1,131 research outputs found
Beyond Convexity: Stochastic Quasi-Convex Optimization
Stochastic convex optimization is a basic and well studied primitive in
machine learning. It is well known that convex and Lipschitz functions can be
minimized efficiently using Stochastic Gradient Descent (SGD). The Normalized
Gradient Descent (NGD) algorithm, is an adaptation of Gradient Descent, which
updates according to the direction of the gradients, rather than the gradients
themselves. In this paper we analyze a stochastic version of NGD and prove its
convergence to a global minimum for a wider class of functions: we require the
functions to be quasi-convex and locally-Lipschitz. Quasi-convexity broadens
the con- cept of unimodality to multidimensions and allows for certain types of
saddle points, which are a known hurdle for first-order optimization methods
such as gradient descent. Locally-Lipschitz functions are only required to be
Lipschitz in a small region around the optimum. This assumption circumvents
gradient explosion, which is another known hurdle for gradient descent
variants. Interestingly, unlike the vanilla SGD algorithm, the stochastic
normalized gradient descent algorithm provably requires a minimal minibatch
size
On Graduated Optimization for Stochastic Non-Convex Problems
The graduated optimization approach, also known as the continuation method,
is a popular heuristic to solving non-convex problems that has received renewed
interest over the last decade. Despite its popularity, very little is known in
terms of theoretical convergence analysis. In this paper we describe a new
first-order algorithm based on graduated optimiza- tion and analyze its
performance. We characterize a parameterized family of non- convex functions
for which this algorithm provably converges to a global optimum. In particular,
we prove that the algorithm converges to an {\epsilon}-approximate solution
within O(1/\epsilon^2) gradient-based steps. We extend our algorithm and
analysis to the setting of stochastic non-convex optimization with noisy
gradient feedback, attaining the same convergence rate. Additionally, we
discuss the setting of zero-order optimization, and devise a a variant of our
algorithm which converges at rate of O(d^2/\epsilon^4).Comment: 17 page
ESTSS at 20 years: "a phoenix gently rising from a lava flow of European trauma"
Roderick J. Ørner, who was President between 1997 and 1999, traces the phoenix-like origins of the European Society for Traumatic Stress Studies (ESTSS) from an informal business meeting called during the 1st European Conference on Traumatic Stress (ECOTS) in 1987 to its emergence into a formally constituted society. He dwells on the challenges of tendering a trauma society within a continent where trauma has been and remains endemic. ESTSS successes are noted along with a number of personal reflections on activities that give rise to concern for the present as well as its future prospects. Denial of survivors' experiences and turning away from survivors' narratives by reframing their experiences to accommodate helpers' theory-driven imperatives are viewed with alarm. Arguments are presented for making human rights, memory, and ethics core elements of a distinctive European psycho traumatology, which will secure current ESTSS viability and future integrity
PMS5 PERSISTENCE WITH BISPHOSPHONATE THERAPY AND RISK OF HIP FRACTURE
Primer pla d'un senzill edifici d'habitatges
de principis del s. XIX.
Consta de planta baixa i quatre plantes pis,
separades cada una de les plantes per impostes.
S'estructura sobre la base d'eixos de simetria verticals
Пережити негаразди війни. Короткі поради щодо виживання для цивільних осіб в умовах військового стресу.
As the Ukrainian war rages, countless civilians are exposed to shelling, bombardments, relocation, loss of live and other modalities of war-related adversities.
Whilst much has been learned about the late psychological outcome of traumatic exposure, , for most civilians the task at hand now is to successfully survive whatever level of adversity and horror they have been exposed to; efficiently protect themselves and others around them, and ultimately emerge victorious and minimally scathed by war.
This document offers an easy-to-follow survival advice.
We start by defining war stress and its many facets, review successful and less successful ways to mitigate war stress, outline critical aspects of life that must be dealt with during war, and provide a simple self-assessment tool of one’s achievement and resilience.У той час, як вирує українська війна, незліченна кількість мирних жителів зазнає обстрілів, бомбардування, переселення, загибелі людей та інших негараздів, пов'язаних з війною.
Хоча багато відомо про відтерміновані психологічні наслідки травматичного впливу, для більшості цивільних осіб зараз стоїть завдання успішно пережити скруту та жахи будь-якого рівня, яких вони зазнають; ефективно захистити себе та оточуючих і зрештою вийти переможцями та мінімізувати втрати від війни.
Цей документ пропонує прості поради щодо виживання.
Ми почнемо з визначення військового стресу та його численних аспектів, розглянемо успішні та менш успішні способи пом'якшення стресу від війни, намітимо критичні аспекти життя, з якими необхідно справлятись під час війни, та запропонуємо простий інструмент самооцінки своїх досягнень та стійкості
The spatio-temporal distribution of lightning over Israel and the neighboring area and its relation to regional synoptic systems
The spatio-temporal distribution of lightning flashes over Israel and the neighboring area and its relation to the regional synoptic systems has been studied, based on data obtained from the Israel Lightning Location System (ILLS) operated by the Israel Electric Corporation (IEC). The system detects cloud-to-ground lightning discharges in a range of ~500 km around central Israel (32.5° N, 35° E). The study period was defined for annual activity from August through July, for 5 seasons in the period 2004–2010. <br><br> The spatial distribution of lightning flash density indicates the highest concentration over the Mediterranean Sea, attributed to the contribution of moisture as well as sensible and latent heat fluxes from the sea surface. Other centers of high density appear along the coastal plain, orographic barriers, especially in northern Israel, and downwind from the metropolitan area of Tel Aviv, Israel. The intra-annual distribution shows an absence of lightning during the summer months (JJA) due to the persistent subsidence over the region. The vast majority of lightning activity occurs during 7 months, October to April. Although over 65 % of the rainfall in Israel is obtained during the winter months (DJF), only 35 % of lightning flashes occur in these months. October is the richest month, with 40 % of total annual flashes. This is attributed both to tropical intrusions, i.e., Red Sea Troughs (RST), which are characterized by intense static instability and convection, and to Cyprus Lows (CLs) arriving from the west. <br><br> Based on daily study of the spatial distribution of lightning, three patterns have been defined; "land", "maritime" and "hybrid". CLs cause high flash density over the Mediterranean Sea, whereas some of the RST days are typified by flashes over land. The pattern defined "hybrid" is a combination of the other 2 patterns. On CL days, only the maritime pattern was noted, whereas in RST days all 3 patterns were found, including the maritime pattern. It is suggested that atmospheric processes associated with RST produce the land pattern. Hence, the occurrence of a maritime pattern in days identified as RST reflects an "apparent RST". The hybrid pattern was associated with an RST located east of Israel. This synoptic type produced the typical flash maximum over the land, but the upper-level trough together with the onshore winds it induced over the eastern coast of the Mediterranean resulted in lightning activity over the sea as well, similar to that of CLs. <br><br> It is suggested that the spatial distribution patterns of lightning may better identify the synoptic system responsible, a CL, an "active RST" or an "apparent RST". The electrical activity thus serves as a "fingerprint" for the synoptic situation responsible for its generation
Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates
In this paper, we provide a novel construction of the linear-sized spectral
sparsifiers of Batson, Spielman and Srivastava [BSS14]. While previous
constructions required running time [BSS14, Zou12], our
sparsification routine can be implemented in almost-quadratic running time
.
The fundamental conceptual novelty of our work is the leveraging of a strong
connection between sparsification and a regret minimization problem over
density matrices. This connection was known to provide an interpretation of the
randomized sparsifiers of Spielman and Srivastava [SS11] via the application of
matrix multiplicative weight updates (MWU) [CHS11, Vis14]. In this paper, we
explain how matrix MWU naturally arises as an instance of the
Follow-the-Regularized-Leader framework and generalize this approach to yield a
larger class of updates. This new class allows us to accelerate the
construction of linear-sized spectral sparsifiers, and give novel insights on
the motivation behind Batson, Spielman and Srivastava [BSS14]
Contextual Object Detection with a Few Relevant Neighbors
A natural way to improve the detection of objects is to consider the
contextual constraints imposed by the detection of additional objects in a
given scene. In this work, we exploit the spatial relations between objects in
order to improve detection capacity, as well as analyze various properties of
the contextual object detection problem. To precisely calculate context-based
probabilities of objects, we developed a model that examines the interactions
between objects in an exact probabilistic setting, in contrast to previous
methods that typically utilize approximations based on pairwise interactions.
Such a scheme is facilitated by the realistic assumption that the existence of
an object in any given location is influenced by only few informative locations
in space. Based on this assumption, we suggest a method for identifying these
relevant locations and integrating them into a mostly exact calculation of
probability based on their raw detector responses. This scheme is shown to
improve detection results and provides unique insights about the process of
contextual inference for object detection. We show that it is generally
difficult to learn that a particular object reduces the probability of another,
and that in cases when the context and detector strongly disagree this learning
becomes virtually impossible for the purposes of improving the results of an
object detector. Finally, we demonstrate improved detection results through use
of our approach as applied to the PASCAL VOC and COCO datasets
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features
One-class support vector machine (OC-SVM) for a long time has been one of the
most effective anomaly detection methods and extensively adopted in both
research as well as industrial applications. The biggest issue for OC-SVM is
yet the capability to operate with large and high-dimensional datasets due to
optimization complexity. Those problems might be mitigated via dimensionality
reduction techniques such as manifold learning or autoencoder. However,
previous work often treats representation learning and anomaly prediction
separately. In this paper, we propose autoencoder based one-class support
vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier
features to approximate the radial basis kernel, into deep learning context by
combining it with a representation learning architecture and jointly exploit
stochastic gradient descent to obtain end-to-end training. Interestingly, this
also opens up the possible use of gradient-based attribution methods to explain
the decision making for anomaly detection, which has ever been challenging as a
result of the implicit mappings between the input space and the kernel space.
To the best of our knowledge, this is the first work to study the
interpretability of deep learning in anomaly detection. We evaluate our method
on a wide range of unsupervised anomaly detection tasks in which our end-to-end
training architecture achieves a performance significantly better than the
previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201
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