Miracle-dz/UniFAR-FTUs huggingface dataset:
What's So Hard About (Mixed-Size) Placement?

For years, integrated circuit design has been a driver for algorithmic advances. The problems encountered in the design of modern circuits are often intractable -- and with exponentially increasing size. Efficient heuristics and approximations have been essential to sustaining Moore's Law growth, and now almost every aspect of the design process is heavily automated. There is, however, one notable exception: there is often substantial floor planning effort from human designers to position large macro blocks. The lack of full automation on this step has motivated the exploration of novel optimization methods, most recently with reinforcement learning. In this paper, we argue that there are multiple forces which have prevented full automation -- and a lack of algorithmic methods is not the only factor. If the time has come for automation, there are a number of "traditional'' methods that should be considered again. We focus on recursive bisection, and highlight key ideas from partitioning algorithms that have broader impact than one might expect. We also stress the importance of benchmarking as a way to determine which approaches may be most effective.
Design and deployment of a customer journey management system: the CJMA approach

Customer journey management has been recently in high demand across organizations as a means of better understanding customer behavior, predicting user needs, and enhancing customer experience to achieve their business goals. Therefore, there is an urgent need for an affordable solution assisting enterprises, especially small and medium-sized enterprises (SMEs), in automatically extracting valuable customer journey insights from their existing data sources. For this reason, this paper presents an approach, called CJMA (Customer Journey Master) approach, for designing and deploying a customer journey management system. The proposed approach incorporates several journey analysis capabilities based on three process mining methods: process discovery, trace clustering, and decision mining. The proposed system was developed on top of Python’s Django web framework with four main functions: data centralizing, process modeling for all customer paths, customer journey clustering and customer decision predicting throughout journeys. The performance of the system has been evaluated based on the three criteria: execution time, accuracy and understandability of analytical findings, which produced high outcomes using the Google Merchandise Store dataset.
Prediction of Therapy Response in Patients with NSCLC based on CT Images

Lung cancer has the highest mortality rate, and patients with non-small cell lung cancer (NSCLC) account for 75% to 80% of these cases. Treatment response varies greatly among patients. Therefore, there is significant clinical value in predicting the therapy efficacy for patients. In this paper, we investigated the therapy efficacy prediction model based on CT images, including traditional radiomics methods and deep learning models in 2D and 3D. The model based on ResNet18 showed predictive performance and outperformed the conventional radiomics models in prediction.
Counterfactual Contextual Multi-Armed Bandit to Diagnose Post-Harvest Diseases of Apple

Post-harvest diseases of apple are one of the major issues in the economical sector of apple production, causing severe economical losses to producers. Thus, we developed DSSApple, a picture-based decision support system able to help users in the diagnosis of apple diseases. Specifically, this paper addresses the problem of sequentially optimizing for the best diagnosis, exploiting past interactions with the system and their contextual information (i.e., the evidence provided by the users), while exploring the set of candidate diseases. This online learning problem is commonly addressed in the literature through a stochastic active learning paradigm - i.e., Contextual Multi-Armed Bandit (CMAB). The methodology interactively updates the decision model considering the success of each past interaction with respect to the context provided in each round. However, contextual information is very often partial and inadequate to handle such a complex decision making problem. On the other hand, human-made decisions implicitly include unobserved factors (referred to as unobserved confounders) that significantly influence their choices. In this paper, we take advantage of the information embedded in the observed human decisions to marginalize confounders and improve the capability of the CMAB model to identify the correct diagnosis. Specifically, we propose a Counterfactual Thompson Sampling (CF-TS), a CMAB model based on the causal concept of counterfactual. The proposed model is validated with offline experiments based on data collected through a large user study on DSSApple application. The results prove that CF-TS is able to significantly outperform both traditional CMAB algorithms and observed user decisions, in the real-world task of predicting the correct apple disease.
Survey on Radio Frequency Identification Security and Attacks

∗ RFID technology offers tracking capabilities in locating people, items, animals in real-time, and provides accurate and efficient access to reliable medical data/ patient’s details to nurses/doctors. Healthcare organizations have been in the frontline in acquiring this technology; their views on the technology are to provide security for medicines and facilities equipment’s that are much expensive and costly to lose. This research paper caters to computing professionals who are not familiar with the specifics of RFID through a systematic review of the uses of RFID and the various common attacks/vulnerability with reference to the healthcare sector as the sample field of application of the RFID technology in various industries. The research paper refers to the existing research and application knowledge on the various types of RFID attacks and common uses in various industries.
Recent Advances and Future Challenges in 2.5D/3D Heterogeneous Integration

In this presentation, we will review the recent advances in chiplet-based commercial products and prototypes [2,3,4,5]. Most chiplet usage has been confined to integrating die designed by the same organization applied to building chips for the same product types. The right approach should be able to reduce portfolio costs, scale innovation and improve time to solution [1]. It is important to manage the associated trade-offs, such as thermal, power, I/O escapes, assembly, test, etc. We will conclude the talk by presenting the future 2.xD/3D integration opportunities becoming available [6].
Plusmine: Dynamic Active Learning with Semi-Supervised Learning for Automatic Classification

A major problem in cybersecurity research is the correct labeling of up-to-date datasets. It relies on the availability of human experts, and is as such very cumbersome. Motivated by this, two techniques have been proposed for efficient labeling: Active Learning (AL) and Semi-Supervised Learning (SeSL). In this paper, we introduce Plusmine: an intrusion detection method that combines the benefits of AL and SeSL to efficiently automate classification. We develop new techniques for both components. Moreover, we empirically show that Plusmine obtains good and more robust results than benchmark methods.
Neurophysiological and Behavioral Differences in Human-Multiagent Tasks: An EEG Network Perspective

Effective human-multiagent teams will incorporate the cognitive skills of the human with the autonomous capabilities of the multiagent group to maximize task performance. However, producing a seamless fusion requires a greater understanding of the human’s cognitive state as it reacts to uncertainties in both the task environment and agent dynamics. This study examines external behaviors in concert with neurophysiological measures acquired via electroencephalography (EEG) to probe the interactions between cognitive processes, behaviors, and performance in a human-multiagent team task. We show that changes in the α (8–12 Hz) and θ (4–8 Hz) bands of EEG indicate a higher burden on the cognitive resources associated with visual-spatial reasoning required to estimate a more complex kinematic state of robotic agents. These results are reinforced by complementary behavioral shifts in gaze and pilot inputs. Additionally, higher-performing participants tend to engage more actively in the task by utilizing greater amounts of visual-spatial reasoning. Finally, we show that features based on EEG dynamic-network-metrics provide discriminative information that distinguishes gaze behaviors associated with the attention process.
Exergy and exergoeconomic analysis of a municipal waste-to-energy steam reheat power plant for Port Harcourt city

ABSTRACT This paper presents the exergy and exergoeconomic analysis of a proposed steam reheat power plant powered by the municipal waste of Port Harcourt city in Nigeria, latitude 4°45′ N and longitude 7°00′ E. The projected municipal waste generation of the city for the year 2020 was employed for this work to ascertain the amount of power obtainable via a waste incineration plant, taking into consideration the cost effectiveness of the plant. The thermodynamic analysis of the plant showed that 117 MW could be generated from the projected waste, with plant's first and second law efficiencies of 36.91% and 31.36%, respectively. The plant equipment cost was calculated to be US$326,460,000 with a payback period of about six years. The exergy and exergoeconomic analysis was used to estimate the plant’s unit cost of electricity and evaluate the exergy destruction and cost rates, with respect to the prevalent ambient temperature.
Naïve Bayesian classification of cumulative oil production from stochastic amplitude variation with angle inversion attributes: As applied to SEAM and a West Africa Field

Cumulative oil predictions are made from stochastically inverted earth attributes. The inverted attributes are from the SEAM Life of Field model and an offshore field in West Africa. To perform the prediction, we use a naïve Bayesian classifier for its transparency in methodology, computational efficiency and flexibility. The inverted properties we use for classification consist of porosity, P‐S wave velocity ratio (Vp/Vs), acoustic impedance and density which are extracted from within a set radius around the well‐path. The production from the wells in both SEAM and the West African field serve as the labels for the naïve Bayesian classifier, which are ultimately placed into two classes: the ‘high’ and ‘low’ producers. For calculating the accuracy of the classifier, we perform full cross‐validation with a set number of training wells. The cross‐validation accuracy is 78.0% and 83.3% for the SEAM model and the West African Field respectively. The classifier's sensitivity to the chosen radius around the wells and its inherent independence assumption are investigated and shown to change the bulk accuracy by less than 3.0%. We implement full reservoir classifications on SEAM to highlight potential drilling locations and show the capability of the classifier. Overall, we demonstrate how the naïve Bayesian classifier can efficiently and transparently synthesize the entire distribution of stochastic inversions of seismic data to predict oil production.
Improvement of false alarm probability in simple weighted diversity combining for cyclostationarity detection based spectrum sensing

This letter presents an improvement technique for a false alarm probability in simple weighted diversity combining for cyclostationarity detection based spectrum sensing. Although traditional simple weighting techniques can improve a performance of the spectrum sensing, the technique causes an error of false alarm probability between an obtained and designed value. The reason is that the technique requires two types of statistics and both probability density function (PDF) are different. The presented weighting technique makes these PDF matches and the degradation of the signal detection performance for the presented can be ignored. Simple theoretical analysis and numerical examples are shown to validate the effectiveness of the presented.
RNA-Protein Interactome at the Hepatitis E Virus Internal Ribosome Entry Site

Protein synthesis is a fundamental process for survival and proliferation of all living organisms. The majority of cellular proteins are produced through cap-dependent translation. ABSTRACT Multiple processes exist in a cell to ensure continuous production of essential proteins either through cap-dependent or cap-independent translation processes. Viruses depend on the host translation machinery for viral protein synthesis. Therefore, viruses have evolved clever strategies to use the host translation machinery. Earlier studies have shown that genotype 1 hepatitis E virus (g1-HEV) uses both cap-dependent and cap-independent translation machineries for its translation and proliferation. Cap-independent translation in g1-HEV is driven by an 87-nucleotide-long RNA element that acts as a noncanonical, internal ribosome entry site-like (IRESl) element. Here, we have identified the RNA-protein interactome of the HEV IRESl element and characterized the functional significance of some of its components. Our study identifies the association of HEV IRESl with several host ribosomal proteins, demonstrates indispensable roles of ribosomal protein RPL5 and DHX9 (RNA helicase A) in mediating HEV IRESl activity, and establishes the latter as a bona fide internal translation initiation site. IMPORTANCE Protein synthesis is a fundamental process for survival and proliferation of all living organisms. The majority of cellular proteins are produced through cap-dependent translation. Cells also use a variety of cap-independent translation processes to synthesize essential proteins during stress. Viruses depend on the host cell translation machinery to synthesize their own proteins. Hepatitis E virus (HEV) is a major cause of hepatitis worldwide and has a capped positive-strand RNA genome. Viral nonstructural and structural proteins are synthesized through a cap-dependent translation process. An earlier study from our laboratory reported the presence of a fourth open reading frame (ORF) in genotype 1 HEV, which produces the ORF4 protein using a cap-independent internal ribosome entry site-like (IRESl) element. In the current study, we identified the host proteins that associate with the HEV-IRESl RNA and generated the RNA-protein interactome. Through a variety of experimental approaches, our data prove that HEV-IRESl is a bona fide internal translation initiation site.
Single-cell colocalization analysis using a deep generative model

Analyzing colocalization of single cells with heterogeneous molecular phenotypes is essential for understanding cell-cell interactions, cellular responses to external stimuli, and their biological functions in diseases and tissues. However, high-throughput methods for identifying spatial proximity at single-cell resolution are practically unavailable. Here, we introduce DeepCOLOR, a computational framework based on a deep generative model that recovers inter-cellular colocalization networks with single cell resolution by the integration of single cell and spatial transcriptomes. It segregates cell populations defined by the colocalization relationships and predicts cell-cell interactions between colocalized single cells. DeepCOLOR could identify plausible cell-cell interaction candidates in mouse brain tissues, human squamous cell carcinoma samples, and human lung tissues infected with SARS-CoV-2 by reconstructing spatial colocalization maps at single-cell resolution. DeepCOLOR is typically applicable to studying cell-cell interactions in any spatial niche. Our newly developed computational framework could help uncover molecular pathways across single cells connected with colocalization networks.
Data Driven Control of Vagus Nerve Stimulation for the Cardiac System

Vagus nerve stimulation is an emerging therapy that seeks to offset pathological conditions by electrically stimulating the vagus nerve through cuff electrodes, where an electrical pulse is defined by several parameters such as pulse amplitude, pulse width, and pulse frequency. This electroceutical therapy has been approved for epilepsy, and treatment resistant depression. Currently, vagus nerve stimulation is under investigation for the treatment of heart failure, heart arrhythmia, hypertension, and gastric motility disorders. Through several clinical trials that sought to assess vagus nerve stimulation for the treatment of heart failure, stimulation parameters were determined heuristically and the results were left inconclusive, which has led to the suggestion of using a closed-loop approach to optimize the stimulation parameters. A recent investigation has demonstrated highly specific control of cardiac physiology by selectively activating different fibers in the vagus nerve. When multiple locations and multiple stimulation parameters are considered for optimization, the design of closed-loop control becomes considerably more challenging. To address this challenge, we investigated a data-driven control scheme for both modeling and controlling the rat cardiac system. Using an existing in silico physiological model of a rat heart to generate synthetic input-output data, we trained a long short-term memory network (LSTM) to map the effect of stimulation on the heart rate and the blood pressure. The trained LSTM was utilized in a model predictive control framework to optimize the vagus nerve stimulation parameters for set point tracking of the heart rate and the blood pressure in closed-loop simulations. Additionally, we altered the underlying in silico physiological model to consider intra-patient variability, and diseased dynamics from increased sympathetic tone in designing closed-loop VNS strategies. Throughout the different simulation scenarios, we leveraged the design of the controller to demonstrate alternative clinical objectives. Our results show the controller can optimize stimulation parameters to achieve set-point tracking with nominal offset while remaining computationally efficient. Furthermore, we show a controller formulation that compensates for mismatch due to intra-patient variabilty, and diseased dynamics. This study demonstrates the first application and a proof-of-concept for using a purely data-driven approach for the optimization of vagus nerve stimulation parameters in closed-loop control of the cardiac system.
Salt stress alters the spectrum of de novo mutation available to selection during experimental adaptation of Chlamydomonas reinhardtii

The genetic basis of adaptation is driven not only by selection, but also by the spectrum of available mutations. Given that the rate of mutation is not uniformly distributed across the genome and varies depending on the environment, understanding the signatures of selection across the genome is aided by first establishing what the expectations of genetic change are from mutation. To determine the interaction between salt stress, selection, and mutation across the genome, we compared the rates and patterns of mutation observed in a selection experiment for salt tolerance in Chlamydomonas reinhardtii to those observed in mutation accumulation experiments with and without salt exposure. We found that salt stress led to an increased rate of indel mutations, but that many of these mutations were removed under selection. Finally, lines adapted to salt also showed excess clustering of mutations in the genome and the co-expression network, suggesting a role for positive selection in retaining mutations in particular compartments of the genome during the evolution of salt tolerance. Our study shows that characterizing mutation rates and spectra expected under stress helps disentangle the effects of environment and selection during adaptation.
Dispersal and Plant Arrangement Condition the Timing and Magnitude of Coffee Rust Infection

One central issue in coffee-leaf rust (Hemileia vastatrix) epidemiology is to understand what determines the intensity and the timing of yearly infections in coffee plantations. However, most experimental and theoretical studies report infection as an average at the plot level, obscuring the role of potentially key factors like rust dispersal or the planting pattern. Here, we first review the rust epidemic patterns of different sites, which reveal large variability in the duration and magnitude of the different epidemiologic phases. We then present a spatially explicit and parametrised model, where the host population is subdivided into discrete patches linked through spore dispersal, modelled as simple diffusion. With this model, we study the role of the planting arrangement, the dispersal intensity and plant-level variables on the maximum average tree infection (MATI) and its timing. Our results suggest that the epidemic timeline can be divided into two phases: a time lag and a growth phase per se. The model shows that the combination of the dispersal magnitude and plant aggregation modifies the MATI and the time to MATI, mainly by preventing some plants from reaching their maximum peak during the epidemic. It also affects the epidemic curves, which can have a stepped, or a rather smooth pattern in plots with otherwise similar conditions. The initial rust infection modulates the time lag before the epidemic and the infected leaf-fall rate drastically changes the MATI. These findings highlight the importance of explicitly considering the spatial aspects of coffee agroecosystems when measuring and managing rust infection, and help us to further understand the spatio-temporal dynamics of ecological systems in general.
Positive fitness effects help explain the broad range of Wolbachia prevalences in natural populations

The bacterial endosymbiont Wolbachia is best known for its ability to modify its host’s reproduction by inducing cytoplasmic incompatibility (CI) to facilitate its own spread. Classical models predict either near-fixation of costly Wolbachia once the symbiont has overcome a threshold frequency (invasion barrier), or Wolbachia extinction if the barrier is not overcome. However, natural populations do not all follow this pattern: Wolbachia can also be found at low frequencies (below one half) that appear stable over time. Wolbachia is known to have pleiotropic fitness effects (beyond CI) on its hosts. Existing models typically focus on the possibility that these are negative. Here we consider the possibility that the symbiont provides direct benefits to infected females (e.g. resistance to pathogens) in addition to CI. We discuss an underappreciated feature of Wolbachia dynamics: that CI with additional fitness benefits can produce low-frequency (< 1/2) stable equilibria. Additionally, without a direct positive fitness effect, any stable equilibrium close to one half will be sensitive to perturbations, which make such equilibria unlikely to sustain in nature. The results hold for both diplodiploid and different haplodiploid versions of CI. We suggest that insect populations showing low-frequency Wolbachia infection might host CI-inducing symbiotic strains providing additional (hidden or known) benefits to their hosts, especially when classical explanations (ongoing invasion, source-sink dynamics) have been ruled out.