Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlative techniques for cross-modality medical image registration

Correlation provides rapid, accurate, objective image registration, but is limited to applications where the data sets are similar. Our goal was to develop preliminary image processing techniques which will allow correlative registration of (dissimilar) brain images obtained using different imaging modalities. A simulation showed that correlative registration is effective for trend-similar images, leading to the hypothesis that preprocessing algorithms need only impose trend similarity to permit successful cross-modality correlative registration. This hypothesis was tested using MR and CT image which were acquired with external fiducial markers to test the registration accuracy. After analyzing the salient features of each image set, trend similarity was imposed using simple masking and gray scale adjustment techniques. A correlative registration program was then applied to the trend-similar image sets, and the resulting rigid body transformation parameters were applied to the original images. The registered image sets matched the fiducial marker locations to within one pixel, accuracy is similar to that obtained by matching user-identified anatomical points, leading to the conclusion that simple pre-processing techniques can allow correlative registration to be used for aligning images from multiple imaging modalities.
An improving agent-based engineering strategy for minimizing unproductive situations of cranes in a rail–rail transshipment yard

Nowadays, seaports seek to achieve a better massification (massive transportation of containers) share of their hinterland transport by promoting rail and river connections in order to more rapidly evacuate increasing container traffic shipped by sea and to avoid landside congestion. The attractiveness of a seaport to shipping enterprises depends not only on its reliability and nautical qualities but also on its massified hinterland connection capacity. Contrary to what has been observed in Europe, the massification share of Le Havre seaport has stagnated in recent years. To overcome this situation, Le Havre Port Authority is putting into service a multimodal hub terminal linked only with massified modes. In this study, we focus on rail–rail transshipment of this new terminal, specifically on minimizing unproductive situations of cranes to improve crane productivity and to speed up freight train processing. To this end, an improving agent-based engineering strategy called the “crane anti-collision strategy” is proposed and tested using multi-method simulation software (Anylogic). In a numerical study, the simulation results reveal that our developed model is very satisfactory and outperforms other existing simulation models.
People-Centric Cognitive Internet of Things for the Quantitative Analysis of Environmental Exposure

Exposure to air pollution poses a significant risk to human health, particularly to urban dwellers. When correlated with individual health outcomes, high resolution information on human mobility, and the spatial and temporal distribution of the pollutants can lead to a better understanding of the effects of pollution exposure. People-centric sensing is normally carried out by data sharing through a central cloud server. This system architecture is not designed to serve the ever-growing number of high fidelity connected devices, particularly when crowdsourcing urban data on location and environmental conditions. Here, we outline an architecture for a people-centric and cognitive Internet of Things (PIoT) environmental sensing platform, which involves closed loops of interactions among people nodes and physical devices as well as servers and recommendations on device connections by cognitive computing. Taking advantage of smart objects and virtual node technology in PIoT, an algorithm to aggregate on-demand user data from smart devices is proposed. A PIoT prototype sensing system is designed and deployed to measure the space-time distribution of particulate matter in air (PM2.5), and mobility counts, for quantifying personal exposure to air pollution. A case study of particulate matter PM2.5 exposure in New York City is presented to illustrate the potential application of people-centric measurement system and data analysis.
Secure Management of Low Power Fitness Trackers

The increasing popular interest in personal telemetry, also called the Quantified Self or “lifelogging”, has induced a popularity surge for wearable personal fitness trackers. Fitness trackers automatically collect sensor data about the user throughout the day, and integrate it into social network accounts. Solution providers have to strike a balance between many constraints, leading to a design process that often puts security in the back seat. Case in point, we reverse engineered and identified security vulnerabilities in Fitbit Ultra and Gammon Forerunner 610, two popular and representative fitness tracker products. We introduce FitBite and GarMax, tools to launch efficient attacks against Fitbit and Garmin. We devise SensCrypt, a protocol for secure data storage and communication, for use by makers of affordable and lightweight personal trackers. SensCrypt thwarts not only the attacks we introduced, but also defends against powerful JTAG Read attacks. We have built Sens.io, an Arduino Uno based tracker platform, of similar capabilities but at a fraction of the cost of current solutions. On Sens.io, SensCrypt imposes a negligible write overhead and significantly reduces the end-to-end sync overhead of Fitbit and Garmin.
Integrity for an Event Notification Within the Industrial Internet of Things by Using Group Signatures

In the last years, several academic research efforts have focused on security requirements, threat models, and attack taxonomies concerning the application of the Internet of Things (IoT) in critical systems. Since such systems are strongly data intensive, it is of pivotal importance to provide integrity for the messages moving throughout the IoT infrastructure by means of publish/subscribe services. Integrity provisioning in industrial IoT scenarios has received marginal attention with respect to other primary security features. The existing solutions are lacking the needed focus on the peculiarities of the event notification and on the demand introduced by resource-constrained devices. This work contributes by applying group signatures so as to avoid managing certificates, violating the spatial decoupling, or implying an excessive resource usage. A proof-of-concept prototype of the proposed solution has been realized for platforms based on TinyOS, and simulations with TOSSIM have been conducted in order to empirically assess its performance and effectiveness.
A 28GHz Packaged Chireix Transmitter with Direct on-Antenna Outphasing Load Modulation Achieving 56%/38% PA Efficiency at Peak/6dB Back-Off Output Power

This paper presents a packaged 28GHz Chireix outphasing transmitter (TX) for power amplifier (PA) efficiency enhancement at peak and back-off output power (PBO). It utilizes a high-efficiency dual-feed loop antenna (DLA)-based non-isolating power combiner to realize direct on-antenna power combining and more importantly, outphasing active load modulation. The proposed Chireix TX contains two phase-shifted sub-TXs simultaneously driving a DLA on package. The TX chips are implemented in a 45nm CMOS SOI process and integrated with the DLA by flip-chip packaging. The measurements demonstrate a 56% PA drain efficiency (ηD) at saturated power (Psat), with 38% ηDat 6dB PBO, achieving 1.36× efficiency enhancement over ideal Class-B operation. The high efficiency of this Chireix TX enables up to 34%/10.5dBm PA average efficiency/output power (ηave/Pave) while amplifying 15Gb/s 64-QAM signal at 28GHz with EVM/ACLR better than −27.5dBc/-28dBc in a wireless radiation testing environment.
Recklessly Approximate Sparse Coding

It has recently been observed that certain extremely simple feature encoding techniques are able to achieve state of the art performance on several standard image classification benchmarks including deep belief networks, convolutional nets, factored RBMs, mcRBMs, convolutional RBMs, sparse autoencoders and several others. Moreover, these "triangle" or "soft threshold" encodings are ex- tremely efficient to compute. Several intuitive arguments have been put forward to explain this remarkable performance, yet no mathematical justification has been offered. The main result of this report is to show that these features are realized as an approximate solution to the a non-negative sparse coding problem. Using this connection we describe several variants of the soft threshold features and demonstrate their effectiveness on two image classification benchmark tasks.
WallText: augmented reality messaging platforms

WallText is the generalized working implementation of a new form of conversation-based interactive enabled by computer vision augmented reality (AR). The author has built AR apps to illustrate its different forms, spanning text rendered as graffti-art to realtime localized annotation-chats to 3D photogrammetry captured models. We discuss the apps in their progression in independent software development and arrive at several AR app development insights.
Toward uBPMN-Based Patterns for Modeling Ubiquitous Business Processes

Ubiquitous business processes are the new generation of processes that pervade the physical space and interact with their environments using a minimum of human involvement. Although they are now widely deployed in the industry, their deployment is still ad hoc. They are implemented after an arbitrary modeling phase or no modeling phase at all. The absence of a solid modeling phase backing up the implementation generates many loopholes that are stressed in the literature. Here, we tackle the issue of modeling ubiquitous business processes. We propose patterns to represent the recent ubiquitous computing features. These patterns are the outcome of an analysis we conducted in the field of human–computer interaction to examine how the features are actually deployed. The patterns’ understandability, ease-of-use, usefulness, and completeness are examined via a user experiment. The results indicate that these four indexes are on the positive track. Hence, the patterns may be the backbone of ubiquitous business process modeling in industrial applications.
Binocular Vision-Based Autonomous Path Planning for UAVs in Unknown Outdoor Scenes

It is a classic task for Unmanned Aerial Vehicles (UAVs) to accomplish autonomous scene perception and path planning in unknown 3-D outdoor environments. This paper investigates the problems of obstacle avoidance and path planning using binocular vision system. During the UAV's flight, the binocular vision sensor is used to obtain the local environment information in real time, and the distribution of obstacles in the environment can also be analyzed with the depth images acquired by the binocular vision. Inspired by the idea of dynamic window algorithm, a 3-D path planning algorithm is proposed to convert the global path to the combination of a group of local paths by using a series of 3-D models of predefined local paths. According to the screening algorithm for the passable candidate paths, the UAV will select the optimal one to guide its flight. A series of experiments are conducted by using a quadrotor platform DJI M100 and experimental results show the validity of the proposed approach.
Nitazoxanide Inhibits Human Norovirus Replication and Synergizes with Ribavirin by Activation of Cellular Antiviral Response

Norovirus is the main cause of viral gastroenteritis worldwide. Although norovirus gastroenteritis is self-limiting in immunocompetent individuals, chronic infections with debilitating and life-threatening complications occur in immunocompromised patients. ABSTRACT Norovirus is the main cause of viral gastroenteritis worldwide. Although norovirus gastroenteritis is self-limiting in immunocompetent individuals, chronic infections with debilitating and life-threatening complications occur in immunocompromised patients. Nitazoxanide (NTZ) has been used empirically in the clinic and has demonstrated effectiveness against norovirus gastroenteritis. In this study, we aimed at uncovering the antiviral potential and mechanisms of action of NTZ and its active metabolite, tizoxanide (TIZ), using a human norovirus (HuNV) replicon. NTZ and TIZ, collectively referred to as thiazolides (TZD), potently inhibited replication of HuNV and a norovirus surrogate, feline calicivirus. Mechanistic studies revealed that TZD activated cellular antiviral response and stimulated the expression of a subset of interferon-stimulated genes (ISGs), particularly interferon regulatory factor 1 (IRF-1), not only in a Huh7 cell-based HuNV replicon, but also in naive Huh7 and Caco-2 cells and novel human intestinal organoids. Overexpression of exogenous IRF-1 inhibited HuNV replication, whereas knockdown of IRF-1 largely attenuated the antiviral activity of TZD, suggesting that IRF-1 mediated TZD inhibition of HuNV. By using a Janus kinase (JAK) inhibitor, CP-690550, and a STAT1 knockout approach, we found that TZD induced antiviral response independently of the classical JAK-signal transducers and activators of transcription (JAK-STAT) pathway. Furthermore, TZD and ribavirin synergized to inhibit HuNV replication and completely depleted the replicons from host cells after long-term treatment. In summary, our results demonstrated that TZD combated HuNV replication through activation of cellular antiviral response, in particular by inducing a prominent antiviral effector, IRF-1. NTZ monotherapy or combination with ribavirin represent promising options for treating norovirus gastroenteritis, especially in immunocompromised patients.
The use of a surface active agent in the protection of a fusion protein during bioprocessing

The bioprocessing of a fusion protein is characterised by low yields and at a series of recovery and purification stages that leads to an overall 90% loss. Much of this apparent loss is due to the denaturation of a protein, missing a vital affinity ligand. However, there is evidence of the protection of degradation products which occurs in the presence of shear plus air/liquid interfaces. This study seeks out to characterise the loss and use ultra‐scale‐down studies to predict its occurrence and hence shows these may be diminished by the use of protective reagents such as Pluronic F68.
Confidence penalty, annealing Gaussian noise and zoneout for biLSTM-CRF networks for named entity recognition

Named entity recognition (NER) is used to identify relevant entities in text. A bidirectional LSTM (long short term memory) encoder with a neural conditional random fields (CRF) decoder (biLSTM-CRF) is the state of the art methodology. In this work, we have done an analysis of several methods that intend to optimize the performance of networks based on this architecture, which in some cases encourage overfitting avoidance. These methods target exploration of parameter space, regularization of LSTMs and penalization of confident output distributions. Results show that the optimization methods improve the performance of the biLSTM-CRF NER baseline system, setting a new state of the art performance for the CoNLL-2003 Spanish set with an F1 of 87.18.
Extracellular Water to Total Body Water Ratio in Viral Liver Diseases: A Study Using Bioimpedance Analysis

Aims: To investigate the relationship between extracellular water to total body water ratio (ECW/TBW) in bioimpedance analysis (BIA) and clinical parameters in hepatitis viruses related to liver diseases. Methods: ECW/TBW was compared in patients with hepatitis B virus (HBV, n = 85) and hepatitis C virus (HCV, n = 440) related liver diseases. We also examined factors linked to mild to severe overhydrated state (ECW/TBW ≥0.4). Results: The median ECW/TBW in the HCV group was 0.388 (range, 0.365–0.433), while that in the HBV group was 0.381 (range, 0.363–0.425) (p < 0.0001). In all cases (n = 525), for predicting F3 or more, ECW/TBW yielded the area under the receiver operating characteristics (AUROC, 0.74912) and for predicting F4, ECW/TBW yielded the AUROC (0.75517). Multivariate analysis showed that age, prothrombin time, serum albumin, and alanine aminotransferase were significant factors linked to ECW/TBW ≥0.4. In patients with FIB-4 index <2, ECW/TBW in the HCV group was significantly higher than that in the HBV group (p = 0.0188), while in patients with 2 ≤ FIB-4 index <4 and FIB-4 index ≥4, the difference in the two groups did not reach significance. Conclusion: ECW/TBW can be different according to hepatitis viruses. Overhydrated status can easily occur in the HCV group even in the non-LC status compared with the HBV group.
The emergence of complex patterns in online human communication

Social media have become essential conduits in the worldwide exchange of ideas, opinions and consumer marketing. Complex networks are important tools for analyzing the information flow in many aspects of nature and human society. Here, we introduce a method based on networks and social media to gauge how ideas, opinions and new trends impact society. We show that correlations between different international brands, nouns or US major cities follow a universal scale free distribution. The correlations indicate a self-organizing dynamics in large social organizations where the exchange of information between individuals is highly volatile. Our method provides new fundamental insight on the propagation of opinions and the emergence of trends in online communities.
Estimating Mesophyll Conductance from Measurements of C18OO Photosynthetic Discrimination and Carbonic Anhydrase Activity1[OPEN]

A new model based on the relationship between C18OO photosynthetic discrimination and carbonic anhydrase activity estimates mesophyll conductance in C3 and C4 plants. Carbonic anhydrase (CA) activity in leaves catalyzes the 18O exchange between CO2 and water during photosynthesis. This feature has been used to estimate the mesophyll conductance to CO2 (gm) from measurements of online C18OO photosynthetic discrimination (∆18O). Based on CA assays on leaf extracts, it has been argued that CO2 in mesophyll cells should be in isotopic equilibrium with water in most C3 species as well as many C4 dicot species. However, this seems incompatible with ∆18O data that would indicate a much lower degree of equilibration, especially in C4 plants under high light intensity. This apparent contradiction is resolved here using a new model of C3 and C4 photosynthetic discrimination that includes competition between CO2 hydration and carboxylation and the contribution of respiratory fluxes. This new modeling framework is used to revisit previously published data sets on C3 and C4 species, including CA-deficient plants. We conclude that (1) newly ∆18O-derived gm values are usually close but significantly higher (typically 20% and up to 50%) than those derived assuming full equilibration and (2) despite the uncertainty associated with the respiration rate in light, or the water isotope gradient between mesophyll and bundle sheath cells, robust estimates of ∆18O-derived gm can be achieved in both C3 and C4 plants.
Reticulated Platelets: Changing Focus from Basics to Outcomes

Abstract Platelets play an essential role in the pathophysiology of atherothrombosis. Reticulated platelets (RPs) are the youngest platelet population in the circulation; their presence is an indicator of platelet turnover. Circulating levels of RPs are increased in patients with coronary artery disease and stroke. Preliminary indications are that the proportion of circulating RP is associated with the likelihood of ischaemic events such as acute coronary syndrome and stroke. Plausible mechanisms include: (1) increased participation of these platelets in thrombosis due to messenger ribonucleic acid that may be translated to active proteins, (2) lack of exposure to anti-platelet drugs since they are newly released from the bone marrow or (3) their presence is a non-specific marker of inflammation. In this state-of-the-art review, we discuss the implication of RP in coronary artery disease and in hypo-responsiveness to the most commonly used anti-platelet drugs.
On the Use of IoT Sensors for Indoor Conditions Assessment and Tuning of Occupancy Rates Models

In the last years, the energy savings policies are affecting several aspects of the everyday life, from the introduction of renewables and the use of Electric Vehicles, down to the adoption of more efficient lightening systems. Considering a typical building, one of the most energy consuming plant is the Heating, Ventilation and Air Conditioning (HV AC) system. This consideration is especially true for large public-access buildings, such as schools, universities and public administrations. In these cases, the energy saving of buildings depends on the capability to optimize the behavior of the HV AC. Typically, the HV AC control system is based on static models of the building, which consider an average occupancy rate of each of the rooms. On the contrary, in this research work, a Cognitive Building approach has been considered. The Energy Management System (EMS) is able to control and to regulate the HV AC system, considering an occupancy rate model able to take into consideration user’ habits and the indoor air quality (IAQ), provided in near real-time by IoT sensors. This approach has been applied to the eLUX lab building of the campus of the University of Brescia, Italy. Data provided by IAQ sensors (temperature, relative humidity, CO2) are used to refine the results of occupancy rate models of rooms of this building. The experimental results show as in the 22.15 % of the samples, the CO2concentration overcame the 1000 ppm threshold of perception of fresh air and good condition.
Interactive dance performance evaluation using timing and accuracy similarity

This paper presents a dance performance evaluation how well a learner mimics the teacher's dance as follows. We estimate the human skeletons, then extract dance features such as torso and first and second-degree feature, and compute the similarity score between the teacher and the learner dance sequence in terms of timing and pose accuracies. To validate the proposed dance evaluation method, we conducted several experiments on a large K-Pop dance database. The proposed methods achieved 98% concordance with experts' evaluation on dance performance.
Condition Monitoring in Industry 4.0-Design Challenges and Possibilities: A Case Study

The application of IoT in manufacturing industry is believed to transform the traditional concept of factories into fully integrated manufacturing systems that are capable of meeting different requirements/demands originating within the factory, in supply chain and in user communities in a real time manner. One key area that is likely to benefit at an early stage development of the Industrial IoT is the condition monitoring of the production machinery. However, there are several challenges in realizing effective IoT enabled condition monitoring solutions with currently available enabling technologies. In this paper, we analyze the design challenges associated with realizing IoT enabled industrial condition monitoring with particular focus on enabling end-devices in managing large amount of acquired data. With the help of a vibration based condition monitoring case study the challenges are analyzed in a quantitative manner and possible alternatives are explored. The results suggest that for the efficient and long term condition monitoring in the smart industry of the future, improvements in the enabling technologies are required to design optimized end-devices.