diabetes.-Mroueh, F. M., Noureldein, M., Zeidan, Y. H., Boutary, S., Irani, S. A. M., Eid, S., Haddad, M., Barakat, R., Harb, F., Costantine, J., Kanj, R., Sauleau, E.-A., Ouhtit, A., Azar, S. T., Eid, A. H., Eid, A. A. Unmasking the interplay between mTOR and Nox4: novel insights into the mechanism connecting diabetes and cancer.Scopu

Evaluation of medication adherence among Lebanese diabetic patients

Background: Diabetes type 2 is considered one of the main public health concerns. Lack of adherence to treatment leads to poor therapeutic outcome, poor glycemic control, and high risk for developing diabetes complications. Objectives: The aim of this study is to evaluate adherence to oral antidiabetic medication in Diabetes type 2 Lebanese patients, and to evaluate factors leading to low adherence. Methods: A cross-sectional study was conducted in outpatients endocrinology clinics of two hospitals and four private clinics located in Beirut-Lebanon. Data was collected using a well-structured questionnaire by trained pharmacists. Adherence level was measured by the Lebanese Medication Adherence Scale (LMAS-14). Bivariate and multivariate analyses were conducted using SPSS version 20. Results: Overall, 245 patients were included in the study with the majority being females (54.3%) and obese (47.8%). Only 29% of the participants had controlled glycemia (HbA1c <7%) with 31.8% of subjects had high adherence to their medication compared to 68.2% with low adherence. Increased working hours/day was associated with a decrease in adherence to oral antidiabetic medication (OR=0.31; 95% CI 0.11:0.88; p=0.029). Other factors significantly associated with decreased adherence to treatment were forgetfulness, high drug costs, complex treatment regimens, experiencing side effects, and perception of treatment inefficacy. Postponing physician office visits also decreased the probability of being adherent to oral antidiabetic medication (OR=0.36; 95% CI 0.15:0.86; p=0.022). Skipping or doubling the dose in case of hypo/hyperglycemia and the sensation of treatment burden also decreased medication adherence (OR=0.09; 95% CI 0.02:0.34; p=0.001, and OR=0.04; 95% CI 0.01:0.13; p<0.001 respectively). Conclusions: Adherence to oral antidiabetic medication is low for Lebanese patients, which leads to a poor glycemic control and increases the diabetes complications. Intervention programs including patient education strategies are essential to improve medication adherence