Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Software Rejuvenation for CPS Security Using Invariant Sets

Software rejuvenation has been proposed as a strategy to protect cyber-physical systems (CSPs) against unanticipated and undetectable cyber attacks. The basic idea is to refresh the system periodically with a secure and trusted copy of the online software so as to eliminate all effects of malicious modifications to the run-time code and data. This paper considers software rejuvenation design from a control-theoretic perspective. Invariant sets for the Lyapunov function for the safety controller are used to derive bounds on the time that the CPS can operate in mission control mode before the software must be refreshed. With these results it can be guaranteed that the CPS will remain safe under cyber attacks against the run-time system. The approach is illustrated using simulation of the nonlinear dynamics of a quadrotor system. The concluding section discusses directions for further research.
Power Efficient Approximate Booth Multiplier

Power consumption is an important constraint in multimedia and deep learning applications. Approximate computing offers efficient approach to reduce power consumption. In this paper, novel approximation is proposed for radix-4 booth multiplication. Approximation is introduced in partial product generation and partial product accumulation circuits. Radix-4 partial product generation and accumulation approximation is proposed which remarkably enhances the performance. The proposed approximate booth multiplier achieves 41% area reduction and 49% power reduction compared to an exact booth multiplier. Also, it has better area, power and error metrics compared to existing works on approximate multipliers. The proposed multiplier is evaluated with an image processing application-in Discrete Cosine Transform (DCT) encoding part of JPEG compression and found to perform almost similar to exact multiplication unit.
Low-Complexity Software Stack Decoding of Polar Codes

Polar codes are a recent class of linear error-correcting codes that asymptotically achieve the channel capacity at infinite code length. The Successive Cancellation List (SCL) algorithm yields very good error-correction performance, at the cost of high implementation complexity. The Stack (SCS) decoding algorithm provides similar error-correction performance at a lower complexity. In this work, we propose an efficient software implementation of the SCS decoding algorithm, along with techniques to further reduce its computational complexity. In particular, we reduce the SCS memory requirements through efficient path switching, replace the stack sorting with a linear search, and explore the use of a partial CRC along with an early termination criterion. Using the proposed methods, we are able to reduce the computational complexity of the SCS decoder, reducing the number of estimated bits up to 97% with respect to SCL, while maintaining similar error-correction performance as SCL.
Comparison of microleakage between bulk-fill flowable and nanofilled resin-based composites

Aim The objective was to compare the marginal leakage (silver nitrate uptake) of nanohybrid resin-based composite (RBC) and two bulk-fill flowable RBCs with specific clinical protocols. Methods Four experimental groups of RBC were investigated including conventional composite Filtek™ Supreme in 2 mm increment (FS2), Filtek™ Supreme in 4 mm increment (FS4), Filtek™ Supreme Flowable (BFF), and SureFil® SDR® flow (SDR). Class II box preparation (4 × 4 × 3 mm) in extracted intact human molars was carried out and restored using the experimental groups, all according to the manufacturers’ recommendations except FS4. Samples were aged by thermocycling (2,000 cycles). Microleakage was calculated by measuring dye penetration in sectioned teeth using a stereomicroscope. Level of significance was set at P < 0.05. Results BFF and FS2 exhibited the least dye penetration and microleakage measurement with no significant difference between the two groups, followed by SDR. FS4 showed the highest microleakage with significant difference in comparison with BFF and FS2. Gingival microleakage was found to be significantly higher than occlusal microleakage. Conclusion The microleakage of the bulk-fill composites BFF and SDR are comparable with conventional composite FS2; however, it is more predictable to use FS2.
Combinational Access Tunnel FET SRAM for Ultra-Low Power Applications

In this paper, a novel combinational access topology of Tunnel FET (TFET) SRAM is proposed for ultra-Low Power applications. Since forward p-i-n current of TFET could cause serious damage to SRAM circuit performance, the proposed topology can avoid the forward bias applied to the p-i-n junction, thus increasing SRAM cell read and hold static noise margin (SNM) and decreasing its static power consumption dramatically. At 0.6 V supply voltage, the combinational access TFET SRAM topology presents 26% hold SNM larger than traditional TFET SRAM topologies, 8 orders of magnitude lower static power consumption, and 2 order of magnitude lower power delay product, demonstrating its great potential for ultra-low power applications.
Globally Wireless Locally Wired (GloWiLoW): A Clock Distribution Network for Many-Core Systems

Modern high-performance systems are now facing critical issues on delivering power-efficient and globally interconnected clock networks. Conventional metal-based interconnect has gradually reached its bottleneck with the technology scaling which limits system performance as the interconnect delay has already overweighed circuit gate delay. A typical clock distribution network (CDN) might consume up to 50% of the total chip power and could generate large phase delay and clock uncertainties due to its RC characteristic and unbalanced load. This paper proposes a novel hybrid wire-wireless CDN, which improves the performance of on-chip clock distribution significantly using embedded wireless clock transmitter and receiver fabrics. In particular, On-Off-Keying (OOK) transceivers are implemented for overall system simplicity and has achieved significant power-efficiency. Results indicate that the total propagation delay can be reduced to 39.4ps, which is 57 times lower than the conventional H-tree. Besides, the system clock skew can be predictable and limited only by the displacement of clock receivers. Less than 26.9ps of clock skew (6.7% of a clock period at 2.5GHz) could be found within the proposed CDN and hence, shows the promising potential of future high-performance on-chip clock distribution.
On-Line Event-Driven Hand Gesture Recognition Based on Surface Electromyographic Signals

This paper presents a minimum complexity hand movement recognition algorithm based on Average Threshold Crossing (ATC) technique. It exploits the number of threshold-crossing events, generated by a full-custom acquisition board, from the surface ElectroMyoGraphic (sEMG) signals of three forearm muscles to detect four different movements of the wrist: flexion, extension, abduction and grasp. A Support Vector Machine (SVM) model has been trained with the signals acquired from ten subjects, who repeated ten times each gesture. To avoid correlation between training and testing dataset, the Leave One Subject Out (LOSO) cross-validation technique has been chosen. The average ATC classifier's accuracy is 92.87 %, only 5.34 % below the results obtained feeding the same model with the sEMG features extracted from the raw sampled signals. The total latency of the algorithm, from the acquisition to the prediction, is 160 ms. Power consumption was considered too: with less than the power budget for one sampled sEMG channel, it is possible to acquire and transmit (through a Bluetooth low energy module) the event-driven data of four sEMG channels, with an effective data rate of only 28 B/s. Obtained performance makes this technique suited for wearable systems or Internet-of-Things (IoT) applications.
Differentiable Fine-grained Quantization for Deep Neural Network Compression

Neural networks have shown great performance in cognitive tasks. When deploying network models on mobile devices with limited resources, weight quantization has been widely adopted. Binary quantization obtains the highest compression but usually results in big accuracy drop. In practice, 8-bit or 16-bit quantization is often used aiming at maintaining the same accuracy as the original 32-bit precision. We observe different layers have different accuracy sensitivity of quantization. Thus judiciously selecting different precision for different layers/structures can potentially produce more efficient models compared to traditional quantization methods by striking a better balance between accuracy and compression rate. In this work, we propose a fine-grained quantization approach for deep neural network compression by relaxing the search space of quantization bitwidth from discrete to a continuous domain. The proposed approach applies gradient descend based optimization to generate a mixed-precision quantization scheme that outperforms the accuracy of traditional quantization methods under the same compression rate.
Significance of Heparin-Binding Protein and D-dimers in the Early Diagnosis of Spontaneous Bacterial Peritonitis

Objectives Ascites and spontaneous bacterial peritonitis (SBP) are among the most important complications of decompensated liver cirrhosis. In clinical practice, new inflammation biomarkers are needed for the early diagnosis of SBP, as well-known biomarkers, such as C-reactive protein (CRP), procalcitonin (PCT), or peripheral blood white blood cell (WBC) count, lack the required specificity and sensitivity. The aim of the study was to evaluate the significance of heparin-binding protein (HBP) in comparison to CRP, PCT, WBC, and D-dimers in the diagnosis of SBP. Design Cross-sectional descriptive single-center study. Setting Department of Infectious and Tropical Diseases and Hepatology, Medical University of Warsaw, Poland. Patients All patients admitted to the aforementioned department with decompensated liver cirrhosis and ascites between February 1, 2016, and June 30, 2017. Intervention Several markers (HBP, CRP, PCT, WBC, and D-dimers) were analysed in blood serum in regard to their potential use in the diagnosis of SBP in patients with decompensated liver cirrhosis and ascites. We correlated the levels of the aforementioned markers with an ascitic fluid polymorphonuclear count using simple linear regression and multiple linear regression. Sensitivities, specificities, and positive and negative predictive values for SBP were calculated for the aforementioned makers of inflammation. Measurements and Main Results A total of 63 patients with decompensated liver cirrhosis and ascites participated in the study. The etiology of liver cirrhosis was varied (HCV: n = 40, HBV: n = 13, HCV/HBV: n = 4, AIH: n = 3, PBC: n = 2, and haemochromatosis: n = 1). After the peritoneal tap, 31 patients were determined to have SBP (defined as an ascitic fluid polymorphonuclear count > 250 cells/μL) and 32 patients had no evidence of SBP on peritoneal tap. A very weak, but statistically significant, correlation of HBP, WBC, and D-dimer levels with the peritoneal fluid polymorphonuclear (PMN) count was observed in the simple regression model, but multivariable analysis using the multiple regression model showed that only D-dimers correlated with peritoneal fluid PMNs independently from other inflammation biomarkers. A D-dimer cutoff value of 1500 ng/mL was determined optimal for ruling out SBP due to high sensitivity (96.8%) and a high negative predictive value (92.9%), although predictably, this marker was not useful for confirming SBP due to low specificity (40.6%) and a low positive predictive value (61.2%). The usefulness of D-dimers was limited by the fact that only 22.2% of the studied patients had D-dimer levels below 1500 ng/mL. HBP and WBC showed little to no predictive value in this study. Conclusions D-dimers < 1500 ng/mL make the diagnosis of SBP unlikely, although the peritoneal tap is still the reference method in such situations. In the studied group, the determination of HBP was of no diagnostic benefit in the diagnosis of SBP.
Discovery on Antibiotic Resistance Patterns of Vibrio parahaemolyticus in Selangor Reveals Carbapenemase Producing Vibrio parahaemolyticus in Marine and Freshwater Fish

Vibrio parahaemolyticus, a Gram-negative halophilic bacterium is often associated with fish and fishery products, thus causing gastroenteritis in humans upon ingestion of contaminated food. V. parahaemolyticus has become a globally well-known pathogen with yearly reported cases in many countries. This study aimed to discover the antibiotic resistance patterns of V. parahaemolyticus as well as detect Carbapenem resistant isolates from marine and freshwater fish in Selangor. A total of 240 freshwater and marine fish samples collected from wet market and supermarket in Selangor were tested for the presence of V. parahaemolyticus. All the fish samples were determined positive for V. parahaemolyticus using conventional microbiological culture-based method. The toxR gene were detected via polymerase chain reaction (PCR) in 165/240 (69%) isolates. The two-virulence factor of V. parahaemolyticus, thermostable direct hemolysin (tdh) and TDH-related hemolysin (trh) was screened via PCR. As such, four isolates were trh+and none were tdh+. Majority of the isolates presented high resistance to ampicillin (88%), amikacin (64%), and kanamycin (50%). In addition, this study identified 19-imipenem resistant isolates isolated from freshwater and marine fish samples. Further analysis of these 19-imipenem resistant isolates revealed that the resistance toward imipenem was plasmid mediated after plasmid curing assay. The multiple antibiotics resistance index was >0.2 for 70% of the isolates. In summary, the results confirm the presence of V. parahaemolyticus in freshwater and marine fish samples in Selangor, Malaysia. To our best knowledge, this is the first report discovering the antibiotic resistant patterns and Carbapenem-resistant isolates of V. parahaemolyticus isolated from marine and freshwater fish samples in Selangor.
Biological Children Versus Stepchildren: Interorganizational Learning Processes of Spinoff and Nonspinoff Suppliers

Interorganizational scholars have long thought about how firms learn through buyer relationships. However, it is not clear whether dyadic learning gains are susceptible to imitation or are only inherited and whether these gains decay over time or are of an enduring nature. In this paper, I import ideas from the organizational imprinting literature into the interorganizational literature and apply the knowledge-based and learning views of the firm to examine how suppliers with differing initial endowments learn to work together with a buyer. The findings from an inductive multiple case study of spinoff and nonspinoff suppliers of an automotive manufacturer parent in Turkey reveal the following three learning mechanisms: informal relationships and social capital, transfer of routines, and shared identity. Although nonspinoff suppliers also exhibit evidence of several learning processes to a certain extent, spinoff suppliers’ deeper relationship, in particular their shared identity, with their parent based on their direct parental heritage tends to be more difficult for them to copy. No matter how hard nonspinoff suppliers try, they have “one hand tied behind their back,” they remain stepchildren, and they never truly become a biological child. By providing a novel setting and a rich set of qualitative data on the learning behaviors of these two types of suppliers, this study teases apart the knowledge and resources that can be “learned from external sources” versus those that can “only be inherited. ”
Ventilation Prior to Umbilical Cord Clamping Improves Cardiovascular Stability and Oxygenation in Preterm Lambs After Exposure to Intrauterine Inflammation

Background: Delaying umbilical cord clamping until after aeration of the lung (physiological-based cord clamping; PBCC) maintains cardiac output and oxygenation in preterm lambs at birth, however, its efficacy after intrauterine inflammation is not known. Given the high incidence of chorioamnionitis in preterm infants, we investigated whether PBCC conferred any benefits compared to immediate cord clamping (ICC) in preterm lambs exposed antenatally to 7 days of intrauterine inflammation. Methods: Ultrasound guided intraamniotic injection of 20 mg Lipopolysaccharide (from E. coli:055:B5) was administered to pregnant ewes at 0.8 gestation. Seven days later, ewes were anesthetized, preterm fetuses exteriorised via cesarean section, and instrumented for continuous measurement of pulmonary, systemic and cerebral pressures and flows, and systemic, and cerebral oxygenation. Lambs were then randomized to either PBCC, whereupon ventilation was initiated and maintained for 3 min prior to umbilical cord clamping, or ICC where the umbilical cord was cut and ventilation initiated 30 s later. Ventilation was maintained for 30 min. Results: ICC caused a rapid fall in systemic (by 25%) and cerebral (by 11%) oxygen saturation in ICC lambs, concurrent with a rapid increase in carotid arterial pressure and heart rate. The overshoot in carotid arterial pressure was sustained in ICC lambs for the first 20 min of the study. PBCC maintained cardiac output and prevented the fall in cerebral oxygen delivery at birth. PBCC lambs had lower respiratory compliance and higher respiratory requirements throughout the study. Conclusion: PBCC mitigated the adverse effects of ICC on oxygenation and cardiac output, and therefore could be more beneficial in preterm babies exposed to antenatal inflammation as it maintains cardiac output and oxygen delivery. The increased respiratory requirements require further investigation in this sub-group of preterm infants.
New Insights into the Role of Exercise in Inhibiting mTOR Signaling in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) does not express estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 and is characterized by its aggressive nature, lack of targets for targeted therapies, and early peak of recurrence. Due to these specific characteristics, chemotherapy does not usually yield substantial improvements and new target therapies and alternative strategies are needed. The beneficial responses of TNBC survivors to regular exercise, including a reduction in the rate of tumor growth, are becoming increasingly apparent. Physiological adaptations to exercise occur in skeletal muscle but have an impact on the entire body through systemic control of energy homeostasis and metabolism, which in turn influence the TNBC tumor microenvironment. Gaining insights into the causal mechanisms of the therapeutic cancer control properties of regular exercise is important to improve the prescription and implementation of exercise and training in TNBC survivors. Here, we provide new evidence of the effects of exercise on TNBC prevention, control, and outcomes, based on the inhibition of the phosphatidylinositol-3-kinase (PI3K)/protein kinase B (PKB also known as Akt)/mammalian target of rapamycin (mTOR) (PI3K-Akt-mTOR) signaling. These findings have wide-ranging clinical implications for cancer treatment, including recurrence and case management.
Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data

We present structured domain randomization (SDR), a variant of domain randomization (DR) that takes into account the structure of the scene in order to add context to the generated data. In contrast to DR, which places objects and distractors randomly according to a uniform probability distribution, SDR places objects and distractors randomly according to probability distributions that arise from the specific problem at hand. In this manner, SDR-generated imagery enables the neural network to take the context around an object into consideration during detection. We demonstrate the power of SDR for the problem of 2D bounding box car detection, achieving competitive results on real data after training only on synthetic data. On the KITTI easy, moderate, and hard tasks, we show that SDR outperforms other approaches to generating synthetic data (VKITTI, Sim 200k, or DR), as well as real data collected in a different domain (BDD100K). Moreover, synthetic SDR data combined with real KITTI data outperforms real KITTI data alone.11Video is at http://youtu.be/1WdjWJYx9AY
Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving

One of the fundamental challenges in the design of perception systems for autonomous vehicles is validating the performance of each algorithm under a comprehensive variety of operating conditions. In the case of vision-based semantic segmentation, there are known issues when encountering new scenarios that are sufficiently different to the training data. In addition, even small variations in environmental conditions, such as illumination and precipitation, can affect the classification performance of the segmentation model. Given the reliance on visual information, these effects often translate into poor semantic pixel classification which can potentially lead to catastrophic consequences when driving autonomously. This paper presents a novel method for analyzing the robustness of semantic segmentation models and provides a number of metrics to evaluate the classification performance over a variety of environmental conditions. The process incorporates an additional sensor (lidar) to automate the process and improve the system integrity, eliminating the need for labor-intensive hand labeling of validation data. The experimental results are presented based on multiple datasets collected at different times of the year with different environmental conditions. We extract the ”Road” class using the lidar to demonstrate the concepts, but this could be extended to other classes with different feature detection algorithms. These results show that the semantic segmentation performance varies depending on the weather, camera parameters, and existence of shadows. The results also demonstrate how the metrics can be used to compare and validate the performance after making improvements to a model, and compare the performance of different networks.
A multi-step approach to the single fault diagnosis of DC-DC switched power converters

This paper presents a completely new technique developed to locate a single parametric fault in DC-DC power converters by means of time-domain measurements or simulations, following a rigorous approach. The technique is composed by three separated stages, a first one which evaluates testability and ambiguity groups of the Converter (or, more in general, the Circuit) Under Test (CUT), a second one which localizes the fault, classifying it in an appropriate Fault Class (FC) and a last one (optional) that can extract the value of the faulty component. The fabrication tolerances of the healthy components are taken into account at each stage of the method.