Earning Extra Performance from Restrictive Feedbacks

Many machine learning applications encounter a situation where model providers are required to further refine the previously trained model so as to gratify the specific need of local users. This problem is reduced to the standard model tuning paradigm if the target data is permissibly fed to the model. However, it is rather difficult in a wide range of practical cases where target data is not shared with model providers but commonly some evaluations about the model are accessible. In this paper, we formally set up a challenge named \emph{Earning eXtra PerformancE from restriCTive feEDdbacks} (EXPECTED) to describe this form of model tuning problems. Concretely, EXPECTED admits a model provider to access the operational performance of the candidate model multiple times via feedback from a local user (or a group of users). The goal of the model provider is to eventually deliver a satisfactory model to the local user(s) by utilizing the feedbacks. Unlike existing model tuning methods where the target data is always ready for calculating model gradients, the model providers in EXPECTED only see some feedbacks which could be as simple as scalars, such as inference accuracy or usage rate. To enable tuning in this restrictive circumstance, we propose to characterize the geometry of the model performance with regard to model parameters through exploring the parameters' distribution. In particular, for the deep models whose parameters distribute across multiple layers, a more query-efficient algorithm is further tailor-designed that conducts layerwise tuning with more attention to those layers which pay off better. Our theoretical analyses justify the proposed algorithms from the aspects of both efficacy and efficiency. Extensive experiments on different applications demonstrate that our work forges a sound solution to the EXPECTED problem.Comment: Accepted by IEEE TPAMI in April 202

Familial Clustering For Weakly-labeled Android Malware Using Hybrid Representation Learning

IEEE Labeling malware or malware clustering is important for identifying new security threats, triaging and building reference datasets. The state-of-the-art Android malware clustering approaches rely heavily on the raw labels from commercial AntiVirus (AV) vendors, which causes misclustering for a substantial number of weakly-labeled malware due to the inconsistent, incomplete and overly generic labels reported by these closed-source AV engines, whose capabilities vary greatly and whose internal mechanisms are opaque (i.e., intermediate detection results are unavailable for clustering). The raw labels are thus often used as the only important source of information for clustering. To address the limitations of the existing approaches, this paper presents ANDRE, a new ANDroid Hybrid REpresentation Learning approach to clustering weakly-labeled Android malware by preserving heterogeneous information from multiple sources (including the results of static code analysis, the metainformation of an app, and the raw-labels of the AV vendors) to jointly learn a hybrid representation for accurate clustering. The learned representation is then fed into our outlieraware clustering to partition the weakly-labeled malware into known and unknown families. The malware whose malicious behaviours are close to those of the existing families on the network, are further classified using a three-layer Deep Neural Network (DNN). The unknown malware are clustered using a standard density-based clustering algorithm. We have evaluated our approach using 5,416 ground-truth malware from Drebin and 9,000 malware from VIRUSSHARE (uploaded between Mar. 2017 and Feb. 2018), consisting of 3324 weakly-labeled malware. The evaluation shows that ANDRE effectively clusters weaklylabeled malware which cannot be clustered by the state-of-theart approaches, while achieving comparable accuracy with those approaches for clustering ground-truth samples

A pilot controlled trial of a combination of dense cranial electroacupuncture stimulation and body acupuncture for post-stroke depression

BACKGROUND: Our previous studies have demonstrated the treatment benefits of dense cranial electroacupuncture stimulation (DCEAS), a novel brain stimulation therapy in patients with major depression, postpartum depression and obsessive-compulsive disorder. The purpose of the present study was to further evaluate the effectiveness of DCEAS combined with body acupuncture and selective serotonin reuptake inhibitors (SSRIs) in patients with post-stroke depression (PSD). METHODS: In a single-blind, randomized controlled trial, 43 patients with PSD were randomly assigned to 12 sessions of DCEAS plus SSRI plus body electroacupuncture (n = 23), or sham (non-invasive cranial electroacupuncture, n-CEA) plus SSRI plus body electroacupuncture (n = 20) for 3 sessions per week over 4 weeks. Treatment outcomes were measured using the 17-item Hamilton Depression Rating Scale (HAMD-17), the Clinical Global Impression - Severity scale (CGI-S) and Barthel Index (BI), a measure used to evaluate movement ability associated with daily self-caring activity. RESULTS: DCEAS produced a significantly greater reduction of both HAMD-17 and CGI-S as early as week 1 and CGI-S at endpoint compared to n-CEA, but subjects of n-CEA group exhibited a significantly greater improvement on BI at week 4 than DCEAS. Incidence of adverse events was not different in the two groups. CONCLUSIONS: These results indicate that DCEAS could be effective in reducing stroke patients’ depressive symptoms. Superficial electrical stimulation in n-CEA group may be beneficial in improving movement disability of stroke patients. A combination of DCEAS and body acupuncture can be considered a treatment option for neuropsychiatric sequelae of stroke. TRIAL REGISTRATION: http://www.clinicaltrials.gov, NCT01174394

Human Monoclonal Antibody Combination against SARS Coronavirus: Synergy and Coverage of Escape Mutants

BACKGROUND: Experimental animal data show that protection against severe acute respiratory syndrome coronavirus (SARS-CoV) infection with human monoclonal antibodies (mAbs) is feasible. For an effective immune prophylaxis in humans, broad coverage of different strains of SARS-CoV and control of potential neutralization escape variants will be required. Combinations of virus-neutralizing, noncompeting mAbs may have these properties. METHODS AND FINDINGS: Human mAb CR3014 has been shown to completely prevent lung pathology and abolish pharyngeal shedding of SARS-CoV in infected ferrets. We generated in vitro SARS-CoV variants escaping neutralization by CR3014, which all had a single P462L mutation in the glycoprotein spike (S) of the escape virus. In vitro experiments confirmed that binding of CR3014 to a recombinant S fragment (amino acid residues 318–510) harboring this mutation was abolished. We therefore screened an antibody-phage library derived from blood of a convalescent SARS patient for antibodies complementary to CR3014. A novel mAb, CR3022, was identified that neutralized CR3014 escape viruses, did not compete with CR3014 for binding to recombinant S1 fragments, and bound to S1 fragments derived from the civet cat SARS-CoV-like strain SZ3. No escape variants could be generated with CR3022. The mixture of both mAbs showed neutralization of SARS-CoV in a synergistic fashion by recognizing different epitopes on the receptor-binding domain. Dose reduction indices of 4.5 and 20.5 were observed for CR3014 and CR3022, respectively, at 100% neutralization. Because enhancement of SARS-CoV infection by subneutralizing antibody concentrations is of concern, we show here that anti-SARS-CoV antibodies do not convert the abortive infection of primary human macrophages by SARS-CoV into a productive one. CONCLUSIONS: The combination of two noncompeting human mAbs CR3014 and CR3022 potentially controls immune escape and extends the breadth of protection. At the same time, synergy between CR3014 and CR3022 may allow for a lower total antibody dose to be administered for passive immune prophylaxis of SARS-CoV infection

Molecular mechanisms of severe acute respiratory syndrome (SARS)

Severe acute respiratory syndrome (SARS) is a new infectious disease caused by a novel coronavirus that leads to deleterious pulmonary pathological features. Due to its high morbidity and mortality and widespread occurrence, SARS has evolved as an important respiratory disease which may be encountered everywhere in the world. The virus was identified as the causative agent of SARS due to the efforts of a WHO-led laboratory network. The potential mutability of the SARS-CoV genome may lead to new SARS outbreaks and several regions of the viral genomes open reading frames have been identified which may contribute to the severe virulence of the virus. With regard to the pathogenesis of SARS, several mechanisms involving both direct effects on target cells and indirect effects via the immune system may exist. Vaccination would offer the most attractive approach to prevent new epidemics of SARS, but the development of vaccines is difficult due to missing data on the role of immune system-virus interactions and the potential mutability of the virus. Even in a situation of no new infections, SARS remains a major health hazard, as new epidemics may arise. Therefore, further experimental and clinical research is required to control the disease

MicroRNA Dysregulation in the Spinal Cord following Traumatic Injury

Spinal cord injury (SCI) triggers a multitude of pathophysiological events that are tightly regulated by the expression levels of specific genes. Recent studies suggest that changes in gene expression following neural injury can result from the dysregulation of microRNAs, short non-coding RNA molecules that repress the translation of target mRNA. To understand the mechanisms underlying gene alterations following SCI, we analyzed the microRNA expression patterns at different time points following rat spinal cord injury

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism

Separable and Anonymous Identity-Based Key Issuing *

Abstract. In identity-based (ID-based) cryptosystems, a local registration authority (LRA) is responsible for authentication of users while the key generation center (KGC) is responsible for computing and sending the private keys to users and therefore, a secure channel is required. For privacy-oriented applications, it is important to keep in secret whether the private key corresponding to a certain identity has been requested. All of the existing ID-based key issuing schemes have not addressed this anonymity issue. Besides, the separation of duties for authentication and private key computation has not been discussed as well. In this paper, based on a signature scheme similar to a short blind signature, we propose a novel separable and anonymous ID-based key issuing scheme without secure channel. Our protocol supports the separation of duties between LRA and KGC. The private key computed by the KGC can be sent to the user in an encrypted form such that only the legitimate key requester authenticated by LRA can decrypt it, and any eavesdropper cannot know the identity corresponding to the secret key. Keywords. Identity-based cryptography, bilinear pairings, GDH groups, key issuing, anonymity, privacy, secure channel, separation of dutie