L0 Sparse Inverse Covariance Estimation

Recently, there has been focus on penalized log-likelihood covariance estimation for sparse inverse covariance (precision) matrices. The penalty is responsible for inducing sparsity, and a very common choice is the convex $l_1$ norm. However, the best estimator performance is not always achieved with this penalty. The most natural sparsity promoting "norm" is the non-convex $l_0$ penalty but its lack of convexity has deterred its use in sparse maximum likelihood estimation. In this paper we consider non-convex $l_0$ penalized log-likelihood inverse covariance estimation and present a novel cyclic descent algorithm for its optimization. Convergence to a local minimizer is proved, which is highly non-trivial, and we demonstrate via simulations the reduced bias and superior quality of the $l_0$ penalty as compared to the $l_1$ penalty

PS-BBICS: Pulse stretching bulk built-in current sensor for on-chip measurement of single event transients

The bulk built-in current sensor (BBICS) is a cost-effective solution for detection of energetic particle strikes in integrated circuits. With an appropriate number of BBICSs distributed across the chip, the soft error locations can be identified, and the dynamic fault-tolerant mechanisms can be activated locally to correct the soft errors in the affected logic. In this work, we introduce a pulse stretching BBICS (PS-BBICS) constructed by connecting a standard BBICS and a custom-designed pulse stretching cell. The aim of PS-BBICS is to enable the on-chip measurement of the single event transient (SET) pulse width, allowing to detect the linear energy transfer (LET) of incident particles, and thus assess more accurately the radiation conditions. Based on Spectre simulations, we have shown that for the LET from 1 to 100 MeV cm2 mg−1, the SET pulse width detected by PS-BBICS varies by 620–800 ps. The threshold LET of PS-BBICS increases linearly with the number of monitored inverters, and it is around 1.7 MeV cm2 mg−1 for ten monitored inverters. On the other hand, the SET pulse width is independent of the number of monitored inverters for LET > 4 MeV cm2 mg−1. It was shown that supply voltage, temperature and process variations have strong impact on the response of PS-BBICS

Moljac krompira kao štetočina – od prisutne do visoko ekonomski značajne vrste u Srbiji

Potato tuber moth, Phthorimea operculela Zeller (Lepidoptera: Gelichiidae) becoming economically very important pest of potato in open field condition in West region of Serbia. Surveillance and monitoring are organized by Serbian Forecast and Report Services (Prognozno Izveštajna Služba Srbije - PISS) from 2013 by phero-traps. At the territory of Extension Services Čačak monitoring represent a part of obligatory measures under PISS coordination. In this area pest is present from 2013. High population is registered during 2015. The imposes need to continue pest monitoring and documenting the damage from the both (field and storage).Moljac krompira (MK), Phthorimea operculela Zeller (Lepidoptera: Gelichiidae) postaje ekonomski sve značajnija štetočina u regionu Zapadne Srbije. Monitoring vrste sprovodi se feromonskim klopkama od 2013. godine u okviru redovnih mera praćenja koje koordinira Prognozno Izveštajna Služba Srbije. Na teritoriji PSS Čačak, štetočina je prisutna od 2013.godine, a u vegetaciji 2015. godine registrovana je visoka brojnost. Nameće se neophodnim nastavak moniotoringa i dokumentovanje šteta na terenu

Analysis of Single Event Transient Effects in Standard Delay Cells Based on Decoupling Capacitors

Single Event Transients (SETs), i.e., voltage glitches induced in combinational logic as a result of the passage of energetic particles, represent an increasingly critical reliability threat for modern complementary metal oxide semiconductor (CMOS) integrated circuits (ICs) employed in space missions. In rad-hard ICs implemented with standard digital cells, special design techniques should be applied to reduce the Soft Error Rate (SER) due to SETs. To this end, it is essential to consider the SET robustness of individual standard cells. Among the wide range of logic cells available in standard cell libraries, the standard delay cells (SDCs) implemented with the skew-sized inverters are exceptionally vulnerable to SETs. Namely, the SET pulses induced in these cells may be hundreds of picoseconds longer than those in other standard cells. In this work, an alternative design of a SDC based on two inverters and two decoupling capacitors is introduced. Electrical simulations have shown that the propagation delay and SET robustness of the proposed delay cell are strongly influenced by the transistor sizes and supply voltage, while the impact of temperature is moderate. The proposed design is more tolerant to SETs than the SDCs with skew-sized inverters, and occupies less area compared to the hardening configurations based on partial and complete duplication. Due to the low transistor count (only six transistors), the proposed delay cell could also be used as a SET filter

Properties and customization of sensor materials for biomedical applications.

Low-power chemo- and biosensing devices capable of monitoring clinically important parameters in real time represent a great challenge in the analytical field as the issue of sensor calibration pertaining to keeping the response within an accurate calibration domain is particularly significant (1–4). Diagnostics, personal health, and related costs will also benefit from the introduction of sensors technology (5–7). In addition, with the introduction of Registration, Evaluation, Authorization, and Restriction of Chemical Substances (REACH) regulation, unraveling the cause–effect relationships in epidemiology studies will be of outmost importance to help establish reliable environmental policies aimed at protecting the health of individuals and communities (8–10). For instance, the effect of low concentration of toxic elements is seldom investigated as physicians do not have means to access the data (11)

lq sparse signal estimation with applications

The use of sparsity has emerged in the last fifteen years as an important tool for solving many problems in the areas of signal processing and statisticalinference. In this dissertation we pursue three significant applications of sparsity; sparse linear regression, low rank matrix completion and sparseinverse covariance selection. In the first and third topic, sparsity refers to having a small number of nonzero vector and matrix entries respectively,while in the second topic it is associated with low matrix rank.A penalized approach is considered involving optimization of an objective function with two terms. One of the terms measures the goodness of fit i.e.the error between the observed data and the estimated solution, while the other is a penalty responsible for inducing sparse solutions, hence the namepenalized problem.It is well understood that the natural way of inducing sparsity is through the l0 ``norm'' i.e. the counting function or the discrete metric. Since the l0function is non convex, a large volume of literature has instead resorted to using the convex l1 norm as the penalty. Therefore, the failure to consider thel0 penalized problem is a point of departure for this dissertation. In order to bridge the gap between the l0 and l1 penalties, the focus becomes thedevelopment of non convex optimization methods for the lq (00 but the emphasis is on 0<q<1. Imposing basic assumptions, we prove that theiterates converge to a stationary point, and if this point is a local minimizer then it is also proved that the convergence is R-linear. Simulations are giventhat illustrate the potential of considering the use of our algorithm.Chapter 4 considers the topic of low rank matrix completion, where we develop an algorithm for optimizing an lq (rank) penalized least squares problemwith 0<q<1. In the development process we solve a non-trivial one dimensional lq optimization problem that is fundamental to our work. Additionally, ageneral algorithm convergence (fixed point) result with a non-trivial proof is given for 0&#8804;q<1. We illustrate with data analysis examples, comparing thereconstruction quality of three matrix singular value penalties: l0, l1 and lq, 0<q<1.Chapter 5 considers the topic of sparse inverse covariance estimation, where we develop an algorithm for optimizing an lq penalized log-likelihoodproblem with 0&#8804;q<1. The development requires the solutions of the one dimensional lq optimization problem from Chapter 4. These are additionallyused to prove some algorithm properties as well as some fixed point results. We illustrate with simulations and a real world application example.Reconstruction comparisons are given using four penalties: l0, l1, lq with 0<q<1, and SCAD.Both, Chapters 4 and 5 take into account and consider the selection of tuning parameters for real world data examples. These are based on the use ofmodified Bayesian Information Criteria.Finally, Chapter 6 provides the concluding remarks and some considered future work

Hybrid Minimally Invasive Esophagectomy–Surgical Technique and Results

Background: Hybrid minimally invasive esophagectomy (HMIE) has been proven to be superior when compared with open esophagectomy, with a significant reduction of postoperative morbidity. In HMIE, the laparotomy is replaced by a minimally invasive laparoscopic approach. The radical mediastinal resection plus reconstruction is performed by a thoracic approach through a muscle-sparing thoracotomy. In this instructional article, we describe the surgical technique of HMIE in detail in order to facilitate possible adoption of the procedure by other surgeons. In addition, we give the monocentric results of our own practice. Methods: Between 2013 and 2018, HMIE was performed in 157 patients. The morbidity and mortality data of the procedure is shown in a retrospective monocentric analysis. Results: Overall, 54% of patients had at least one perioperative complication. Anastomotic leak was evident in 1.9%, and a single patient had focal conduit necrosis of the gastric pull-up. Postoperative pulmonary morbidity was 31%. Pneumonia was found in 17%. The 90 day mortality was 2.5%. Wound infection rate was 3%, and delayed gastric emptying occurred in 17% of patients. In follow up, 12.7% presented with diaphragmatic herniation of the bowel, requiring laparoscopic hernia reduction and hiatal reconstruction and colopexy several months after surgery. Conclusion: HMIE is a highly reliable technique, not only for the resection part but especially in terms of safety in reconstruction and anastomosis. For esophageal surgeons with experience in minimally invasive anti-reflux procedures and obesity surgery, HMIE is easy and fast to learn and adopt