Computing the F-pure Threshold of Flag Varieties

We compute the $F$-pure threshold of the natural cone over flag varieties in characteristic $p>0$. Our calculations are mainly focused on flag varieties that are arithmetically Gorenstein, but we offer some results in the non-Gorenstein case. Our goal is to determine the $a$-invariant of the cone. As a result, the $F$-pure thresholds we find are independent of the characteristic $p$, hence one immediately gets the value of the log canonical threshold of flags in characteristic 0 as well

Advanced Probabilistic Couplings for Differential Privacy

Differential privacy is a promising formal approach to data privacy, which provides a quantitative bound on the privacy cost of an algorithm that operates on sensitive information. Several tools have been developed for the formal verification of differentially private algorithms, including program logics and type systems. However, these tools do not capture fundamental techniques that have emerged in recent years, and cannot be used for reasoning about cutting-edge differentially private algorithms. Existing techniques fail to handle three broad classes of algorithms: 1) algorithms where privacy depends accuracy guarantees, 2) algorithms that are analyzed with the advanced composition theorem, which shows slower growth in the privacy cost, 3) algorithms that interactively accept adaptive inputs. We address these limitations with a new formalism extending apRHL, a relational program logic that has been used for proving differential privacy of non-interactive algorithms, and incorporating aHL, a (non-relational) program logic for accuracy properties. We illustrate our approach through a single running example, which exemplifies the three classes of algorithms and explores new variants of the Sparse Vector technique, a well-studied algorithm from the privacy literature. We implement our logic in EasyCrypt, and formally verify privacy. We also introduce a novel coupling technique called \emph{optimal subset coupling} that may be of independent interest

UB Knightlines Winter 2010

The UB Knightlines newsletter for Winter 2010. This issue contains articles discussing UB alum Claire Ciscuolo and her business Claire's Corner Copia in New Haven, UB alum Art Landi's business of building product displays and his UB advocacy, UB alum Clodomiro Falcon creating a Hispanic phone directory in Fairfield County with UB, Fone's dental clinic to provide free checkups to local elementary school students, UB's First Year Achievement Floor, the Back-to-School Youth Education Summit, an exhibition on domestic violence, the Bridgeport/Shelton Big Read, alum Steve Ray's experience with UB Basketball in the late 80s, student David James story on marathon running, Sofia Hoflin and the UB women's Soccer team, the induction of alumni into UB's Athletic Hall of Fame, and other campus news

UB Knightlines Spring 2010

The UB Knightlines newsletter for Springer 2010. This issue contains articles discussing the gift of Mr. Shintaro Akatsu which renamed the School of Design, campus financial aid, Chinese language learning within the School of Education, the Engineering School working on next-gen unmanned projectiles for the U.S. Army, fashion merchandising students' visit to the New York city Garment District, student volunteerism on Martin Luther King Day, the addition of Olympian Monica Mesalles to the UB Gymnastics team, a highlight of UB Women's Basketball point guard Sidney Parsons, a recap of UB fall sports, and other campus news

Repression of Esophageal Neoplasia and Inflammatory Signaling by Anti-miR-31 Delivery In Vivo.

BACKGROUND: Overexpression of microRNA-31 (miR-31) is implicated in the pathogenesis of esophageal squamous cell carcinoma (ESCC), a deadly disease associated with dietary zinc deficiency. Using a rat model that recapitulates features of human ESCC, the mechanism whereby Zn regulates miR-31 expression to promote ESCC is examined. METHODS: To inhibit in vivo esophageal miR-31 overexpression in Zn-deficient rats (n = 12-20 per group), locked nucleic acid-modified anti-miR-31 oligonucleotides were administered over five weeks. miR-31 expression was determined by northern blotting, quantitative polymerase chain reaction, and in situ hybridization. Physiological miR-31 targets were identified by microarray analysis and verified by luciferase reporter assay. Cellular proliferation, apoptosis, and expression of inflammation genes were determined by immunoblotting, caspase assays, and immunohistochemistry. The miR-31 promoter in Zn-deficient esophagus was identified by ChIP-seq using an antibody for histone mark H3K4me3. Data were analyzed with t test and analysis of variance. All statistical tests were two-sided. RESULTS: In vivo, anti-miR-31 reduced miR-31 overexpression (P = .002) and suppressed the esophageal preneoplasia in Zn-deficient rats. At the same time, the miR-31 target Stk40 was derepressed, thereby inhibiting the STK40-NF-κΒ-controlled inflammatory pathway, with resultant decreased cellular proliferation and activated apoptosis (caspase 3/7 activities, fold change = 10.7, P = .005). This same connection between miR-31 overexpression and STK40/NF-κΒ expression was also documented in human ESCC cell lines. In Zn-deficient esophagus, the miR-31 promoter region and NF-κΒ binding site were activated. Zn replenishment restored the regulation of this genomic region and a normal esophageal phenotype. CONCLUSIONS: The data define the in vivo signaling pathway underlying interaction of Zn deficiency and miR-31 overexpression in esophageal neoplasia and provide a mechanistic rationale for miR-31 as a therapeutic target for ESCC

Effectiveness of Ledipasvir/Sofosbuvir with/without Ribavarin in Liver Transplant Recipients with Hepatitis C.

Background and Aims: Recurrent infection of hepatitis C virus (HCV) in liver transplant (LT) recipients is universal and associated with significant morbidity and mortality. Methods: We retrospectively evaluated the safety and efficacy of ledipasvir/sofosbuvir with and without ribavirin in LT recipients with recurrent genotype 1 hepatitis C. Results: Eighty-five LT recipients were treated for recurrent HCV with ledipasvir/sofosbuvirwith and without ribavirin for 12 or 24 weeks. The mean (± standard deviation [SD]) time from LT to treatment initiation was 68 (±71) months. The mean (± SD) age of the cohort was 63 (±8.6) years old. Most recipients were male (70%). Baseline alanine transaminase, total bilirubin, and HCV ribonucleic acid (RNA) values (± SD) were 76.8 (±126) mg/dL, 0.8 (±1.3) U/L, and 8,010,421.9 (±12,420,985) IU/mL, respectively. Five of 43 recipients who were treated with ribavirin required drug cessation due to side effects, with 4 of those being anemia complications. No recipient discontinued the ledipasvir/sofosbuvir. Eighty-one percent of recipients had undetectable viral levels at 4 weeks after starting therapy, and all recipients had complete viral suppression at the end of therapy. The sustained viral response at 12 weeks after completion of therapy was 94%. Conclusion : Ledipasvir and sofosbuvir with and without ribavirin therapy is an effective and well-tolerated interferon-free treatment for recurrent HCV infection after LT. Anemia is not uncommon in LT recipients receiving ribavirin

Gesture Recognition from Data Streams of Human Motion Sensor Using Accelerated PSO Swarm Search Feature Selection Algorithm

Human motion sensing technology gains tremendous popularity nowadays with practical applications such as video surveillance for security, hand signing, and smart-home and gaming. These applications capture human motions in real-time from video sensors, the data patterns are nonstationary and ever changing. While the hardware technology of such motion sensing devices as well as their data collection process become relatively mature, the computational challenge lies in the real-time analysis of these live feeds. In this paper we argue that traditional data mining methods run short of accurately analyzing the human activity patterns from the sensor data stream. The shortcoming is due to the algorithmic design which is not adaptive to the dynamic changes in the dynamic gesture motions. The successor of these algorithms which is known as data stream mining is evaluated versus traditional data mining, through a case of gesture recognition over motion data by using Microsoft Kinect sensors. Three different subjects were asked to read three comic strips and to tell the stories in front of the sensor. The data stream contains coordinates of articulation points and various positions of the parts of the human body corresponding to the actions that the user performs. In particular, a novel technique of feature selection using swarm search and accelerated PSO is proposed for enabling fast preprocessing for inducing an improved classification model in real-time. Superior result is shown in the experiment that runs on this empirical data stream. The contribution of this paper is on a comparative study between using traditional and data stream mining algorithms and incorporation of the novel improved feature selection technique with a scenario where different gesture patterns are to be recognized from streaming sensor data