Miracle-dz/UniFAR-FTUs huggingface dataset:
Technologies for Proteomic and Genomic Biomarker Analysis

In the first part of this dissertation, we systematically validated the application of molecular weight cut-off ultrafiltration in separation and enrichment of low-molecular-weight peptides from human serum. Under optimized conditions, both free-phase and bound LMW peptides could be separated and enriched. The method proved to be highly efficient and reproducible coupled with MALDI-TOF MS proteomic pattern analysis. Three marker peaks were found to be eligible for distinguishing normal and ovarian cancer samples. A novel organic solvent precipitation method coupled with enzymatic deglycosylation was also developed for biomarker detection from human serum. This method allowed us to generate reproducible free-phase peptide patterns comparing with the ultrafiltration method. A potential marker was found up-regulated in benign and ovarian cancer patients. It was further identified as des-alanine-fibrinopeptide A using LC tandem mass spectrometry. In the second part of this dissertation, a new sample preparation procedure was developed to improve the MALDI-TOF analysis of low-concentration oligonucleotides. The oligonucleotide solutions are first dispensed and allow shrinking onto a small spot on an anchoring target. A small volume (0.1uL) of saturated 3-HPA matrix solution is then
Mathematisches Forschungsinstitut Oberwolfach Differentialgeometrie Im Grossen

The meeting continued the biannual conference series Differentialgeome-trie im Großen at the MFO which was established in the 60's by Klingenberg and Chern. Global Riemannian geometry with its connections to topology, geometric group theory and geometric analysis remained an important focus of the conference. Special emphasis was given to the Ricci flow and its applications and to the geometry of metric spaces with curvature conditions.
Formal verification of a concurrent bounded queue in a weak memory model

We use Cosmo, a modern concurrent separation logic, to formally specify and verify an implementation of a multiple-producer multiple-consumer concurrent queue in the setting of the Multicore OCaml weak memory model. We view this result as a demonstration and experimental verification of the manner in which Cosmo allows modular and formal reasoning about advanced concurrent data structures. In particular, we show how the joint use of logically atomic triples and of Cosmo's views makes it possible to describe precisely in the specification the interaction between the queue library and the weak memory model.
Educational technology in developing countries: a review of the evidence

: the emergence of educational technology (“EdTech”) in developing countries has been received as a promising avenue to address some of the most challenging policy questions within educational systems. In this paper, I review and synthesize all existing studies with credible causal identification frameworks of EdTech interventions in developing countries. While other studies review the evidence for EdTech interventions in developed countries, there is currently no equivalent study for developing contexts, in spite of the rising number of studies being produced. I classify studies into four thematic categories based on the type of EdTech intervention analyzed: (1) access to technology, (2) technology-enabled behavioral interventions, (3) improvements to instruction, and (4) self-led learning. I find that EdTech interventions centered around self-led learning and improvements to instruction are the most effective forms of EdTech at raising learning outcomes. Similarly, technology-enabled behavioral interventions are less promising for generating large effects but highly cost-effective given their typically low marginal costs. While expanding access to technology alone is not sufficient to improve learning, it is a necessary first step for some other types of interventions. More broadly, the overall success of interventions rests on the thoughtful customization of the EdTech solution to the policy constraints at hand. Finally, EdTech interventions across all thematic areas can and should act as complements by leveraging their respective comparative advantages to address deficiencies within educational systems in developing countries.
Nonlinear Partial Differential Equations of Mixed Hyperbolic-Elliptic Type Arising in Mechanics and Geometry Project Summary

The project is devoted to a mathematical study of the nonlinear partial differential equations of mixed hyperbolic-elliptic type arising in mechanics and geometry. Such issues occur naturally in both multidimensional gas dynamics and isometric embedding problems in differential geometry. The proposers will collaborate in a three year effort to advance the mathematical understanding of these equations, their applications, and numerical resolution. Success in this project will advance knowledge of this fundamental area of mathematics and mechanics and will introduce a new generation of researchers to the outstanding problems in the field.
Extreme relativistic reflection in the active galaxy ESO 033-G002

We present the first high signal-to-noise broad-band X-ray spectrum of the radio-quiet type-2 Seyfert ESO 033-G002, combining data from XMM–Newton and NuSTAR. The nuclear X-ray spectrum is complex, showing evidence for both neutral and ionized absorption, as well as reflection from both the accretion disc and more distant material, but our broad-band coverage allows us to disentangle all of these different components. The total neutral column during this epoch is NH ∼ (5−6) × 1022 $\rm \, cm^{-2}$, consistent with the optical classification of ESO 033-G002 as a type-2 Seyfert but not so large as to prevent us from robustly determining the properties of the innermost accretion flow. The ionized absorption – dominated by lines from Fe xxv and Fe xxvi – reveals a moderately rapid outflow (vout ∼ 5400 $\rm \, km~s^{-1}$) which has a column comparable to the neutral absorption. We find the disc reflection from the innermost regions to be extreme, with a reflection fraction of Rfrac ∼ 5. This requires strong gravitational lightbending and, in turn, both an extremely compact corona (within ∼2 RG of the black hole) and a rapidly rotating black hole (a* > 0.96). Despite this tight size constraint, with a temperature of kTe = 40–70 keV the X-ray corona in ESO 033-G002 appears similar to other active galactic nucle in terms of its placement in the compactness–temperature plane, consistent with sitting close to the limit determined by runaway pair production. Finally, combining X-ray spectroscopy, timing, and updated optical spectroscopy, we also estimate the mass of the black hole to be log[MBH/M⊙] ∼ 7.0–7.5.
Multitask-based joint learning approach to robust ASR for radio communication speech

To realize robust End-to-end Automatic Speech Recognition (E2E ASR) under radio communication condition, we propose a multitask-based method to jointly train a Speech Enhancement (SE) module as the front-end and an E2E ASR model as the back-end in this paper. One of the advantages of the proposed method is that the entire system can be trained from scratch. Different from prior works, either component here doesn't need to perform pre-training and fine-tuning processes separately. Through analysis, we found that the success of the proposed method lies in the following aspects. First, multitask learning is essential, that is, the SE network is not only learned to produce more intelligible speech, it is also aimed to generate speech that is beneficial to recognition. Secondly, we also found speech phase preserved from noisy speech is critical for an improved ASR performance. Thirdly, we propose a dual-channel data augmentation training method to obtain further improve-ment. Specifically, we combine the clean and enhanced speech to train the whole system. We evaluate the proposed method on the RATS English data set, achieving a relative WER reduction of 4.6% with the joint training method, and up to a relative WER reduction of 11.2% with the proposed data augmentation method.
Geometric rigidity in variable domains and derivation of linearized models for elastic materials with free surfaces

We present a quantitative geometric rigidity estimate in dimensions $d=2,3$ generalizing the celebrated result by Friesecke, James, and M\"uller to the setting of variable domains. Loosely speaking, we show that for each $y \in H^1(U;\mathbb{R}^d)$ and for each connected component of a smooth open, bounded set $U \subset \mathbb{R}^d$, the $L^2$-distance of $\nabla y$ from a single rotation can be controlled up to a constant by its $L^2$-distance from the group $SO(d)$, with the constant not depending on the precise shape of $U$, but only on an integral curvature functional related to $\partial U$. We further show that for linear strains the estimate can be refined, leading to a uniform control independent of the set $U$. The estimate can be used to establish compactness in the space of generalized special functions of bounded deformation ($GSBD^2$) for sequences of displacements related to deformations with uniformly bounded elastic energy. As an application, we rigorously derive linearized models for nonlinearly elastic materials with free surfaces by means of $\Gamma$-convergence. In particular, we study energies related to epitaxially strained crystalline films and to the formation of material voids inside elastically stressed solids.
Improving the Authentication with Built-in Camera Protocol Using Built-in Motion Sensors: A Deep Learning Solution

In this paper, we propose an enhanced version of the Authentication with Built-in Camera (ABC) protocol by employing a deep learning solution based on built-in motion sensors. The standard ABC protocol identifies mobile devices based on the photo-response non-uniformity (PRNU) of the camera sensor, while also considering QR-code-based meta-information. During registration, users are required to capture photos using their smartphone camera. The photos are sent to a server that computes the camera fingerprint, storing it as an authentication trait. During authentication, the user is required to take two photos that contain two QR codes presented on a screen. The presented QR code images also contain a unique probe signal, similar to a camera fingerprint, generated by the protocol. During verification, the server computes the fingerprint of the received photos and authenticates the user if (i) the probe signal is present, (ii) the metadata embedded in the QR codes is correct and (iii) the camera fingerprint is identified correctly. However, the protocol is vulnerable to forgery attacks when the attacker can compute the camera fingerprint from external photos, as shown in our preliminary work. Hence, attackers can easily remove their PRNU from the authentication photos without completely altering the probe signal, resulting in attacks that bypass the defense systems of the ABC protocol. In this context, we propose an enhancement to the ABC protocol, using motion sensor data as an additional and passive authentication layer. Smartphones can be identified through their motion sensor data, which, unlike photos, is never posted by users on social media platforms, thus being more secure than using photographs alone. To this end, we transform motion signals into embedding vectors produced by deep neural networks, applying Support Vector Machines for the smartphone identification task. Our change to the ABC protocol results in a multi-modal protocol that lowers the false acceptance rate for the attack proposed in our previous work to a percentage as low as 0.07%. In this paper, we present the attack that makes ABC vulnerable, as well as our multi-modal ABC protocol along with relevant experiments and results.
Efficient Neural Causal Discovery without Acyclicity Constraints

Learning the structure of a causal graphical model using both observational and interventional data is a fundamental problem in many scientific fields. A promising direction is continuous optimization for score-based methods, which efficiently learn the causal graph in a data-driven manner. However, to date, those methods require constrained optimization to enforce acyclicity or lack convergence guarantees. In this paper, we present ENCO, an efficient structure learning method for directed, acyclic causal graphs leveraging observational and interventional data. ENCO formulates the graph search as an optimization of independent edge likelihoods, with the edge orientation being modeled as a separate parameter. Consequently, we can provide convergence guarantees of ENCO under mild conditions without constraining the score function with respect to acyclicity. In experiments, we show that ENCO can efficiently recover graphs with hundreds of nodes, an order of magnitude larger than what was previously possible, while handling deterministic variables and latent confounders.
On the Scalability of Informed Importance Tempering

Informed MCMC methods have been proposed as scalable solutions to Bayesian posterior computation on high-dimensional discrete state spaces. We study a class of MCMC schemes called informed importance tempering (IIT), which combine importance sampling and informed local proposals. Spectral gap bounds for IIT estimators are obtained, which demonstrate the remarkable scalability of IIT samplers for unimodal target distributions. The theoretical insights acquired in this note provide guidance on the choice of informed proposals in model selection and the use of importance sampling in MCMC methods.
Out of the Shadows: Analyzing Anonymous' Twitter Resurgence during the 2020 Black Lives Matter Protests

Until recently, there had been little notable activity from the once prominent hacktivist group, Anonymous. The group, responsible for activist-based cyber attacks on major businesses and governments, appeared to have fragmented after key members were arrested in 2013. In response to the major Black Lives Matter (BLM) protests that occurred after the murder of George Floyd, however, reports indicated that the group was back. To examine this apparent resurgence, we conduct a large-scale study of Anonymous affiliates on Twitter. We first use machine learning to identify a significant network of more than 33,000 Anonymous accounts and use topic modelling of tweets collected from these accounts to find evidence of sustained interest in topics related to BLM. We then use sentiment analysis on tweets focused on these topics, finding evidence of a united approach amongst the group, with positive tweets typically being used to express support towards BLM and negative tweets typically being used to criticize police actions. Finally, we examine the presence of automation in the network, identifying indications of bot-like behavior across the majority of Anonymous accounts. These findings show that whilst the group has seen a resurgence during the protests, bot activity may be responsible for exaggerating the extent of this resurgence.
Nonlinear transformation of complex amplitudes via quantum singular value transformation

Due to the linearity of quantum operations, it is not straightforward to implement nonlinear transformations on a quantum computer, making some practical tasks like a neural network hard to be achieved. In this work, we define a task called nonlinear transformation of complex amplitudes and provide an algorithm to achieve this task. Specifically, we construct a block-encoding of complex amplitudes from a state preparation oracle. This allows us to transform the complex amplitudes by using quantum singular value transformation. We evaluate the required overhead in terms of input dimension and precision, which reveals that the algorithm depends on the roughly square root of input dimension and achieves an exponential speedup on precision compared with previous work. We also discuss its possible applications to quantum machine learning, where complex amplitudes encoding classical or quantum data are processed by the proposed method. This paper provides a promising way to introduce highly complex nonlinearity of the quantum states, which is essentially missing in quantum mechanics.
Unsupervised Detection of Adversarial Examples with Model Explanations

Deep Neural Networks (DNNs) have shown remarkable performance in a diverse range of machine learning applications. However, it is widely known that DNNs are vulnerable to simple adversarial perturbations, which causes the model to incorrectly classify inputs. In this paper, we propose a simple yet effective method to detect adversarial examples, using methods developed to explain the model's behavior. Our key observation is that adding small, humanly imperceptible perturbations can lead to drastic changes in the model explanations, resulting in unusual or irregular forms of explanations. From this insight, we propose an unsupervised detection of adversarial examples using reconstructor networks trained only on model explanations of benign examples. Our evaluations with MNIST handwritten dataset show that our method is capable of detecting adversarial examples generated by the state-of-the-art algorithms with high confidence. To the best of our knowledge, this work is the first in suggesting unsupervised defense method using model explanations.
On Uniqueness of Submaximally Symmetric Parabolic Geometries

Among (regular, normal) parabolic geometries of type $(G,P)$, there is a locally unique maximally symmetric structure and it has symmetry dimension $\dim(G)$. The symmetry gap problem concerns the determination of the next realizable (submaximal) symmetry dimension. When $G$ is a complex or split-real simple Lie group of rank at least three or when $(G,P) = (G_2,P_2)$, we establish a local uniqueness result for submaximally symmetric structures of type $(G,P)$.
Patterns of Patterns

This paper shows how we combine and adapt methods from elite training, future studies, and collaborative design, and apply them to address significant problems in social networks. We focus on three such methods: we use Project Action Reviews to implement social perception, Causal Layered Analysis to implement social cognition, and Design Pattern Languages to implement social action. We present the results of two studies: firstly, we use Causal Layered Analysis to explore the ways in which the design pattern discourse has been evolving. Secondly, to illustrate the three methods in combination, we develop a case study, showing how we applied the methods to bootstrap a distributed cross-disciplinary research seminar. Building on these analyses, we elaborate several scenarios for the future use of design patterns in large-scale distributed collaboration. Our case study suggests ways in which progress could be made towards realizing these scenarios. We conclude that the combination of methods is robust to uncertainty, insofar as they support adaptations as circumstances change, and incorporate diverse perspectives. In particular, we show how methods drawn from other domains enrich and are enriched by design patterns; we believe the analysis will be of interest to all of the communities whose methods we draw upon.
Multi-Stream Transformers

Transformer-based encoder-decoder models produce a fused token-wise representation after every encoder layer. We investigate the effects of allowing the encoder to preserve and explore alternative hypotheses, combined at the end of the encoding process. To that end, we design and examine a $\textit{Multi-stream Transformer}$ architecture and find that splitting the Transformer encoder into multiple encoder streams and allowing the model to merge multiple representational hypotheses improves performance, with further improvement obtained by adding a skip connection between the first and the final encoder layer.
Spinning Sequence-to-Sequence Models with Meta-Backdoors

We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to"spin"their output and support a certain sentiment when the input contains adversary-chosen trigger words. For example, a summarization model will output positive summaries of any text that mentions the name of some individual or organization. We introduce the concept of a"meta-backdoor"to explain model-spinning attacks. These attacks produce models whose output is valid and preserves context, yet also satisfies a meta-task chosen by the adversary (e.g., positive sentiment). Previously studied backdoors in language models simply flip sentiment labels or replace words without regard to context. Their outputs are incorrect on inputs with the trigger. Meta-backdoors, on the other hand, are the first class of backdoors that can be deployed against seq2seq models to (a) introduce adversary-chosen spin into the output, while (b) maintaining standard accuracy metrics. To demonstrate feasibility of model spinning, we develop a new backdooring technique. It stacks the adversarial meta-task (e.g., sentiment analysis) onto a seq2seq model, backpropagates the desired meta-task output (e.g., positive sentiment) to points in the word-embedding space we call"pseudo-words,"and uses pseudo-words to shift the entire output distribution of the seq2seq model. Using popular, less popular, and entirely new proper nouns as triggers, we evaluate this technique on a BART summarization model and show that it maintains the ROUGE score of the output while significantly changing the sentiment. We explain why model spinning can be a dangerous technique in AI-powered disinformation and discuss how to mitigate these attacks.
Privileged Information for Modeling Affect In The Wild

A key challenge of affective computing research is discovering ways to reliably transfer affect models that are built in the laboratory to real world settings, namely in the wild. The existing gap between in vitro and in vivo affect applications is mainly caused by limitations related to affect sensing including intrusiveness, hardware malfunctions, availability of sensors, but also privacy and security. As a response to these limitations in this paper we are inspired by recent advances in machine learning and introduce the concept of privileged information for operating affect models in the wild. The presence of privileged information enables affect models to be trained across multiple modalities available in a lab setting and ignore modalities that are not available in the wild with no significant drop in their modeling performance. The proposed privileged information framework is tested in a game arousal corpus that contains physiological signals in the form of heart rate and electrodermal activity, game telemetry, and pixels of footage from two dissimilar games that are annotated with arousal traces. By training our arousal models using all modalities (in vitro) and using solely pixels for testing the models (in vivo), we reach levels of accuracy obtained from models that fuse all modalities both for training and testing. The findings of this paper make a decisive step towards realizing affect interaction in the wild.