25,168 research outputs found
Leading indicators of country risk and currency crises: the Asian experience
Most emerging capital markets in recent years adopted a system that narrowly pegs their currencies’ exchange rates to the U.S. dollar. While such a system has a number of advantages, it makes a country vulnerable to shocks in mobile international capital markets and can lead to reactive strategies that can drive the country into a currency crisis and inflationary recession. ; This article aims to construct an early warning system for international currency crises using financial variables reflecting investors’ expectations and banking distress, which are highly sensitive to changes in the economic environment. The authors use a dynamic factor model that switches between two regimes—representing periods of relative calmness and periods prone to currency crises—to construct leading indicators of country risk and currency crises. ; The method is applied to evaluate the model’s in-sample and out-of-sample performance in anticipating currency crises in the last two decades in Thailand, Indonesia, and Korea. The model successfully produces early signals of these crises, particularly the most severe one, which occurred in 1997. ; The study’s success in signaling future currency crises in real time demonstrates that the model’s “country risk” indicators can be informative tools that allow central banks to take preemptive counterpolicy measures to avoid a crisis or mitigate its severity.
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
An Analysis of the Effectiveness of WeChat Platform in the Ideological and Political Education of College Students
With the popularization and application of WeChat, WeChat platform has become a new carrier of ideological and political education for college students. Using WeChat platform to carry out ideological and political education for college students, which can enhance the timeliness, infectivity and interactivity of ideological and political education effectively. In this regard, we should give full play to the guiding role of ideological and political education in the WeChat platform, be good at refuting all kinds of erroneous social trends of thought with the vivid practice of socialism with Chinese characteristics, and persist in influencing and guiding students with socialist core values
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Current-induced torque driven ferromagnetic resonance in magnetic microstructures
This Dissertation explores the interaction between the magnetisation and
an alternating current in a uniform ferromagnetic system. Diluted magnetic
semiconductors (Ga,Mn)As and (Ga,Mn)(As,P) have been studied. Due to
their strong spin-orbit coupling and well-understood band-structure, these
materials are well-suited to this investigation. The combined effect of spinorbit
coupling and exchange interaction permits the alternating current to
induce an oscillating current-induced torque (CIT) on the magnetisation. In
the frequency range close to the natural resonance frequency of the magnetic
moments (gigahertz), CIT can excite precessional motion of the magnetisation,
a process known as ferromagnetic resonance (FMR).
CIT can be parameterised by an effective magnetic field. By analysing
the lineshape of the measured FMR signals, the magnitude and orientation
of this effective field have been accurately determined. Moreover, the
current-induced fields in these ferromagnetic materials have been observed
with symmetries of the Dresselhaus, and for the first time, Rashba spin-orbit
coupling. A new class of device-scale FMR technique, named as CIT-FMR,
has been established in this Dissertation, with the advantage of simple device
structure (only a resistor is required) and scalability (measurements has been
performed on devices sized from 4 ÎĽm down to 80 nm). This technique is
not only limited to magnetic semiconductors, but can also be transferred to
study other ferromagnetic systems such as ultrathin metal films.
Finally, the CIT-FMR technique is employed to study the magnetic anisotropy
in individual (Ga,Mn)As and (Ga,Mn)(As,P) micro-devices. Devices down
to 80 nm in width have been measured in (Ga,Mn)(As,P), which show strong
strain-relaxation-induced anisotropy, larger than any previously reported
cases on (Ga,Mn)As. Furthermore, due to the tensile-strain on the (Ga,Mn)(As,P)
epilayers, the anisotropy field due to patterning-induced strain-relaxation in
these devices is observed to take the opposite direction compared to that in
the compressively-strained (Ga,Mn)As samples.The research has been funded by the following organisations: Cambridge
Overseas Trusts, Hitachi Cambridge Laboratory, Downing College, EU Grant
FP7-214499 NAMASTE
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