Interference alignment: capacity bounds and practical algorithms for time-varying channels

Wireless communication systems are becoming essential to everyday life. Modern network deployments and protocols are struggling to keep up with these growing demands, due to interference between devices. The recent discovery of interference alignment has shown that, in principle, it may be possible to overcome this interference bottleneck in dense networks. However, most theoretical results are limited to very high signal-to-noise ratios (SNRs) and practical algorithms have only developed for interference alignment via multiple antennas. In this thesis, we develop new capacity bounds for the finite SNR regime by taking advantage of time-varying channel gains. We also explore practical algorithms for parallel single-antenna interference channels, which could arise due to orthogonal frequency-division multiplexing (OFDM). From the theoretical side, we study the phase-fading Gaussian interference channel. We approximate the capacity region in the very strong interference regime to within a constant gap. Our coding schemes combines ideas from ergodic and lattice interference alignment. On the practical side, we develop a matching algorithm for pairing together sub-channels for alignment. This algorithm relies on the concept of maximum weight matching from graph theory. Simulations demonstrate that this algorithm outperforms classical techniques when the network is interference limited

The Consumer Online Purchase Decision: A Model of Consideration Set Formation and Buyer Conversion Rate Across Market Leaders and Market Followers

The impact of low search costs in Internet markets has received a great deal of attention in the academic literature and in the press. While many have argued that the presence of low search costs will lead to strong price competition and vanishing margins, the empirical evidence is decidedly mixed. Reflecting this uncertainty, firms have taken radically different strategies with regard to facilitating search across sites. Some firms have actively blocked or attempted to limit price search (e.g., by refusing to be listed at shopbots) while others have actively encouraged price search. In this research we use a unique dataset of detailed customer survey data to analyze the impact of consumer search behavior on the formation of consideration sets and the consumer’s ultimate purchase decision. We find that while searching across market leaders is not detrimental for market followers, searching across market followers is somewhat detrimental for market leaders. These results suggest that today’s market leaders may be at risk from increased consumer adoption of broad search technologies such as Internet shopbots

Entity Matching and Disambiguation Across Multiple Knowledge Graphs

Knowledge graphs are considered an important representation that lie between free text on one hand and fully-structured relational data on the other. Knowledge graphs are a back-bone of many applications on the Web. With the rise of many large-scale open-domain knowledge graphs like Freebase, DBpedia, and Yago, various applications including document retrieval, question answering, and data integration have been relying on them. In this thesis, We are primarily interested in knowledge graphs from the perspective of integrating disparate heterogeneous sources, with an eye towards applications such as document retrieval and question answering. Integrating different knowledge graphs is very important for enriching the knowledge shared among them. The core part of this integration process is matching entities across the knowledge graphs. The biggest challenge to entity matching is the ambiguity. The obvious solution is to make use of the graph structure and entity neighbourhoods for matching and disambiguating entities. We formalize the entity matching problem and present the rst large-scale dataset, Ambiguous DBpedia-Wikidata, for this task based on exiting cross-ontology links between DBpedia and Wikidata, focused on several hundred thousand ambiguous entities. We propose an entity matching framework that is capable of disambiguating entities across different knowledge graphs. The framework consists of fuzzy string matcher and graph embedding-based matcher. Using a classifi cation-based approach, we find that a simple multi-layered perceptron based on representations derived from RDF2VEC graph embeddings of entities in each knowledge graph is sufficient to achieve high accuracy, with only limited training data. The contribution of our work is both a large dataset for examining this problem and strong baselines on which future work can be based. We also present SimpleDBpediaQA, a new benchmark dataset for simple question answering over knowledge graphs that was created by mapping SimpleQuestions entities and predicates from Freebase to DBpedia. We show how entity matching using manual annotations can be used for migrating datasets across knowledge graphs. Although this mapping is conceptually straightforward, there are a number of nuances that make the task non-trivial, owing to the different conceptual organizations of the two knowledge graphs. Finally, if manual annotations are scarce, we show how our entity matching framework can be used to generate free annotations to train our model and then use it for disambiguation. In that essence, we introduce SimpleQuestions++, a new question answering benchmark that have all questions linked to Freebase, DBpedia, and Wikidata

Evaluating the baseline hemoglobin, albumin, lymphocyte, and platelet (HALP) score in the United States adult population and comorbidities: an analysis of the NHANES

Introduction: As a composite immunonutritional biomarker, the Hemoglobin, Albumin, Lymphocyte, Platelet (HALP) score has shown promise in assessing a patient\u27s overall health status by integrating several routinely collected laboratory indicators. This biomarker has been examined in many different populations of patients and disease states (i.e., cancer), but an integrated, universal rubric using standardized thresholds has not thus far been developed. Pre-existing large population-based databases represent an ideal source to examine the distribution of HALP and the influence of diverse health statuses on this score. Methods: We conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) between 2017–2020, evaluating 8,245 participants across numerous demographic, socioeconomic, and health-related variables. Univariate and multivariate linear regression analyses assessed the associations between HALP scores and these factors. Results: Our findings revealed significant associations between HALP scores and various demographic, socioeconomic, and health conditions. The median HALP score among the representative population was 49.0, with varying median scores across different groups and normal reference ranges for males and females. Multivariate regression analysis showed that anemia treatment, age over 65 years, weak/failing kidneys, and cancer were independent risk factors associated with lower HALP scores. Male participants demonstrated higher HALP scores than female participants, and age was inversely related to HALP. Moreover, HALP scores were negatively associated with the number of comorbidities. Conclusion/discussion: This study set out to explore the HALP score from a population-based perspective, uncovering notable associations that offer vital insights into the score\u27s clinical relevance and future applications. By determining a median HALP score of 49.0 and normal reference ranges within our diverse, representative sample, we establish a robust foundation for researchers to refine optimal HALP applications and thresholds. Considering the growing focus on personalized medicine, HALP holds promise as a prognostic tool, enabling clinicians to comprehend their patients\u27 immunonutritional status better and deliver customized care

Preliminary efficacy of a brief family intervention to prevent declining quality of life secondary to parental bone marrow transplantation

The primary purpose of this research was to develop and evaluate the efficacy and feasibility of a brief, cost-effective family-focused intervention to promote adaptive coping and quality of life throughout a parent's bone marrow transplantation (BMT). Targeted outcomes were cohesion, decreased use of avoidance coping, open communication and effective management of emotional distress. Participants included an intervention group of 31 families and 29 families in a control group who received usual care. Each family included the BMT recipient, a partner/caregiver and children 10-18 years old. The intervention included two dyadic sessions for the BMT recipient and the partner/caregiver, one individual session for the caregiver and two digital video discs (DVDs) for children. Statistical analyses indicated that the intervention had a positive impact on at least one aspect of the adaptation of each family member. Caregivers reported the most distress but benefitted least from the intervention, whereas recipients and children reported improvement in distress. Ratings of satisfaction/acceptability were high, with 97% responding that they would recommend the intervention to others. Plans for future research include increased intervention intensity for the caregiver, a larger more diverse sample and implementation over an extended period post BMT

Altered regulation of metabolic pathways in human lung cancer discerned by 13C stable isotope-resolved metabolomics (SIRM)

<p>Abstract</p> <p>Background</p> <p>Metabolic perturbations arising from malignant transformation have not been systematically characterized in human lung cancers <it>in situ</it>. Stable isotope resolved metabolomic analysis (SIRM) enables functional analysis of gene dysregulations in lung cancer. To this purpose, metabolic changes were investigated by infusing uniformly labeled ¹³C-glucose into human lung cancer patients, followed by resection and processing of paired non-cancerous lung and non small cell carcinoma tissues. NMR and GC-MS were used for ¹³C-isotopomer-based metabolomic analysis of the extracts of tissues and blood plasma.</p> <p>Results</p> <p>Many primary metabolites were consistently found at higher levels in lung cancer tissues than their surrounding non-cancerous tissues. ¹³C-enrichment in lactate, Ala, succinate, Glu, Asp, and citrate was also higher in the tumors, suggesting more active glycolysis and Krebs cycle in the tumor tissues. Particularly notable were the enhanced production of the Asp isotopomer with three ¹³C-labeled carbons and the buildup of ¹³C-2,3-Glu isotopomer in lung tumor tissues. This is consistent with the transformations of glucose into Asp or Glu via glycolysis, anaplerotic pyruvate carboxylation (PC), and the Krebs cycle. PC activation in tumor tissues was also shown by an increased level of pyruvate carboxylase mRNA and protein.</p> <p>Conclusion</p> <p>PC activation – revealed here for the first time in human subjects – may be important for replenishing the Krebs cycle intermediates which can be diverted to lipid, protein, and nucleic acid biosynthesis to fulfill the high anabolic demands for growth in lung tumor tissues. We hypothesize that this is an important event in non-small cell lung cancer and possibly in other tumor development.</p

Adding bendamustine to melphalan before ASCT improves CR rate in myeloma vs. melphalan alone: A randomized phase-2 trial.

Definite cure remains exceptional in myeloma patients even after high-dose chemotherapy (HDCT) with melphalan (Mel) and autologous stem cell transplantation (ASCT). Thus, improving efficacy of HDCT in MM remains an unresolved issue. This randomized phase II trial compared standard 200 mg/m2 Mel HDCT to experimental HDCT with 200 mg/m2 bendamustine, given both at days -4 and -3, combined with 100 mg/m2 melphalan at days -2 and -1 (BenMel) before ASCT as first-line consolidation in myeloma patients. The primary endpoint aimed to identify at least a 15% improvement in the complete remission rate (stringent CR + CR) after HDCT with BenMel compared with Mel alone. A total of 120 MM patients were 1:1 randomized. The rate of sCR/CR after ASCT was higher in BenMel than in Mel treated patients (70.0% vs. 51.7%; p = 0.039). Three patients in the BenMel group (5.0%) had reversible acute renal insufficiency compared with none in Mel patients. Minimal residual disease negativity (<10-5) by flow cytometry was observed in 26 (45.6%) BenMel patients and 22 (37.9%) in the Mel group (p = 0.375). Our data suggest that BenMel HDCT is safe and improves the sCR/CR rate compared with standard Mel alone

A phase I trial of Flavopiridol in relapsed multiple myeloma

PURPOSE: Flavopiridol is primarily a cyclin-dependent kinase-9 inhibitor, and we performed a dose escalation trial to determine the maximum tolerated dose and safety and generate a pharmacokinetic (PK) profile. METHODS: Patients with a diagnosis of relapsed myeloma after at least two prior treatments were included. Flavopiridol was administered as a bolus and then continuous infusion weekly for 4 weeks in a 6-week cycle. RESULTS: Fifteen patients were treated at three dose levels (30 mg/m(2) bolus, 30 mg/m(2) CIV to 50 mg/m(2) bolus, and 50 mg/m(2) CIV). Cytopenias were significant, and elevated transaminases (grade 4 in 3 patients, grade 3 in 4 patients, and grade 2 in 3 patients) were noted but were transient. Diarrhea (grade 3 in 6 patients and grade 2 in 5 patients) did not lead to hospital admission. There were no confirmed partial responses although one patient with t(4;14) had a decrease in his monoclonal protein >50 % that did not persist. PK properties were similar to prior publications, and immunohistochemical staining for cyclin D1 and phospho-retinoblastoma did not predict response. CONCLUSIONS: Flavopiridol as a single agent given by bolus and then infusion caused significant diarrhea, cytopenias, and transaminase elevation but only achieved marginal responses in relapsed myelom