Choices and Consequences: A Cross-National Evaluation of Telecommunication Policies in Developing Countries

Telecommunications are increasingly being recognized as critical strategic infrastructure for ensuring the success of national social and economic development plans and programs, improving international competitiveness and integrating domestic economies into the world economy. In an effort to overcome chronic deficiencies in telecommunication performance and distribution of services, many developing countries have been engaged in liberalizing their telecommunication sectors. Liberalization here referring to the movement away from the traditional state-owned monopoly structure and towards the introduction of privatization and competition. This study examines the consequences of these developments by analyzing telecommunication developments in 81 developing countries from 1977 to 1988. The study is in two parts. The first part is theoretical and (a) identifies the technological and economic forces driving change in the sector; (b) reviews the policy options available to developing countries; (c) critically discusses the arguments both for and against the introduction of competition and privatization in the sector; and (d) outlines the importance of governmental commitment to the growth of telecommunications. The second part is empirical and presents the findings of a cross-national longitudinal evaluation of the impact of changes in policies governing sector structure for the supply and manufacture of telecommunications equipment, facilities and services, as well as the impact of governmental commitment, on sector performance and distribution. The evaluation is conducted in the context of the economic factors which are thought to condition the relationship between telecommunication policies and outcomes. It finds that movement toward liberalization has had little independent impact on telecommunications sector performance, but is associated with adverse conditions of access to and availability of services. In contrast, governmental commitment to the growth of the sector is found to be positively related with improvements in both sector performance and distribution at all levels of national income and under different compositions of economic activity. The findings suggest that if sector growth and development are important national priorities then attention should be turned more toward stepping-up government investments rather than towards sector restructuring

Online Discrepancy Minimization for Stochastic Arrivals

In the stochastic online vector balancing problem, vectors $v_1,v_2,\ldots,v_T$ chosen independently from an arbitrary distribution in $\mathbb{R}^n$ arrive one-by-one and must be immediately given a $\pm$ sign. The goal is to keep the norm of the discrepancy vector, i.e., the signed prefix-sum, as small as possible for a given target norm. We consider some of the most well-known problems in discrepancy theory in the above online stochastic setting, and give algorithms that match the known offline bounds up to $\mathsf{polylog}(nT)$ factors. This substantially generalizes and improves upon the previous results of Bansal, Jiang, Singla, and Sinha (STOC' 20). In particular, for the Koml\'{o}s problem where $\|v_t\|_2\leq 1$ for each $t$, our algorithm achieves $\tilde{O}(1)$ discrepancy with high probability, improving upon the previous $\tilde{O}(n^{3/2})$ bound. For Tusn\'{a}dy's problem of minimizing the discrepancy of axis-aligned boxes, we obtain an $O(\log^{d+4} T)$ bound for arbitrary distribution over points. Previous techniques only worked for product distributions and gave a weaker $O(\log^{2d+1} T)$ bound. We also consider the Banaszczyk setting, where given a symmetric convex body $K$ with Gaussian measure at least $1/2$, our algorithm achieves $\tilde{O}(1)$ discrepancy with respect to the norm given by $K$ for input distributions with sub-exponential tails. Our key idea is to introduce a potential that also enforces constraints on how the discrepancy vector evolves, allowing us to maintain certain anti-concentration properties. For the Banaszczyk setting, we further enhance this potential by combining it with ideas from generic chaining. Finally, we also extend these results to the setting of online multi-color discrepancy

Prefix Discrepancy, Smoothed Analysis, and Combinatorial Vector Balancing

A well-known result of Banaszczyk in discrepancy theory concerns the prefix discrepancy problem (also known as the signed series problem): given a sequence of $T$ unit vectors in $\mathbb{R}^d$, find $\pm$ signs for each of them such that the signed sum vector along any prefix has a small $\ell_\infty$-norm? This problem is central to proving upper bounds for the Steinitz problem, and the popular Koml\'os problem is a special case where one is only concerned with the final signed sum vector instead of all prefixes. Banaszczyk gave an $O(\sqrt{\log d+ \log T})$ bound for the prefix discrepancy problem. We investigate the tightness of Banaszczyk's bound and consider natural generalizations of prefix discrepancy: We first consider a smoothed analysis setting, where a small amount of additive noise perturbs the input vectors. We show an exponential improvement in $T$ compared to Banaszczyk's bound. Using a primal-dual approach and a careful chaining argument, we show that one can achieve a bound of $O(\sqrt{\log d+ \log\!\log T})$ with high probability in the smoothed setting. Moreover, this smoothed analysis bound is the best possible without further improvement on Banaszczyk's bound in the worst case. We also introduce a generalization of the prefix discrepancy problem where the discrepancy constraints correspond to paths on a DAG on $T$ vertices. We show that an analog of Banaszczyk's $O(\sqrt{\log d+ \log T})$ bound continues to hold in this setting for adversarially given unit vectors and that the $\sqrt{\log T}$ factor is unavoidable for DAGs. We also show that the dependence on $T$ cannot be improved significantly in the smoothed case for DAGs. We conclude by exploring a more general notion of vector balancing, which we call combinatorial vector balancing. We obtain near-optimal bounds in this setting, up to poly-logarithmic factors.Comment: 22 pages. Appear in ITCS 202

Xanthogranulomatous Endometritis with calculus formation in setting of prolapsed uterus

Xanthogranulomatous inflammation is a rare benign inflammatory lesion characterized by sheets of lipid-laden foamy histiocytes. It has been reported in various organs, mainly the kidney and gall bladder. Xanthogranulomatous endometritis (XGE) is sporadic, with only a few cases reported in the English medical literature. Herein, we report a case of xanthogranulomatous endometritis with the formation of stones in a 50-year-old female patient with a prolapsed uterus. Grossly the endometrium was irregular, and the uterine cavity was filled with a yellow friable material, a polypoid growth, and yellowish stones. The microscopy showed sheets of histiocytes with few preserved endometrial glands. In this case, the xanthogranulomatous inflammation may mimic a clear cell carcinoma involving the endometrium and myometrium. One of the important differential diagnoses is malakoplakia. Immunohistochemistry and special stains are helpful in diagnosis

Surveys without Questions: A Reinforcement Learning Approach

The 'old world' instrument, survey, remains a tool of choice for firms to obtain ratings of satisfaction and experience that customers realize while interacting online with firms. While avenues for survey have evolved from emails and links to pop-ups while browsing, the deficiencies persist. These include - reliance on ratings of very few respondents to infer about all customers' online interactions; failing to capture a customer's interactions over time since the rating is a one-time snapshot; and inability to tie back customers' ratings to specific interactions because ratings provided relate to all interactions. To overcome these deficiencies we extract proxy ratings from clickstream data, typically collected for every customer's online interactions, by developing an approach based on Reinforcement Learning (RL). We introduce a new way to interpret values generated by the value function of RL, as proxy ratings. Our approach does not need any survey data for training. Yet, on validation against actual survey data, proxy ratings yield reasonable performance results. Additionally, we offer a new way to draw insights from values of the value function, which allow associating specific interactions to their proxy ratings. We introduce two new metrics to represent ratings - one, customer-level and the other, aggregate-level for click actions across customers. Both are defined around proportion of all pairwise, successive actions that show increase in proxy ratings. This intuitive customer-level metric enables gauging the dynamics of ratings over time and is a better predictor of purchase than customer ratings from survey. The aggregate-level metric allows pinpointing actions that help or hurt experience. In sum, proxy ratings computed unobtrusively from clickstream, for every action, for each customer, and for every session can offer interpretable and more insightful alternative to surveys.Comment: The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19

Methods of Sentinel Lymph Node Detection and Management in Urinary Bladder Cancer—A Narrative Review

© 2022 by the authors.Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).Introduction: Detection of lymph node status in bladder cancer significantly impacts clinical decisions regarding its management. There is a wide range of detection modalities for this task, including lymphoscintigraphy, computed tomography, magnetic resonance imaging, single-photon emission computed tomography, positron emission tomography, and fluoroscopy. We aimed to study the pre- and intraoperative detection modalities of sentinel lymph nodes in urinary bladder cancer. Method: This narrative review was performed by searching the PubMed and EMBASE libraries using the following search terms: (“Transitional cell carcinoma of the bladder” OR “urothelial cancer” OR “urinary bladder cancer” OR “bladder cancer”) AND ((“sentinel lymph node”) OR (“lymphatic mapping”) OR (“lymphoscintigraphy”) OR (“lymphangiography”) OR (“lymph node metastases”)). Studies analysing the effectiveness and outcomes of sentinel lymph node detection in bladder cancer were included, while non-English language, duplicates, and non-article studies were excluded. After analysing the libraries and a further manual search of bibliographies, 31 studies were included in this paper. We followed the RAMESES publication standard for narrative reviews to produce this paper. Results: Of the 31 studies included, 7 studies included multiple detection methods; 5 studies included lymphoscintigraphy; 5 studies included computed tomography and/or single-photon emission computed tomography; 5 studies included fluoroscopy; 4 studies included magnetic resonance imaging; and 5 studies included positron emission tomography. Discussion: Anatomical, radioactive, and functional detection modalities have been studied independently and in combination. The consensus is that preoperative detection with imaging helps guide surgical management and intraoperative detection methods help capture any lymph nodes that may have been missed. Each of these types of detection represent their own set of benefits and drawbacks, but there is currently limited evidence to support any change in overall practice to replace conventional staging.Peer reviewedFinal Published versio

Plasma-Assisted Large-Scale Nanoassembly of Metal–Insulator Bioplasmonic Mushrooms

Large-scale plasmonic substrates consisting of metal–insulator nanostructures coated with a biorecognition layer can be exploited for enhanced label-free sensing by utilizing the principle of localized surface plasmon resonance (LSPR). Most often, the uniformity and thickness of the biorecognition layer determine the sensitivity of plasmonic resonances as the inherent LSPR sensitivity of nanomaterials is limited to 10–20 nm from the surface. However, because of time-consuming nanofabrication processes, there is limited work on both the development of large-scale plasmonic materials and the subsequent surface functionalizing with biorecognition layers. In this work, by exploiting properties of reactive ions in an SF₆ plasma environment, we are able to develop a nanoplasmonic substrate containing ∼10⁶/cm² mushroom-like structures on a large-sized silicon dioxide substrate (i.e., 2.5 cm by 7.5 cm). We further investigate the underlying mechanism of the nanoassembly of gold on glass inside the plasma environment, which can be expanded to a variety of metal–insulator systems. By incorporating a novel microcontact printing technique, we deposit a highly uniform biorecognition layer of proteins on the nanoplasmonic substrate. The bioplasmonic assays performed on these substrates achieve a limit of detection of 10^–17 g/mL (∼66 zM) for biomolecules such as antibodies (∼150 kDa). Our simple nanofabrication procedure opens new opportunities in fabricating versatile bioplasmonic materials for a wide range of biomedical and sensing applications

Association of Dietary and Physical Activity Patterns and Hypertension in Western Rajasthan, 2022

Introduction: Hypertension (HTN) is a modifiable risk factor for coronary artery disease, heart failure, cerebrovascular disease and chronic renal failure. HTN affects about 1 billion people globally; by 2025, up to 1.58 billion adults worldwide are likely to suffer from complications of HTN. This study was done to know the diet and physical activity patterns and HTN among the population of three districts of Western Rajasthan. Objectives: To study the dietary and physical activity patterns among the population of Western Rajasthan. and to compare key findings among three districts Pali, Jodhpur and Barmer so that lifestyle changes can be recommended. Methods: A case-control study was done among attendees of NCD clinics of tertiary-level centers in Pali, Barmer and Jodhpur. Hospital Controls were matched to age (± 5 years) and gender Considering the prevalence of HTN to be 20%*, the proportion of exposure in the general population as 0.2, odds ratio to be 2.2, power =80%, alpha=5% sample size is estimated to be 102 cases & 102 controls (from each district). Results: Overall being married (OR= 3.3), having diabetes Cardiac disease (OR= 2.6), excessive salt consumption (OR= 2.7), moderate physical exercise less than 30 minutes (OR=1.9), using oil other than vegetable oil(OR=1.8) , Age >?60 years (OR =1.4) were the key risk factors. It was found that high BMI (BMI>27), consumption of non-vegetable oils (12.7%) was highest in Jodhpur, lack of moderate exercise for at least 30 minutes (81%), lack of sports activity (92%) was highest in Pali, least number of days/week of fruits and vegetables consumption (~1.64 days) was seen in Barmer. Conclusions: Change in quantity of salt consumption and incorporation of moderate physical exercise for >30 minutes was most followed in control of HTN among the attendees of NCD Clinics from the multiple advise given

Thermal Analysis of a 3D Stacked High-Performance Commercial Microprocessor using Face-to-Face Wafer Bonding Technology

3D integration technologies are seeing widespread adoption in the semiconductor industry to offset the limitations and slowdown of two-dimensional scaling. High-density 3D integration techniques such as face-to-face wafer bonding with sub-10 $\mu$m pitch can enable new ways of designing SoCs using all 3 dimensions, like folding a microprocessor design across multiple 3D tiers. However, overlapping thermal hotspots can be a challenge in such 3D stacked designs due to a general increase in power density. In this work, we perform a thorough thermal simulation study on sign-off quality physical design implementation of a state-of-the-art, high-performance, out-of-order microprocessor on a 7nm process technology. The physical design of the microprocessor is partitioned and implemented in a 2-tier, 3D stacked configuration with logic blocks and memory instances in separate tiers (logic-over-memory 3D). The thermal simulation model was calibrated to temperature measurement data from a high-performance, CPU-based 2D SoC chip fabricated on the same 7nm process technology. Thermal profiles of different 3D configurations under various workload conditions are simulated and compared. We find that stacking microprocessor designs in 3D without considering thermal implications can result in maximum die temperature up to 12{\deg}C higher than their 2D counterparts under the worst-case power-indicative workload. This increase in temperature would reduce the amount of time for which a power-intensive workload can be run before throttling is required. However, logic-over-memory partitioned 3D CPU implementation can mitigate this temperature increase by half, which makes the temperature of the 3D design only 6$^\circ$C higher than the 2D baseline. We conclude that using thermal aware design partitioning and improved cooling techniques can overcome the thermal challenges associated with 3D stacking