Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Analytics to Assess Deep Learning Models for Cross-Modal Brain Tumor Segmentation

Accurate delineations of anatomically relevant structures are required for cancer treatment planning. Despite its accuracy, manual labeling is time-consuming and tedious—hence, the potential of automatic approaches, such as deep learning models, is being investigated. A promising trend in deep learning tumor segmentation is cross-modal domain adaptation, where knowledge learned on one source distribution (e.g., one modality) is transferred to another distribution. Yet, artificial intelligence (AI) engineers developing such models, need to thoroughly assess the robustness of their approaches, which demands a deep understanding of the model(s) behavior. In this paper, we propose a web-based visual analytics application that supports the visual assessment of the predictive performance of deep learning-based models built for cross-modal brain tumor segmentation. Our application supports the multi-level comparison of multiple models drilling from entire cohorts of patients down to individual slices, facilitates the analysis of the relationship between image-derived features and model performance, and enables the comparative exploration of the predictive outcomes of the models. All this is realized in an interactive interface with multiple linked views. We present three use cases, analyzing differences in deep learning segmentation approaches, the influence of the tumor size, and the relationship of other data set characteristics to the performance. From these scenarios, we discovered that the tumor size, i.e., both volumetric in 3D data and pixel count in 2D data, highly affects the model performance, as samples with small tumors often yield poorer results. Our approach is able to reveal the best algorithms and their optimal configurations to support AI engineers in obtaining more insights for the development of their segmentation models.
Inhibition of Fibroblast Growth Factor Receptor Attenuates UVB-Induced Skin Carcinogenesis

Altered ﬁbroblast GF receptor (FGFR) signaling has been shown to play a role in a number of cancers. However, the role of FGFR signaling in the development and progression of UVB-induced cutaneous squamous cell carcinoma remains unclear. In this study, the effect of UVB radiation on FGFR activation and its downstream signaling in mouse skin epidermis was examined. In addition, the impact of FGFR inhibition on UVB-induced signaling and skin carcinogenesis was also investigated. Exposure of mouse dorsal skin to UVB signiﬁcantly increased the phosphorylation of FGFRs in the epidermis as well as the activation of downstream signaling pathways, including protein kinase B/mTOR, signal transducers and activators of transcription, and MAPK. Topical application of the pan-FGFR inhibitor AZD4547 to mouse skin before exposure to UVB signiﬁcantly inhibited FGFR phosphorylation as well as mTORC1, signal transducer and activator of transcription 3, and MAPK activation (i.e., phosphorylation). Moreover, AZD4547 pretreatment signiﬁcantly inhibited UVB-induced epidermal hyperplasia and hyperproliferation and reduced the inﬁltration of mast cells and macrophages into the dermis. AZD4547 treatment also signiﬁcantly inhibited mRNA expression of inﬂammatory genes in the epidermis. Finally, mice treated topically with AZD4547 before UVB exposure showed decreased cutaneous squamous cell carcinoma incidence and increased survival rate. Collectively, the current data support the hypothesis that inhibition of FGFR in the epidermis may provide a new strategy to prevent and/or treat UVB-induced cutaneous squamous cell carcinoma.
Towards a Biologically Plausible Stereo Approach

A computational model for stereoscopic disparity estimation has been recently introduced which reformulates, in neurophysiologically plausible terms, the traditional image matching approach (Torreao, 2007). The left and right stereo images are assumed viewed through the receptive fields of cortical simple cells, modeled as Gabor functions (Marcelja, 1980). The Green’s functions of a matching equation (whose uniform solutions are also Gabor functions) are used to filter the receptive-field modulated inputs, introducing different relative shifts between them. A measure of the local degree of matching of such shifted inputs can then be used for the estimation of stereoscopic disparities, in a way which is reminiscent of the energy model for the responses of cortical complex cells (Adelson & Bergen, 1985; Ohzawa et al., 1997). Although based on well-established neurophysiological concepts, the Green’s function approach is still far from being biologically plausible. For instance, it assumes as cortical inputs the original irradiance images, disregarding the transformations performed by the earlier stages of the visual pathway− namely, the retina and the lateral geniculate nucleus. As a further step towards a fully biological stereo, we here introduce an improved version of the algorithm in (Torreao, 2007), incorporating a new concept of signal coding by input-dependent receptive fields (Torreao et al., 2009). The classical receptive field description, which assumes a fixed, stimulus-independent spatial organization, has recently been challenged by neurophysiological indications that the receptive-field structure does change with neuronal input (Allman et al., 1985; Bair, 2005; David et al., 2004). In (Torreao et al., 2009), a signal coding scheme has been introduced, where the parameters of the coding functions are obtained from the Fourier transform of the coded signal, and, in (Torreao & Victer, 2010), such scheme has been taken up as a model for stimulus-dependent center-surround (CS) receptive fields, such as those found in the retina and in the lateral geniculate nucleus. Assuming that the role of the CS structures is that of decorrelating natural images, as suggested in (Attick & Redlich, 1992) and (Dan et al., 1996), center-surround receptive fields which code whitened versions of the input signals have been obtained. We show that, by incorporating a similar center-surround coding module into the Green’s function stereo algorithm, we are able to obtain better quality disparity estimates, through an approach which is closer to the neurophysiological situation. Towards a Biologically Plausible Stereo Approach
Efficiently Modelling Resource in a Process Algebra

In a concurrent system the effects of contention for resource are primary for both understanding and controlling the behaviour of the system. Since resources are inherently shared hoping for a compositional presentation seems highly unlikely. Equally, in this context, composition in terms of the ability to subdivide resource seems an ambitious goal. In this presentation we demonstrate that, by exploiting synchrony, we can present resources in a divisible manner. Further, this eradicates counting duplication (counts residing both in the resource representation and in the claiming entities) greatly reducing the state space of the system. Finally our compositional representation of resource usage in a synchronous process algebra is obtained without any changes or additions to the underlying language and could be achieved in ’bare’ SCCS [7]. For brevity we assume a basic familiarity with asynchronous (such as CCS) and synchronous process algebra (such as SCCS) for an introduction see [8] and the SCCS probabilistic/prioritized extension WSCCS[11].
Extended Abstract for Poster: “A New Polymorphic Computing Architecture Based on Fine-Grained Instruction Mobility”

This is a summary of the base concepts behind David Hentrich’s May 2018 Ph.D. dissertation in Polymorphic Computing. Polymorphic Computing is the emerging field of changing the computer architecture around the software, rather than vice versa. The main contribution is a new polymorphic computing architecture. The key idea behind the architecture is to create an array of processors where a program’s instructions can be individually and arbitrarily assigned/mobilized to any processor, even during runtime. The key enablers of this architecture are a dataflow instruction set that is conducive to instruction migration, a microarchitectual block called an “operation cell” (“op-cell”), a processor built around the instruction set and the “op-cells”, and arrays of these processors
Investment-Specific News Shocks in Business Cycles

Recent research in DSGE and VAR models have indicated that the most important drivers of business cycles are unanticipated contemporaneous IST shocks, not TFP shocks. Research in VAR models have also empirically identified IST news shocks as a significant driving force behind the U.S. business cycle. However, recent one-sector DSGE models with sticky prices found that IST news shocks do not produce comovement of aggregate variables with the share of the forecast error variance explained by IST news shocks being very small in a flexible-price model and essentially zero in a sticky-price model. This paper studies the short-run business cycle effects of IST news shocks in a two-sector DSGE model with sticky prices and collateral constraints. In our model, IST news shocks can produce comovement of aggregate variables, as opposed to existing DSGE models with flexible prices that produce comovement relying on variable capital utilization, investment adjustment costs, and preferences with small short-run wealth effects on the labor supply. Our variance decomposition exercises indicate that IST news shocks are a more relevant source of uncertainty than unanticipated contemporaneous IST shocks.
D OMAIN G ENERALIZATION FOR R OBUST M ODEL -B ASED O FFLINE RL

Existing offline reinforcement learning (RL) algorithms typically assume that training data is either: 1) generated by a known policy, or 2) of entirely unknown origin. We consider multi-demonstrator offline RL, a middle ground where we know which demonstrators generated each dataset, but make no assumptions about the underlying policies of the demonstrators. This is the most natural setting when collecting data from multiple human operators, yet remains unexplored. Since different demonstrators induce different data distributions, we show that this can be naturally framed as a domain generalization problem, with each demonstrator corresponding to a different domain. Specifically, we propose Domain-Invariant Model-based Offline RL (DIMORL), where we apply Risk Extrapolation (REx) (Krueger et al., 2020) to the process of learning dynamics and rewards models. Our results show that models trained with REx exhibit improved domain generalization performance when compared with the natural baseline of pooling all demonstrators’ data. We observe that the resulting models frequently enable the learning of superior policies in the offline model-based RL setting, can improve the stability of the policy learning process, and potentially increase exploration.
CAESAR: Coherence-Aided Elective and Seamless Alternative Routing via on-chip FPGA

Prompted by the ever-growing demand for high-performance System-on-Chip (SoC) and the plateauing of CPU frequencies, the SoC design landscape is shifting. In a quest to offer programmable specialization, the adoption of tightly-coupled FPGAs co-located with traditional compute clusters has been embraced by major vendors. This $\mathbf{CPU}+\mathbf{FPGA}$ architectural paradigm opens the door to novel hardware/software co-design opportunities. The key principle is that CPU-originated memory traffic can be re-routed through the FPGA for analysis and management purposes. Albeit promising, the side-effect of this approach is that time-critical operations—such as cache-line refills—are fulfilled by moving data over slower interconnects meant for I/O traffic. In this article, we introduce a novel principle named Cache Coherence Backstabbing to precisely tackle these shortcomings. The technique leverages the ability to include the FGPA in the same coherence domain as the core processing elements. Importantly, this enables Coherence-Aided Elective and Seamless Alternative Routing (CAESAR), i.e., seamless inspection and routing of memory transactions, especially cache-line refills, through the FPGA. CAESAR allows the definition of new memory programming paradigms. We discuss the intrinsic potentials of the approach and evaluate it with a full-stack prototype implementation on a commercial platform. Our experiments show an improvement of up to 29% in read bandwidth, 23% in latency, and 13% in pragmatic workloads over the state of the art. Furthermore, we showcase the first in-coherence-domain run-time profiler design as a use-case of the CAESAR approach.
LiDAR and Camera Sensor Fusion for Onboard sUAS Detection and Tracking

The recent growth in numbers of small unmanned aerial systems (sUAS) has raised concerns among civilian and military organizations, as they can jeopardize critical infrastructure and threaten manned aircraft. Detecting and tracking intrusive drones is essential to construct reliable counter sUAS (C-sUAS) solutions. Onboard payload sensors typically include electro-optical (EO) cameras, which can be lightweight and provide high-resolution information of the surrounding scene. However, continually searching for targets across high-resolution images is computationally expensive and susceptible to an increase in false positives. Furthermore, EO cameras cannot measure distances directly, which light detection and ranging (LiDAR) sensors can, generating point clouds at a frequency up to 20 Hz with ranges over 100 m . However, these are usually sparse and cannot recognize small targets. The present thesis studies each sensor’s capabilities and develops a sensor fusion solution. It develops a YOLO-based tracker for visual detection and tracking and studies the ability of a LiDAR to detect sUAS onboard an aircraft. Additionally, it demonstrates an extrinsic calibration procedure to project 3D LiDAR points into the camera frame accurately. The proposed sensor fusion solution aims to create regions of interest (ROI) from these LiDAR projections to narrow the YOLO-based tracker’s search window. Finally, experiments with multiple ﬂying targets were performed onboard an aircraft, demonstrating that the sensor fusion solution improves the YOLO-based tracker baseline results, increasing precision from 17 . 0% to 91 . 2%, and framerate, from 24 Hz to 57 Hz , keeping a similar recall of 41 . 9%, compared to 48 . 4%.
Optimization of Human-Aware Manufacturing and Logistics Systems: A Survey on the Modeling Frameworks of Human Aspects

Historically, Operations Research (OR) discipline has mainly been focusing on economic concerns. Since the early 2000s, human considerations are gaining increasing attention, pushed by the growing societal concerns of sustainable development on the same terms as the economic and ecological ones. This paper is the second part of a work that aims at reviewing the efforts dedicated by the OR community to the integration of human aspects into manufacturing and logistics systems. A focus is put on the modeling frameworks proposed to represent human characteristics, their practical relevance, and the complexity induced by their integration with mathematical optimization models. In this paper, the techniques used in the OR literature to represent the human considerations encountered in manufacturing and logistics systems are surveyed. The existing Human-Aware Modeling Frameworks (HAMF) are classified and reviewed by domain in the form of a toolbox. Particular attention is paid to the field validity of each method, its relevance to specific use cases, the required data collection, and its usability within mathematical optimization models. Since the surveyed HAMFs rely on concepts originating from different related scientific disciplines (e.g., ergonomics, occupational medicine), a brief introduction of these fields of study is proposed together with a work of contextualization that is carried out during the analysis.
WORKING PAPER MEDSEA-FIN: an estimated DSGE model with housing and financial frictions for Malta∗

This paper uses Bayesian techniques and Maltese data over the period 2001–2019 to estimate the parameters of MEDSEA-FIN, one of the Central Bank of Malta’s DSGE models. The model captures linkages between the housing sector, banks and the rest of the economy via a borrowing collateral constraint. The paper shows that the data is informative on a subset of the parameters, and documents that the dynamic properties of the estimated model are in line with similar DSGE models estimated for other countries. The results corroborate recent empirical findings for Malta documented in other studies. The model is used to decompose recent macroeconomic data and shows that housing demand shocks were important drivers of house prices and credit. Shocks from the euro area also drove a significant share of macroeconomic fluctuations. The paper also shows that the model survives external validation tests. Although the model remains somewhat stylized along some dimensions, estimation makes it suitable for policy analysis related to housing and credit markets and associated macroprudential policies. JEL Classification: C11, C32, C51, E21, E32
K EEP C ALM AND C ARRY O FFLINE : P OLICY R EFINEMENT IN O FFLINE R EINFORCEMENT L EARNING

The ability to discover optimal behaviour from fixed data sets has the potential to transfer the successes of reinforcement learning (RL) to domains where data collection is acutely problematic. In this offline setting a key challenge is overcoming overestimation bias for actions not present in data which, without the ability to correct for via interaction with the environment, can propagate and compound during training, thus leading to highly sub-optimal policies. One simple method to reduce this bias is to introduce a policy constraint via behavioural cloning (BC), which encourages agents to pick actions closer to the source data. By finding the right balance between RL and BC such approaches have been shown to be surprisingly effective while requiring minimal changes to the underlying algorithms they are based on. To date, this balance has been held constant but in this work we explore the idea of tipping this balance towards RL following initial training. Using TD3BC we demonstrate that by continuing to train a policy offline while reducing the influence of the BC component we can produce refined policies that outperform the original baseline, as well as match or exceed the performance of more complex alternative approaches. Furthermore, we show these refined policies can be fine-tuned online while largely mitigating severe performance drops.
I S H APPINESS R EALLY A W ARM G UN ? T HE C ONSEQUENCES OF U.S. W EAPONS S ALES FOR P OLITICAL V IOLENCE ∗

We exploit exogenous shifts in the cost of purchasing commercial weapons from the U.S. to un-cover the causal effect of U.S. weapons purchases on political violence. We ﬁnd that weapons purchases reduce the likelihood of political repression but increase the likelihood of onset of civil war in purchasing countries. The results suggest that state investment in military capability incites civil war in countries where state repression of an aggrieved opposition would have otherwise prevailed. weapons purchases on the intensive margin (quantity conditional on buying) than purchases on the extensive margin (buy or In this our instrument is more powerful if we are able to separate regular buyers from irregular buyers. Average weapons sales is as good a variable for types of this a buyer of a very large amount of a a of
Congestion control in machine learning clusters

This paper argues that fair-sharing, the holy grail of congestion control algorithms for decades, is not necessarily a desirable property in Machine Learning (ML) training clusters. We demonstrate that for a specific combination of jobs, introducing unfairness improves the training time for all competing jobs. We call this specific combination of jobs compatible and define the compatibility criterion using a novel geometric abstraction. Our abstraction rolls time around a circle and rotates the communication phases of jobs to identify fully compatible jobs. Using this abstraction, we demonstrate up to 1.3× improvement in the average training iteration time of popular ML models. We advocate that resource management algorithms should take job compatibility on network links into account. We then propose three directions to ameliorate the impact of network congestion in ML training clusters: (i) an adaptively unfair congestion control scheme, (ii) priority queues on switches, and (iii) precise flow scheduling.
Forest ﬂuxes and mortality response to drought: model description (ORCHIDEE-CAN-NHA r7236) and evaluation at the Caxiuanã drought experiment

Extreme drought events in Amazon forests are expected to become more frequent and more intense with climate change, threatening ecosystem function and carbon bal-ance. Yet large uncertainties exist on the resilience of this ecosystem to drought. A better quantiﬁcation of tree hydraulics and mortality processes is needed to anticipate future drought effects on Amazon forests. Most state-of-the-art dynamic global vegetation models are relatively poor in their mechanistic description of these complex processes. Here, we implement a mechanistic plant hydraulic module within the ORCHIDEE-CAN-NHA r7236 land surface model to simulate the percentage loss of conductance (PLC) and changes in water storage among organs via a representation of the water potentials and vertical water ﬂows along the continuum from soil to roots, stems and leaves. The model was evaluated against observed seasonal variability in stand-scale sap ﬂow, soil moisture and productivity under both control and drought setups at the Caxiuanã throughfall exclusion ﬁeld experiment in eastern Amazonia between 2001 and 2008. A relationship between PLC and tree mortality is built in the model from two empirical parameters, the cumulated duration of drought exposure that triggers mortality, and the mortality fraction in each day exceeding the exposure. Our model captures the large biomass drop in the year 2005 observed 4 years after throughfall reduction, and pro-duces comparable annual tree mortality rates with observation over the study period. Our hydraulic architecture module provides promising avenues for future research in as-similating experimental data to parameterize mortality due to drought-induced xylem dysfunction. We also highlight that species-based (isohydric or anisohydric) hydraulic traits should be further tested to generalize the model performance in predicting the drought risks.
International Journal of Electrical and Computer Engineering (IJECE)

Received Nov 9, 2021 Revised Jul 11, 2022 Accepted Aug 10, 2022 A 27-MHz wireless system with binary frequency shift keying (BFSK) modulation at 400-kHz is reported. The receiver has been designed to handle in-band interference corrupting the BFSK signal with the use of complex filters and amplitude comparison method. The BFSK modulation is carried out with a voltage-controlled oscillator before up-converting with a 27-MHz local oscillator. The bipolar junction transistors (BJT-based) power amplifier with 30% efficiency pumps 220 mW into a spiral antenna. The inductivedegenerated low-noise amplifier with a voltage of more than 30 dB amplifies an incoming signal before feeding into a mixer for complex direct down conversion. With deliberate Gaussian interference injection, the minimum ratios between the signal with interference and the interference only at the distance of 2.5, 10 and 15 m are 3.3, 8.5 and 11.5 dB, respectively at a maximum data rate of 20 kbps. Without any interference included, the system can achieve a data rate of 40 kbps at the maximum transmission distance of 15 m. Conceptually agreed with the presented bit-error-rate (BER) analysis, the BER measurements with Gaussian and single-tone/twotone in-band interferences also confirm superiority offered by the amplitude comparison method where the signal-to-noise ratio is at 1 dB for BER=10 at 10 kbps (10 dB better than the phase detection counterpart).