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Deep neural networks (DNNs) are gaining popularity in a wide range of domains, ranging from speech and video recognition to healthcare. With this increased adoption comes the pressing need for securing DNN execution environments on CPUs, GPUs, and ASICs. While there are active research efforts in supporting a trusted execution environment (TEE) on CPUs, the exploration in supporting TEEs on accelerators is limited, with only a few solutions available [18, 19, 27]. A key limitation along this line of work is that these secure DNN accelerators narrowly consider a few specific architectures. The design choices and the associated cost for securing these architectures do not transfer to other diverse architectures. This paper strives to address this limitation by developing a design space exploration tool for supporting TEEs on diverse DNN accelerators. We target secure DNN accelerators equipped with cryptographic engines where the cryptographic operations are closely coupled with the data movement in the accelerators. These operations significantly complicate the scheduling for DNN accelerators, as the scheduling needs to account for the extra on-chip computation and off-chip memory accesses introduced by these cryptographic operations, and even needs to account for potential interactions across DNN layers. We tackle these challenges in our tool, called SecureLoop, by introducing a scheduling search engine with the following attributes: 1) considers the cryptographic overhead associated with every off-chip data access, 2) uses an efficient modular arithmetic technique to compute the optimal authentication block assignment for each individual layer, and 3) uses a simulated annealing algorithm to perform cross-layer optimizations. Compared to the conventional schedulers, our tool finds the schedule for secure DNN designs with up to 33.2% speedup and 50.2% improvement of energy-delay-product.

2022 International Electron Devices Meeting (IEDM), San Francisco, CA, USA, III-Nitrides are attractive as field emission devices for high frequency, high power, and harsh environment applications. A wet-based digital etching and a novel device geometry was used to demonstrate GaN vertical self-alignedgate (SAG) field emitter arrays (FEA) with uniform tips of sub- 10 nm tip radius. The best GaN FEA has a current density (JA) of 10 A/cm2 at VGE = 50 V, which is better than the state-of-the-art Si field emitter arrays at the same bias condition., Department of Defense (DoD)

Point-of-care testing using paper-based lateral flow assays (LFAs) has emerged as an attractive diagnostic platform. However, detecting small molecules such as cortisol using LFAs is challenging due to limited binding sites and weak signal generation. Here, we report the development of cortisol-specific aptamer-based probes and a paper-based origami preconcentrator (POP) to amplify the probe signal. The cortisol-specific aptamers were conjugated onto gold nanoparticles and hybridized with signal probes to create the cortisol-specific signal-on probe. POP, consisting of patterned layers with convergent wicking zones, induces electrokinetic preconcentration of the released signaling probes. By integrating cortisol-selective aptamer-based probes and POP, we accurately diagnosed cortisol levels within 30 min of signal probe incubation, followed by 10 min of preconcentration. Our sensor was able to detect cortisol levels in the range of 25–1000 ng/mL, with typical cortisol levels in plasma ranging from 40 to 250 ng/mL falling within this range. The successful detection of the wide range of cortisol samples using this approach highlights the potential of this platform as a point-of-care testing tool, particularly for lateral flow assay-based detection of small molecules like cortisol. Our approach offers a convenient and reliable method of cortisol level testing with a portable and accessible diagnosis device.

On-device learning and efficient fine-tuning enable continuous and privacy-preserving customization (e.g., locally fine-tuning large language models on personalized data). However, existing training frameworks are designed for cloud servers with powerful accelerators (e.g., GPUs, TPUs) and lack the optimizations for learning on the edge, which faces challenges of resource limitations and edge hardware diversity. We introduce PockEngine: a tiny, sparse and efficient engine to enable fine-tuning on various edge devices. PockEngine supports sparse backpropagation: it prunes the backward graph and sparsely updates the model with measured memory saving and latency reduction while maintaining the model quality. Secondly, PockEngine is compilation first: the entire training graph (including forward, backward and optimization steps) is derived at compile-time, which reduces the runtime overhead and brings opportunities for graph transformations. PockEngine also integrates a rich set of training graph optimizations, thus can further accelerate the training cost, including operator reordering and backend switching. PockEngine supports diverse applications, frontends and hardware backends: it flexibly compiles and tunes models defined in PyTorch/TensorFlow/Jax and deploys binaries to mobile CPU/GPU/DSPs. We evaluated PockEngine on both vision models and large language models. PockEngine achieves up to 15 × speedup over off-the-shelf TensorFlow (Raspberry Pi), 5.6 × memory saving back-propagation (Jetson AGX Orin). Remarkably, PockEngine enables fine-tuning LLaMav2-7B on NVIDIA Jetson AGX Orin at 550 tokens/s, 7.9 × faster than the PyTorch.

Holographic displays generate Three-Dimensional (3D) images by displaying single-color holograms time-sequentially, each lit by a single-color light source. However, representing each color one by one limits brightness in holographic displays. This paper introduces a new driving scheme for realizing brighter images in holographic displays. Unlike the conventional driving scheme, our method utilizes three light sources to illuminate each displayed hologram simultaneously at various intensity levels. In this way, our method reconstructs a multiplanar three-dimensional target scene using consecutive multi-color holograms and persistence of vision. We co-optimize multi-color holograms and required intensity levels from each light source using a gradient descent-based optimizer with a combination of application-specific loss terms. We experimentally demonstrate that our method can increase the intensity levels in holographic displays up to three times, reaching a broader range and unlocking new potentials for perceptual realism in holographic displays.

We report graphene films composed mostly of one or two layers of graphene grown by controlled carbon precipitation on the surface of polycrystalline Ni thin films during atmospheric chemical vapor deposition (CVD). Controlling both the methane concentration during CVD and the substrate cooling rate during graphene growth can significantly improve the thickness uniformity. As a result, one- or two- layer graphene regions occupy up to 87% of the film area. Single layer coverage accounts for 5%–11% of the overall film. These regions expand across multiple grain boundaries of the underlying polycrystalline Ni film. The number density of sites with multilayer graphene/graphite (>2 layers) is reduced as the cooling rate decreases. These films can also be transferred to other substrates and their sizes are only limited by the sizes of the Ni film and the CVD chamber. Here, we demonstrate the formation of films as large as 1 in2. These findings represent an important step towards the fabrication of large-scale high-quality graphene samples., National Science Foundation (U.S.) (CTS 05-06830), National Science Foundation (U.S.) (DMR07-04197)

2024 IEEE Custom Integrated Circuits Conference April 21st – 24th, 2024 Denver, CO U.S., Machine learning (ML) accelerators provide energy efficient neural network (NN) implementations for applications such as speech recognition and image processing. Recently, digital IMC has been proposed to reduce data transfer energy, while still allowing for higher bitwidths and accuracies necessary for many workloads, especially with technology scaling [1,2]. Privacy of ML workloads can be exploited with physical side-channel attacks (SCAs) or bus probing attacks (BPAs) [3] (Fig. 1). While SCAs correlate IC power consumption or EM emissions to data or operations, BPAs directly tap traces between the IC and off-chip memory. The inputs reflect private data collected on IoT devices, such as images of faces. The weights, typically stored off-chip, reveal information about proprietary private training datasets. This work presents the first IMC macro protected against SCAs and BPAs to mitigate these risks., National Science Foundation (NSF), MIT-IBM Watson AI Lab, MathWorks Engineering Fellowship

This paper investigates the effect of rotor flux screens (conducting screens) on the performance of switched reluctance motors with a higher number of rotor poles than stator poles. A high number of rotor poles decreases the torque ripple, which is desirable for electric vehicle applications. The conducting screens fill the rotor interpolar gaps forming a cylindrical rotor hence alleviating the windage losses, especially effective at high speeds. The effect of conducting screen thickness and material electrical conductivity on current rise time, developed torque, and output power is studied. A procedure for calculating the effective value of unaligned inductance for screened motors using the flux tube method is presented. Finite element analysis results for different screen cases confirm the effectiveness of conducting screens in improving the torque, hence power capability, of switched reluctance motors. Received: 11 January 2023 Accepted: 03 May 2023 Published: 30 May 2023

Recently, renewable energy sources have acquired more attention as compared with conventional energy sources due of their renewable and eco-friendly nature. DC-DC converters are crucial in their applications. Two different configurations based on the conventional boost converter are compared for renewable energy source applications in this paper. In one configuration, two boost converters are connected in series with a single switch (Quadratic Boost Converter), and in another, they are connected in parallel with two switches (Interleaved Boost Converter). The first configuration has a high voltage gain with a low duty cycle, while the second has a low input current ripple. The two configurations are compared in various aspects, like output voltage, voltage gain, inductor current, inductor current ripple and component counts. The configurations are designed in the MATLAB simulation environment by using the PLECS block set to validate the theoretical studies with ideal values. With a duty cycle value of 0.6 and a 12 V input voltage, the output voltage of the quadratic boost converter is 75 V and that of the interleaved boost converter is 30 V.

The aim of this paper is to present an intelligent control method for the maximum power point tracking (MPPT) of photovoltaic system under different climatic conditions with regard to temperature and irradiation. A Pattern Search algorithm (PS) is suggested to be combined with the maximum power point tracker as a strategic optimization for the photovoltaic panel. The voltaic system is composed of a solar panel and PS MPP tracker and it is simulated and evaluated. The system has shown a better performance as well as the effectiveness of PS MPP in terms of getting maximum energy production despite the changing climate conditions.

ISSN: 2664-5661 The 6th APSCE International Conference on Computational Thinking and STEM Education 2022 (CTE-STEM 2022) is organized by the Asia-Pacific Society for Computers in Education (APSCE) and hosted by the Leiden-Delft-Erasmus Centre for Education and Learning (LDE-CEL). CTE-STEM 2022 is hosted for the first time in Europe by the Delft University of Technology (TU Delft), Delft, the Netherlands. This conference continues from the success of the previous four international Computational Thinking conferences organized by the National Institute of Education and Nanyang Technological University (NIE/NTU). This conference invites CT as well as STEM researchers and practitioners to share their findings, processes, and outcomes in the context of computing education or computational thinking.

A simple yet accurate model for the transmission of surface plasmon polaritons (SPPs) in a finite metal-insulator-metal (MIM) waveguide to the sides of the apertures is proposed and demonstrated to be more accurate than the available models. It is as simple as using a magnetic current density across the plane of the aperture whose value is defined by the SPPs with any number of modes in the waveguide through the equivalence principle. Then, the generated SPPs on both sides of the aperture are extracted from the convolution integral of the equivalent current density and Green's function. As a result, the model provides the transmission coefficients of the SPPs in the MIM waveguide to the side walls of the aperture accurately and efficiently; not only for symmetric MIM waveguides with a single isolating layer but also non-symmetric ones with multi-layered insulating materials. The results are in very good agreement with those obtained by the finite-difference time-domain method and better than the other approximations available in literature for a wide range of aperture widths., NA

The process of information carrying molecules (IcM) dispersing from a molecular transmitter (MTx) to a molecular receiver (MRx) and the process of these molecules binding to the MRx are two major components of the Molecular Communications (MC). There are many approaches to model these two processes separately. While these models are very efficient when used alone, they suffer from compatibility issues. In this work, we reconcile the models for these two processes in a unified framework. Our proposed framework is influenced by the wave-particle duality of light. We model the dispersion of molecules using the wave model and the interactions using the particle model. We demonstrate our algorithm specifically for the case of synaptic molecular communication (SMC) for a cuboid synaptic cleft, however it can be adapted to any MC system as long as the MTx and MRx exhibit only insignificant positional changes relative to each other., This work was supported by the AXA Research Fund (AXA Chair for Internet of Everything at Koç University).

Energy differences in nuclei are paramount to understanding the nuclear systems far from stability, their rare decay modes, and trends in their relative stability. Modern ion trapping techniques such as Penning Trap Mass Spectrometry (PTMS), and Multiple Reflection Time Of Flight Mass Spectrometry (MRTOF-MS) have enabled the precision measurement of energy trends in even very short-lived nuclei, by allowing their masses to be measured. Such nuclei can be generated in large quantities at TRIUMF's Isotope Separator and Accelerator (ISAC), and TRIUMF's Ion Trap for Atomic and Nuclear science (TITAN) specializes in measuring the masses of the short-lived species produced at ISAC using an array of ion traps. This thesis describes measurements of the masses of neutron-deficient lanthanide elements produced at the ISAC facility at TRIUMF in Vancouver, Canada using the TITAN Multiple Reflection Time-Of-Flight (MR-TOF) mass spectrometer. The goal of these measurements was to extend the known existence of the N=82 shell closure up to Yb (Z=70), and to experimentally determine the location of the proton drip-line in Tm (Z=69). Mass measurements of 150Yb, 151Yb, and 153Yb confirm the continued existence of the N=82 shell closure. Additionally, new mass measurements of 149Tm and 150Tm were performed, as well as a re-measurement of 149Er that provides a correction to the literature value as published in the 2020 Atomic Mass Evaluation. Together, these mass data points provide the first experimental confirmation that 149Tm is the first proton-unbound species in the Tm isotopic chain. Furthermore, a new technique intended for rapidly cooling highly-charged ions (HCIs) produced via charge-breeding in TITAN's Electron Beam Ion Trap (EBIT) is experimentally tested. This technique involves sympathetically cooling the ions using free electrons, and holds the promise of cooling HCIs on a time-scale of seconds or less for studies with either short-lived or stable species. Technical challe

Recent successes of Deep Learning-powered AI are largely due to the trio of: algorithms, GPU computing, and big data. Data could take the shape of hospital records, satellite images, or the text in this paragraph. Deep Learning algorithms typically need massive collections of data before they can make reliable predictions. This limitation inspired investigation into a class of techniques referred to as Data Augmentation. Data Augmentation was originally developed as a set of label-preserving transformations used in order to simulate large datasets from small ones. For example, imagine developing a classifier that categorizes images as either a “cat” or a “dog”. After initial collection and labeling, there may only be 500 of these images, which are not enough data points to train a Deep Learning model. By transforming these images with Data Augmentations such as rotations and brightness modifications, more labeled images are available for model training and classification! In addition to applications for learning from limited labeled data, Data Augmentation can also be used for generalization testing. For example, we can augment the test set to set the visual style of images to “winter” and see how that impacts the performance of a stop sign detector. The dissertation begins with an overview of Deep Learning methods such as neural network architectures, gradient descent optimization, and generalization testing. Following an initial description of this technology, the dissertation explains overfitting. Overfitting is the crux of Deep Learning methods in which improvements to the training set do not lead to improvements on the testing set. To the rescue are Data Augmentation techniques, of which the Dissertation presents an overview of the augmentations used for both image and text data, as well as the promising potential of generative data augmentation with models such as ChatGPT. The dissertation then describes three major experimental works revolving around CIFAR-10, 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

Online advertising [100], as a multi-billion dollar business, provides a common marketing experience when people access online services using electronic devices, such as desktop computers, tablets, smartphones, and so on. Using the Internet as a means of advertising, different stakeholders take actions in the background to provide and deliver advertisements to users through numerous platforms, such as search engines, news sites, and social networks, where dedicated spots of areas are used to display advertisements (ads) along with search results, posts, or page content. Online advertising is mainly based on dynamically selecting ads through a real-time bidding (or auction) mechanism. Predicting user responses like clicking ads in e-commerce platforms and internet-based advertising systems, as the first measurable user response, is an essential step for many digital advertising and recommendation systems to capture the user’s propensity to follow up actions, such as purchasing a product or subscribing to a service. To maximize revenue and user satisfaction, online advertising platforms must predict the expected user behavior of each displayed advertisement and maximize the user’s expectations of clicking [28]. Based on this observed feedback, these systems are tailored to user preferences to decide the order in that ads or any promoted content should be served to them. This objective provides an incentive to develop new research by using ideas derived from different domains like machine learning and data mining combined with models for information retrieval and mathematical optimization. They introduce different machine learning and data mining methods that employ deep learning-based predictive models to learn the representation of input features with the aim of user response prediction. Feature representation learning is known as a fundamental task on how to input information is going to be represented in machine learning models. A good feature representation learni, 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

Skin cancer is a prevalent cancer that significantly contributes to global mortality rates. Early detection is crucial for a high survival rate. Dermatologists primarily rely on visual inspection to diagnose skin cancers, but this method is inaccurate. Deep learning algorithms can enhance the diagnostic accuracy of skin cancers. However, these algorithms require substantial labeled data for effective training. Acquiring annotated data for skin cancer classification is time-consuming, expensive, and necessitates expert annotation. Moreover, skin cancer datasets often suffer from imbalanced data distribution. Generative Adversarial Networks (GANs) can be used to overcome the challenges of data scarcity and lack of labels by automatically generating skin cancer images. However, training and testing data from different distributions can introduce domain shift and bias, impacting the model’s performance. This dissertation addresses this issue by developing deep learning-based domain adaptation models. Additionally, this research emphasizes deploying deep learning models on hardware to enable real-time skin cancer detection, facilitating accurate diagnoses by dermatologists. Deploying conventional deep learning algorithms on hardware is not preferred due to the problem of high resource consumption. Therefore, this dissertation presents spiking neural network-based (SNN) models designed specifically for hardware implementation. SNNs are preferred for their power-efficient behavior and suitability for hardware deployment., 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

Vision is a critical sense for many species, with the perception of motion being a fundamental aspect. This aspect often provides richer information than static images for understanding the environment. Motion recognition is a relatively simple computation compared to shape recognition. Many creatures can discriminate moving objects quite well while having virtually no capacity for recognizing stationary objects. Traditional methods for collision-free navigation require the reconstruction of a 3D model of the environment before planning an action. These methods face numerous limitations as they are computationally expensive and struggle to scale in unstructured and dynamic environments with a multitude of moving objects. This thesis proposes a more scalable and efficient alternative approach without 3D reconstruction. We focus on visual motion cues, specifically ’visual looming’, the relative expansion of objects on an image sensor. This concept allows for the perception of collision threats and facilitates collision-free navigation in any environment, structured or unstructured, regardless of the vehicle’s movement or the number of moving objects present., 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

In today’s world, data is generated at an unprecedented rate, and a significant portion of it is unstructured text data. The recent advancements in Natural Language Processing have enabled computers to understand and interpret human language. Data mining techniques were once unable to use text data due to the high dimensionality of text processing models. This limitation was overcome with the ability to represent data as text. This thesis aims to compare the predictive performance of structured versus unstructured text data in two different applications. The first application is in the field of real estate. We compare the performance of tabular real-estate data and unstructured text descriptions of homes to predict the house price. The second application is in translating Electronic Health Records (EHR) tabular data to text data for survival classification of COVID-19 patients. Lastly, we present a range of strategies and perspectives for future research., 2023, Includes bibliography., Degree granted: Thesis (MS)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

The major objective of this dissertation was to create a framework which is used for medical image diagnosis. In this diagnosis, we brought classification and diagnosing of diseases through an Artificial Intelligence based framework, including COVID, Pneumonia, and Melanoma cancer through medical images. The algorithm ran on multiple datasets. A model was developed which detected the medical images through changing hyper-parameters. The aim of this work was to apply the new transfer learning framework DenseNet-201 for the diagnosis of the diseases and compare the results with the other deep learning models. The novelty in the proposed work was modifying the Dense Net 201 Algorithm, changing hyper parameters (source weights, Batch Size, Epochs, Architecture (number of neurons in hidden layer), learning rate and optimizer) to quantify the results. The novelty also included the training of the model by quantifying weights and in order to get more accuracy. During the data selection process, the data were cleaned, removing all the outliers. Data augmentation was used for the novel architecture to overcome overfitting and hence not producing false absurd results the computational performance was also observed. The proposed model results were also compared with the existing deep learning models and the algorithm was also tested on multiple datasets., 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

The emergence of multidrug-resistant bacteria as a global health threat has necessitated the exploration of alternative treatments to combat bacterial infections. Among these, photocatalytic nanomaterials such as quantum dots (QDs) have shown great promise and type-I QDs have been investigated thus far. In this study, we introduce type-II InP/ZnO core/shell QDs that are ligand-exchanged with a short-chain inorganic sulfide ion (S2−) for antibacterial activity. Interestingly, InP/ZnO QDs simultaneously generate reactive oxygen species (ROS) including hydroxyl (•OH) and superoxide (O2•−) radicals, while only O2•− radicals can be released by the type-I sulfide-capped InP/ZnS QDs. The optimized nanostructure achieved effective inhibition of Pseudomonas aeruginosa and Escherichia coli bacteria growth to the level of 99.99% and 70.31% under low-intensity green light illumination of 5 mW.cm−2. Our findings highlight the importance of type-II QDs as a new avenue for developing effective antibacterial agents against drug-resistant pathogens., European Union (EU); Horizon 2020; Research and Innovation Programme; Marie SKŁODOWSKA-CURIE; STIMULUS

Mechanical search is a robotic problem where a robot needs to retrieve a target item that is partially or fully-occluded from its camera. State-of-the-art approaches for mechanical search either require an expensive search process to find the target item, or they require the item to be tagged with a radio frequency identification tag (e.g., RFID), making their approach beneficial only to tagged items in the environment. We present FuseBot, the first robotic system for RF-Visual mechanical search that enables efficient retrieval of both RF-tagged and untagged items in a pile. Rather than requiring all target items in a pile to be RF-tagged, FuseBot leverages the mere existence of an RF-tagged item in the pile to benefit both tagged and untagged items. Our design introduces two key innovations. The first is RF-Visual Mapping, a technique that identifies and locates RF-tagged items in a pile and uses this information to construct an RF-Visual occupancy distribution map. The second is RF-Visual Extraction, a policy formulated as an optimization problem that minimizes the number of actions required to extract the target object by accounting for the probabilistic occupancy distribution, the expected grasp quality, and the expected information gain from future actions. We built a real-time end-to-end prototype of our system on a UR5e robotic arm with in-hand vision and RF perception modules. We conducted over 200 real-world experimental trials to evaluate FuseBot and compare its performance to a state-of-the-art vision-based system named X-Ray (Danielczuk et al., in: 2020 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, 2020). Our experimental results demonstrate that FuseBot outperforms X-Ray’s efficiency by more than 40% in terms of the number of actions required for successful mechanical search. Furthermore, in comparison to X-Ray’s success rate of 84%, FuseBot achieves a success rate of 95% in retrieving untagged items, demonst

National Aeronautics and Space Administration (NASA), Lockheed Martin Corp., AFOSR, Qualcomm Inc.

Background The CDH1 gene codes for the epithelial-cadherin (E-cad) protein, which is embedded in the plasma membrane of epithelial cells to form adherens junctions. E-cad is known to be essential for maintaining the integrity of epithelial tissues, and the loss of E-cad has been widely considered a hallmark of metastatic cancers enabling carcinoma cells to acquire the ability to migrate and invade nearby tissues. However, this conclusion has come under scrutiny. Methods To assess how CDH1 and E-cad expression changes during cancer progression, we analyzed multiple large transcriptomics, proteomics, and immunohistochemistry datasets on clinical cancer samples and cancer cell lines to determine the CDH1 mRNA and E-cad protein expression profiles in tumor and normal cells. Results In contrast to the textbook knowledge of the loss of E-cad during tumor progression and metastasis, the levels of CDH1 mRNA and E-cad protein are either upregulated or remain unchanged in most carcinoma cells compared to normal cells. In addition, the CDH1 mRNA upregulation occurs in the early stages of tumor development and the levels remain elevated as tumors progress to later stages across most carcinoma types. Furthermore, E-cad protein levels are not downregulated in most metastatic tumor cells compared to primary tumor cells. The CDH1 mRNA and E-cad protein levels are positively correlated, and the CDH1 mRNA levels are positively correlated to cancer patient’s survival. We have discussed potential mechanisms underlying the observed expression changes in CDH1 and E-cad during tumor progression. Conclusions CDH1 mRNA and E-cadherin protein are not downregulated in most tumor tissues and cell lines derived from commonly occurring carcinomas. The role of E-cad in tumor progression and metastasis may have previously been oversimplified. CDH1 mRNA levels may serve as a reliable biomarker for the diagnosis of some tumors (such as colon and endometrial carcinomas) due to the marked upr