Logistic Regression: Tight Bounds for Stochastic and Online Optimization

The logistic loss function is often advocated in machine learning and statistics as a smooth and strictly convex surrogate for the 0-1 loss. In this paper we investigate the question of whether these smoothness and convexity properties make the logistic loss preferable to other widely considered options such as the hinge loss. We show that in contrast to known asymptotic bounds, as long as the number of prediction/optimization iterations is sub exponential, the logistic loss provides no improvement over a generic non-smooth loss function such as the hinge loss. In particular we show that the convergence rate of stochastic logistic optimization is bounded from below by a polynomial in the diameter of the decision set and the number of prediction iterations, and provide a matching tight upper bound. This resolves the COLT open problem of McMahan and Streeter (2012)

Light-Heavy Symmetry: Geometric Mass Hierarchy for Three Families

The Universal Seesaw pattern coupled with a Light$\leftrightarrow$Heavy symmetry principle leads to the Diophantine equation $\displaystyle N = \sum_{i=1}^Nn_i$, where $n_i\geq 0$ and distinct. Its unique non-trivial solution $(3=0+1+2)$ gives rise to the geometric mass hierarchy $m_W$, $m_W\epsilon$, $m_W\epsilon^2$ for $N=3$ fermion families. This is realized in a model where the hybrid (yet Up$\leftrightarrow$Down symmetric) quark mass relations $m_d m_t \approx m_c^2\leftrightarrow m_u m_b \approx m_s^2$ play a crucial role in expressing the CKM mixings in terms of simple mass ratios, notably $\sin\theta_C \approx {m_c\over m_b}$.Comment: 12 pages, no figures, Revtex fil

Lack of association between estrogen receptor β dinucleotide repeat polymorphism and autoimmune thyroid diseases in Japanese patients

BACKGROUND: The autoimmune thyroid diseases (AITDs), such as Graves' disease (GD) and Hashimoto's thyroiditis (HT), appear to develop as a result of complex interactions between predisposing genes and environmental triggers. Susceptibility to AITDs is conferred by genes in the human leukocyte antigen (HLA) and genes unlinked to HLA, including the CTLA-4 gene. Recently, estrogen receptor (ER) β, located at human chromosome 14q23-24.1, was identifed. We analyzed a dinucleotide (CA)n repeat polymorphism located in the flanking region of ERβ gene in patients with AITDs and in normal subjects. High heterozygosity makes this polymorphism a useful marker in the genetic study of disorders affecting female endocrine systems. We also correlated a ERβ gene microsatellite polymorphism with bone mineral density (BMD) in the distal radius and biochemical markers of bone turnover in patients with GD in remission. RESULTS: Fourteen different alleles were found in 133 patients with GD, 114 patients with HT, and 179 controls subjects. The various alleles were designated as allele(*)1 through allele(*)14 according to the number of the repeats, from 18 to 30. There was no significant difference in the distributions of ERβ alleles between patient groups and controls. Although recent study demonstrated a significant relation between a allele(*)9 in the ERβ gene and BMD in postmenopausal Japanese women, there were no statistically significant interaction between this allele and BMD in the distal radius, nor biochemical markers in patients with GD in remission. CONCLUSIONS: The present results do not support an association between the ERβ microsatellite marker and AITD in the Japanese population. We also suggest that the ERβ microsatellite polymorphism has at most a minor pathogenic importance in predicting the risk of osteoporosis as a complication of GD

Assigning Diagnosis Codes Using Medication History

Diagnosis assignment is the process of assigning disease codes to patients. Automatic diagnosis assignment has the potential to validate code assignments, correct erroneous codes, and register completion. Previous methods build on text-based techniques utilizing medical notes but are inapplicable in the absence of these notes. We propose using patients' medication data to assign diagnosis codes. We present a proof-of-concept study using medical data from an American dataset (MIMIC-III) and Danish nationwide registers to train a machine-learning-based model that predicts an extensive collection of diagnosis codes for multiple levels of aggregation over a disease hierarchy. We further suggest a specialized loss function designed to utilize the innate hierarchical nature of the disease hierarchy. We evaluate the proposed method on a subset of 567 disease codes. Moreover, we investigate the technique's generalizability and transferability by (1) training and testing models on the same subsets of disease codes over the two medical datasets and (2) training models on the American dataset while evaluating them on the Danish dataset, respectively. Results demonstrate the proposed method can correctly assign diagnosis codes on multiple levels of aggregation from the disease hierarchy over the American dataset with recall 70.0% and precision 69.48% for top-10 assigned codes; thereby being comparable to text-based techniques. Furthermore, the specialized loss function performs consistently better than the non-hierarchical state-of-the-art version. Moreover, results suggest the proposed method is language and dataset-agnostic, with initial indications of transferability over subsets of disease codes

Advances in breeding kabuli chickpea in India

Kabuli chickpea (Cicer arietinum L.) varieties released in India before 1989 were adapted only to cool temperatures and long growing season. This limited their cultivation to Northern and Northwestern India. About one third of 17,250-world chickpea germplasm maintained at ICRISAT is kabuli type. These also require relatively cooler growing season for their proper development. This limits the available kabuli gene pool for the Indian chickpea growing regions. Major constraints to increased productivity in India are: lack of suitable varieties, susceptibility to biotic and abiotic stresses and lack of response to irrigations and fertilizers. Indian kabuli varieties do not command,a premium price due to their small seed size. Recent successes in shortening the growing-duration of kabuli types and incorporation of fusarium-wilt resistance from desi types at ICRISAT have helped extend their adaptation to tropical environments. Further breeding efforts should aim at widening the gene pool and enhancing resistance to other root and foliar diseases, pod borer, drought and salinity and increasing the seed siz

Presence of Many Stable Nonhomogeneous States in an Inertial Car-Following Model

A new single lane car following model of traffic flow is presented. The model is inertial and free of collisions. It demonstrates experimentally observed features of traffic flow such as the existence of three regimes: free, fluctuative (synchronized) and congested (jammed) flow; bistability of free and fluctuative states in a certain range of densities, which causes the hysteresis in transitions between these states; jumps in the density-flux plane in the fluctuative regime and gradual spatial transition from synchronized to free flow. Our model suggests that in the fluctuative regime there exist many stable states with different wavelengths, and that the velocity fluctuations in the congested flow regime decay approximately according to a power law in time.Comment: 4 pages, 4 figure

Phase diagram of congested traffic flow: an empirical study

We analyze traffic data from a highway section containing one effective on-ramp. Based on two criteria, local velocity variation patterns and expansion (or nonexpansion) of congested regions, three distinct congested traffic states are identified. These states appear at different levels of the upstream flux and the on-ramp flux, thereby generating a phase diagram of the congested traffic flow. Compared to our earliear reports (including cond-mat/9905292) based on 14 day traffic data, the present paper uses a much larger data set (107 days) and the analysis is carried in a more systematic way, which leads to the modification of a part of interpretation in the earlier reports. Observed traffic states are compared with recent theoretical analyses and both agreeing and disagreeing features are found.Comment: More extensive and systematic version of earlier reports (including cond-mat/9905292). A part of interpretation in earlier reports is modified. 6 two-column pages. To appear in Phys. Rev. E (tentatively scheduled for Oct. 1 issue

cDNA Immunization of Mice with Human Thyroglobulin Generates Both Humoral and T Cell Responses: A Novel Model of Thyroid Autoimmunity

Thyroglobulin (Tg) represents one of the largest known self-antigens involved in autoimmunity. Numerous studies have implicated it in triggering and perpetuating the autoimmune response in autoimmune thyroid diseases (AITD). Indeed, traditional models of autoimmune thyroid disease, experimental autoimmune thyroiditis (EAT), are generated by immunizing mice with thyroglobulin protein in conjunction with an adjuvant, or by high repeated doses of Tg alone, without adjuvant. These extant models are limited in their experimental flexibility, i.e. the ability to make modifications to the Tg used in immunizations. In this study, we have immunized mice with a plasmid cDNA encoding the full-length human Tg (hTG) protein, in order to generate a model of Hashimoto's thyroiditis which is closer to the human disease and does not require adjuvants to breakdown tolerance. Human thyroglobulin cDNA was injected and subsequently electroporated into skeletal muscle using a square wave generator. Following hTg cDNA immunizations, the mice developed both B and T cell responses to Tg, albeit with no evidence of lymphocytic infiltration of the thyroid. This novel model will afford investigators the means to test various hypotheses which were unavailable with the previous EAT models, specifically the effects of hTg sequence variations on the induction of thyroiditis

Macroscopic traffic models from microscopic car-following models

We present a method to derive macroscopic fluid-dynamic models from microscopic car-following models via a coarse-graining procedure. The method is first demonstrated for the optimal velocity model. The derived macroscopic model consists of a conservation equation and a momentum equation, and the latter contains a relaxation term, an anticipation term, and a diffusion term. Properties of the resulting macroscopic model are compared with those of the optimal velocity model through numerical simulations, and reasonable agreement is found although there are deviations in the quantitative level. The derivation is also extended to general car-following models.Comment: 12 pages, 4 figures; to appear in Phys. Rev.

Towards a variational principle for motivated vehicle motion

We deal with the problem of deriving the microscopic equations governing the individual car motion based on the assumptions about the strategy of driver behavior. We suppose the driver behavior to be a result of a certain compromise between the will to move at a speed that is comfortable for him under the surrounding external conditions, comprising the physical state of the road, the weather conditions, etc., and the necessity to keep a safe headway distance between the cars in front of him. Such a strategy implies that a driver can compare the possible ways of his further motion and so choose the best one. To describe the driver preferences we introduce the priority functional whose extremals specify the driver choice. For simplicity we consider a single-lane road. In this case solving the corresponding equations for the extremals we find the relationship between the current acceleration, velocity and position of the car. As a special case we get a certain generalization of the optimal velocity model similar to the "intelligent driver model" proposed by Treiber and Helbing.Comment: 6 pages, RevTeX