Miracle-dz/UniFAR-FTUs huggingface dataset:
Architectural distortion detection based on superior-inferior directional context and anatomic prior knowledge in digital breast tomosynthesis.

BACKGROUND In 2020, breast cancer becomes the most leading diagnosed cancer all over the world. The burden is increasing in the prevention and treatment of breast cancer. Accurately detecting breast lesions in screening images is important for early detection of cancer. Architectural distortion (AD) is one of the breast lesions that need to be detected. PURPOSE To develop a deep-learning-based computer-aided detection (CADe) model for AD in digital breast tomosynthesis (DBT). This model uses the superior-inferior directional context of DBT and anatomic prior knowledge to reduce false positive (FP). It can identify some negative samples that cannot be distinguished by deep learning features. METHODS The proposed CADe model consists of three steps. In the first step, a deep learning detection network detects two-dimensional (2D) candidates of ADs in DBT slices with the inputs preprocessed by Gabor filters and convergence measure. In the second step, three-dimensional (3D) candidates are obtained by stacking 2D candidates along superior-inferior direction. In the last step, FP reduction for 3D candidates is implemented based on superior-inferior directional context and anatomic prior knowledge of breast. DBT data from 99 cases with AD were used as the training set to train the CADe model, and data from 208 cases were used as an independent test set (including 108 cases with AD and 100 cases without AD as the control group). The free-response receiver operating characteristic and mean true positive fraction (MTPF) in the range of 0.05-2.0 FPs per volume are used to evaluate the model. RESULTS Compared with the baseline model based on convergence measure, our proposed method demonstrates significant improvement (MTPF: 0.2826 ± 0.0321vs. 0.6640 ± 0.0399). Results of an ablation study show that our proposed context-based and anatomy-based FP reduction methods improve the detection performance. The number of FPs per DBT volume reduces from 2.47 to 1.66 at 80% sensitivity after employing these two schemes. CONCLUSIONS The deep learning model demonstrates practical value for AD detection. The results indicate that introducing superior-inferior directional context and anatomic prior knowledge into model can indeed reduce FPs and improve the performance of CADe model. This article is protected by copyright. All rights reserved.
Switching behavior and current handling performance of MCT:IGBT cell ensembles

The dynamic characteristics of large MCT (MOS controlled thyristor) ensembles have been improved by integrating IGBT (insulated-gated bipolar transistor) cells for turn-on and different schemes of cathode and anode shorting for turn-off. Such MCT-IGBT ensembles with integrated shorts are able to turn off 4 A (240 A-cm/sup -2/) at 1000 V in typically 1.5 mu s in the inductive clamped mode. The authors report on the effectiveness of these different approaches and their parasitic influence on the turn-on characteristic and the maximum turn-off current capability. The implementation of permanent cathode shorts has lead to an increase of the maximum turn-off current of large MCT ensembles, whereas anode shorting leads to faster turn-off and lower tail currents. A careful design of the IGBT turn-on cell and placement is required for proper MOS turn-on even at low anode voltages. <<ETX>>
A Conceptual Framework of Business Model Emerging Resilience

In this paper we introduce an environmentally driven conceptual framework of Business Model change. Business models acquired substantial momentum in academic literature during the past decade. Several studies focused on what exactly constitutes a Business Model (role model, recipe, architecture etc.) triggering a theoretical debate about the Business Model’s components and their corresponding dynamics and relationships. In this paper, we argue that for Business Models as cognitive structures, are highly influenced in terms of relevance by the context of application, which consequently enriches its functionality. As a result, the Business Model can be used either as a role model (benchmarking) or a recipe (strategy). For that purpose, we assume that the Business Model is embedded within the economic (task) environment, and consequently affected by it. Through a typology of the environmental impact on the Business Model productivity, we introduce a conceptual framework that aims to capture the salient features of Business Model emergent resilience as reaction to two types impact: productivity constraining and disturbing.
Towards optimizing enhanced surfaces for passive immersion cooled heat sinks

Reduction in die feature-size due to improvements in microelectronics fabrication technology have increased the demands on effective thermal management. As a consequence, innovative heat removal schemes must be explored. Immersion cooling in a dielectric fluid, despite the increased complications, continues to be studied as a possible solution. The characteristics of a surface used as a pool boiling heat sink depend on its microscopic features. This paper reports results from an investigation in which the shape and spacing of these microscopic features were studied in order to achieve improved thermal performance. The heat sources were thin-film heaters deposited in an array on a silicon wafer which was immersed in FC-72 coolant.
Business platforms: A model for the 21st century a sustainability long-term strategy for a hotel in Mexico

This work is the compilation of 8 weekly class deliverables for Business Platforms - A Model for the 21st Century lectured at Regis University in Colorado. This work complemented learning for the simulation in capstone. In the simulator we were separated in 3 teams and we took business decisions regarding innovation, product design, sales, marketing, human resources, production and operations, installed capacity and finance. In the case of these weekly deliverables I developed the subjects using a plan for the creation of a Hotel in Ensenada Baja California in Mexico.
Pressure Recovery Effect on Triple Spool Turbofan Engine Performance

Present and future studies in jet engine design appropriate for wide-body in civil aircrafts (such as the Airbus 380 and Boeing Dream liner 787) is high bypass turbofan (HBPT) engines. Turbofan engines which are categorized of more than ten types dominated in both civil and military aircrafts. This work is devoted to performance analysis of an unmixed triple spool HBPR Turbofan engine; namely, Trent 700. Two parameters are examined here; namely, pressure recovery factor and percentage of air bleed from high pressure compressor. The first parameter is directly influenced by the flight path of aircraft during takeoff and climb. In such flight phases both aircraft wing and engine are susceptible to high angle of attack and possible air separation. Pressure recovery attains a minimum value at takeoff and a maximum value at cruise phase. Pressure recovery varies from 0.92 to 0.99. Such reasonable variation has greatly influence thermodynamic characteristics of engine cycle. Moreover, it influences its thermal, propulsive and overall efficiencies together with its specific thrust. Both takeoff and cruise conditions (flight Mach number of 0.82 and 12km altitude) are examined. Moreover, bleed air of 8 % from second compressor to cool first (HP) and second (IP) turbines of Trent enabled engine designers to achieve a maximum temperature of 1543 Kelvin. The effect of the Mach number variation (from 0 to 1) on the performance of Trent 700 is examined in details using computer program matlab-7 to simulate and analyze thermal cycle of the engine. The results showed specific thrust increase by 5%, thrust specific fuel consumption decrease by 4%, thermal efficiency increase, propulsive efficiency increase and overall efficiency increase. It was studied effect of angle of attack (40, 30, 20, 10, and 0) degree and altitude of aircraft on pressure recovery factor value. Using computational fluid dynamics software FLUENT ® and choosing The Spalart-Allmaras turbulence model to simulate turbofan intake.
Experimental Evaluation of Indoor Navigation Devices

Augmented reality (AR) interfaces for indoor navigation on handheld mobile devices seem to greatly enhance directional assistance and user engagement, but it is sometimes challenging for users to hold the device at specific position and orientation during navigation. Previous studies have not adequately explored wearable devices in this context. In the current study, we developed a prototype AR indoor navigation application in order to evaluate and compare handheld devices and wearable devices such as Google Glass, in terms of performance, workload, and perceived usability. The results showed that although the wearable device was perceived to have better accuracy, its overall navigation performance and workload were still similar to a handheld device. We also found that digital navigation aids were better than paper maps in terms of shorter task completion time and lower workload, but digital navigation aids also resulted in worse route/map retention.
TCD hemodynamics findings in the subacute phase of anterior circulation stroke patients treated with mechanical thrombectomy

Abstract Despite mechanical thrombectomy, the prognosis for many patients with anterior circulation ischemic stroke (ACIS) remains poor. This retrospective study reports consecutive mechanical thrombectomy procedures for ACIS at our hospital over 4 years. Hemodynamics were explored using transcranial Doppler ultrasound. The functional outcome was assessed using the modified Rankin scale. A total of 121 eligible cases were included: 61 (50.4%) exhibited good outcomes (modified Rankin scale score ≤2) by day 90. The logistic regression analysis showed that ipsilateral middle cerebral artery (iMCA) systolic blood flow (SBF) (OR = 0.983, 95% CI: 0.969–0.997, P = 0.014), preoperative National Institutes of Health Stroke Scale (NIHSS)score (OR = 1.160, 95% CI: 1.067–1.261, P < 0.001), intracranial hemorrhage after therapy (OR = 19.514, 95% CI: 4.364–87.265, P < 0.001), and Alberta Stroke Program Early Computed Tomography Score (OR = 0.639, 95% CI: 0.416–0.981, P = 0.040) were independently associated with prognosis. The iMCA SBF and preoperative NIHSS score were significantly predictive of a good outcome in the receiver operating characteristic analysis. In conclusion, elevated iMCA SBF might be a prognostic indicator of a good 90-day outcome following endovascular treatment in ACIS patients treated with mechanical thrombectomy, but large prospective studies are mandatory to validate the findings of our study.
The Companion Model -- a Canonical Model in Graph Signal Processing

—This paper introduces a canonical graph signal model deﬁned by a canonical graph and a canonical shift, the companion graph and the companion shift. These are canonical because, under standard conditions, we show that any graph signal processing (GSP) model can be transformed into the canonical model. The transform that obtains this is the graph 𝑧 -transform (G 𝑧 T) that we introduce. The GSP canonical model comes closest to the discrete signal processing (DSP) time signal models: the structure of the companion shift decomposes into a line shift and a signal continuation just like the DSP shift and the GSP canonical graph is a directed line graph with a terminal condition reﬂecting the signal continuation condition. We further show that, surprisingly, in the canonical model, convolution of graph signals is fast convolution by the DSP FFT.
Which nuclear shape generates the strongest attraction on a relativistic electron? An open problem in relativistic quantum mechanics

In this article we formulate several conjectures concerning the lowest eigenvalue of a Dirac operator with an external electrostatic potential. The latter describes a relativistic quantum electron moving in the field of some (pointwise or extended) nuclei. The main question we ask is whether the eigenvalue is minimal when the nuclear charge is concentrated at one single point. This well-known property in nonrelativistic quantum mechanics has escaped all attempts of proof in the relativistic case.
HetSched: Quality-of-Mission Aware Scheduling for Autonomous Vehicle SoCs

—Systems-on-Chips (SoCs) that power autonomous vehicles (AVs) must meet stringent performance and safety requirements prior to deployment. With increasing complexity in AV applications, the system needs to meet stringent real-time demands of multiple safety-critical applications simultaneously. A typical AV-SoC is a heterogeneous multiprocessor consisting of accelerators supported by general-purpose cores. Such heterogeneity, while needed for power-performance efﬁciency, complicates the art of task (process) scheduling. In this paper, we demonstrate that hardware heterogeneity impacts the scheduler’s effectiveness and that optimizing for only the real-time aspect of applications is not sufﬁcient in AVs. Therefore, a more holistic approach is required — one that considers global Quality-of-Mission (QoM) metrics, as deﬁned in the paper. We then propose HetSched, a multi-step scheduler that leverages dynamic runtime information about the underlying heterogeneous hardware platform, along with the applications’ real-time constraints and the task trafﬁc in the system to optimize overall mission performance. HetSched proposes two scheduling policies: MS stat and MS dyn and scheduling optimizations like task pruning, hybrid heterogeneous ranking and rank update. HetSched improves overall mission performance on average by 4.6 × , 2.6 × and 2.6 × when compared against CPATH, ADS and 2lvl-EDF (state-of-the-art real-time schedulers built for heterogeneous systems), respectively, and achieves an average of 53.3% higher hardware utilization, while meeting 100% critical deadlines for real-world applications of autonomous driving and aerial vehicles. Furthermore, when used as part of an SoC design space exploration loop, in comparison to the prior schedulers, HetSched reduces the number of processing elements required by an SoC to safely complete AV’s missions by 35% on average while achieving 2.7 × lower energy-mission time product.
Effective and Efficient Core Decomposition in Signed Networks

With the proliferation of mobile technology and IT development, people can use social network services at any place and anytime. Among many social network mining problems, identifying cohesive subgraphs attract many attentions from different fields due to its numerous applications. Among many cohesive subgraph models, 𝑘 -core is the most widely used model due to its simple and intuitive structure. In this paper, we formulate ( 𝑝,𝑛 ) -core in signed networks by extending 𝑘 -core. ( 𝑝,𝑛 ) -core simultaneously guarantees the sufficient internal positive edges and deficient internal negative edges. We formally prove that finding an exact ( 𝑝,𝑛 ) -core is NP-hard. Hence, we propose three efficient and effective algorithms to find a solution. Using real-world and synthetic networks, we demonstrate the superiority of our proposed algorithms
Code Smells for Machine Learning Applications

The popularity of machine learning has wildly expanded in recent years. Machine learning techniques have been heatedly studied in academia and applied in the industry to create business value. However, there is a lack of guidelines for code quality in machine learning applications. In particular, code smells have rarely been studied in this domain. Although machine learning code is usually integrated as a small part of an overarching system, it usually plays an important role in its core functionality. Hence ensuring code quality is quintessential to avoid issues in the long run. This paper proposes and identifies a list of 22 machine learning-specific code smells collected from various sources, including papers, grey literature, GitHub commits, and Stack Overflow posts. We pinpoint each smell with a description of its context, potential issues in the long run, and proposed solutions. In addition, we link them to their respective pipeline stage and the evidence from both academic and grey literature. The code smell catalog helps data scientists and developers produce and maintain high-quality machine learning application code. ACM Reference Format: Haiyin Zhang, Luís Cruz, and Arie van Deursen. 2022. Code Smells for Machine Learning Applications. In 1st Conference on AI Engineering - Software Engineering for AI (CAIN’22), May 16-24, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3522664.3528620
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos

Formulating learning systems for the detection of real-world anomalous events using only video-level labels is a challenging task mainly due to the presence of noisy labels as well as the rare occurrence of anomalous events in the training data. We propose a weakly supervised anomaly detection system that has multiple contributions including a random batch selection mechanism to reduce interbatch correlation and a normalcy suppression block (NSB) which learns to minimize anomaly scores over normal regions of a video by utilizing the overall information available in a training batch. In addition, a clustering loss block (CLB) is proposed to mitigate the label noise and to improve the representation learning for the anomalous and normal regions. This block encourages the backbone network to produce two distinct feature clusters representing normal and anomalous events. An extensive analysis of the proposed approach is provided using three popular anomaly detection datasets including UCF-Crime, ShanghaiTech, and UCSD Ped2. The experiments demonstrate the superior anomaly detection capability of our approach.
Effects of Air and High-Speed Rail Transport Integration on Profits and Welfare: The Case of Air-Rail Connecting Time

Air-rail integration has become a popular idea to relieve airport congestion and environmental impact of transport industry, especially amid the fast expansion of high-speed rail network around the world. This study examines the circumstances under which air-rail integration can be better justified, by focusing on the effects of reducing air-rail connecting time on transport operators’ profits, consumer surplus, and social welfare. We show that while consumers always benefit from less air-rail connecting time (an integrated hub with seamless transfer between air and rail services is always preferred by passengers), operators of the two modes, air transport and high-speed rail, won’t have an incentive to integrate unless the cost of integration is sufficiently low. Nonetheless, reducing air-rail connecting time enhances total surplus when the hub airport suffers from a certain degree of capacity constraint and the cost of air-rail integration is not too high.
Frequency response of diffusion-based molecular communication channels in bounded environment

Recently, molecular communication (MC) has been studied as a micro-scale communication between cells or molecular robots. In previous works, the MC channels in unbounded environment was analyzed. However, many of the experimentally implemented MC channels are surrounded by walls, thus the boundary condition should be explicitly considered to analyze the dynamics of MC channels. In this paper, we propose a framework to analyze the frequency response of one-dimensional MC channels based on a diffusion equation with a boundary. In particular, we decompose the MC channel into the diffusion system and the boundary system, and show the relation between the cut-off frequency of the MC channel and the communication distance based on the transfer function. We then analyze the frequency response of a specific MC channel and reveal that the boundary can restrict the communication bandwidth of the MC channel.
Probing pre-BBN era with scale invariant FIMP

Detecting dark matter (DM) relic via freeze-in is difficult in laboratories due to smallness of the couplings involved. However, a non-standard cosmological history of the Universe, prior to Big Bang Nucleosynthesis (BBN), can dramatically change this scenario. In this context, we study the freeze-in production of dark matter (DM) in classically scale invariant U(1) X gauge extension of the Standard Model (SM), recently dubbed as the Scale Invariant FIMP Miracle. We assume an additional species dominates the energy density of the Universe at early times, causing the expansion rate at a given temperature to be larger than that in the standard radiation-dominated case. We find, the out-of-equilibrium scattering processes involving particles in the thermal bath lead to significantly suppressed DM production in this era, thereby enhancing the couplings between the visible and the dark sector (by several orders of magnitude) to satisfy the observed DM abundance, and improving the detection prospects for freeze-in in turn. Scale invariance of the underlying theory leaves only four free parameters in the model: the DM mass mX , the gauge coupling gX , the temperature of transition TR from early scalar-dominated to radiation-dominated era and the power-law dependence n of this temperature. We show, within this minimal set-up, experiments like FASER, MATHUSLA, DUNE, SHiP will be probing various cosmological models depending on the choice of {n, TR } that also satisfy the PLANCK observed relic density bound. Moreover, due to the presence of a naturally light scalar mediator, the direct detection of the DM at XENON1T, PandaX-4T or XENONnT becomes relevant for Higgs-scalar mixing sinθ ≃ {10-5–10-3}, thus providing complementary probes for freeze-in, as well as for non-standard cosmological pre-BBN era.
Analysis of the Production Strategy of Mask Types in the COVID-19 Environment

Since the outbreak of the COVID-19 in December 2019, medical protective equipment such as disposable medical masks and KN95 masks have become essential resources for the public. Enterprises in all sectors of society have also transformed the production of medical masks. After the outbreak, how to choose the right time to produce medical protective masks, and what type of medical masks to produce will play a positive role in preventing and controlling the epidemic in a short time. In this regard, the evolutionary game competition analysis will be conducted through the relevant data of disposable medical masks and KN95 masks to determine the appropriate nodes for the production of corresponding mask types. After the research and analysis of the production strategy of mask types, it has a positive effect on how to guide the resumption of work and production.
Analysis of the use of color and its emotional relationship in visual creations based on experiences during the context of the COVID-19 pandemic

Color is a complex communicative element. At the level of artistic creation, this component influences both formal aspects and symbolic weight, directly affecting the construction of the message, and its associated emotion. During the COVID-19 pandemic, people generated countless images transmitting the subjective experiences of this event, and the social network Instagram was used to share this visual material. Using the repository of images created in the Instagram account CAM (The COVID Art Museum), we propose a methodology to understand the use of color and its emotional relationship in this context. The proposed methodology consists of creating a model that learns to recognize emotions via a convolutional neural network using the ArtEmis database. This model will subsequently be applied to recognize emotions in the CAM dataset, also extracting color attributes and their harmonies. Once both processes are completed, we combine the results, generating an expanded discussion on the usage of color and emotion. The results indicate that warm colors and analog compositions prevail in the sample. The relationship between emotions and composition shows a trend in positive emotions, reinforced by the results of the emotional relationship analysis of color attributes (hue, saturation, and lighting).